reading strategies for research papers

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Research Guides

Organizing Your Social Sciences Research Paper

Reading Research Effectively
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

Reading a Scholarly Article or Research Paper

Identifying a research problem to investigate usually requires a preliminary search for and critical review of the literature in order to gain an understanding about how scholars have examined a topic. Scholars rarely structure research studies in a way that can be followed like a story; they are complex and detail-intensive and often written in a descriptive and conclusive narrative form. However, in the social and behavioral sciences, journal articles and stand-alone research reports are generally organized in a consistent format that makes it easier to compare and contrast studies and to interpret their contents.

General Reading Strategies

W hen you first read an article or research paper, focus on asking specific questions about each section. This strategy can help with overall comprehension and with understanding how the content relates [or does not relate] to the problem you want to investigate. As you review more and more studies, the process of understanding and critically evaluating the research will become easier because the content of what you review will begin to coalescence around common themes and patterns of analysis. Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper.

1. Abstract

The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research paper. Use the abstract to filter out sources that may have appeared useful when you began searching for information but, in reality, are not relevant. Questions to consider when reading the abstract are:

Is this study related to my question or area of research?
What is this study about and why is it being done ?
What is the working hypothesis or underlying thesis?
What is the primary finding of the study?
Are there words or terminology that I can use to either narrow or broaden the parameters of my search for more information?

2. Introduction

If, after reading the abstract, you believe the paper may be useful, focus on examining the research problem and identifying the questions the author is trying to address. This information is usually located within the first few paragraphs of the introduction or in the concluding paragraph. Look for information about how and in what way this relates to what you are investigating. In addition to the research problem, the introduction should provide the main argument and theoretical framework of the study and, in the last paragraphs of the introduction, describe what the author(s) intend to accomplish. Questions to consider when reading the introduction include:

What is this study trying to prove or disprove?
What is the author(s) trying to test or demonstrate?
What do we already know about this topic and what gaps does this study try to fill or contribute a new understanding to the research problem?
Why should I care about what is being investigated?
Will this study tell me anything new related to the research problem I am investigating?

3. Literature Review

The literature review describes and critically evaluates what is already known about a topic. Read the literature review to obtain a big picture perspective about how the topic has been studied and to begin the process of seeing where your potential study fits within the domain of prior research. Questions to consider when reading the literature review include:

W hat other research has been conducted about this topic and what are the main themes that have emerged?
What does prior research reveal about what is already known about the topic and what remains to be discovered?
What have been the most important past findings about the research problem?
How has prior research led the author(s) to conduct this particular study?
Is there any prior research that is unique or groundbreaking?
Are there any studies I could use as a model for designing and organizing my own study?

4. Discussion/Conclusion

The discussion and conclusion are usually the last two sections of text in a scholarly article or research report. They reveal how the author(s) interpreted the findings of their research and presented recommendations or courses of action based on those findings. Often in the conclusion, the author(s) highlight recommendations for further research that can be used to develop your own study. Questions to consider when reading the discussion and conclusion sections include:

What is the overall meaning of the study and why is this important? [i.e., how have the author(s) addressed the " So What? " question].
What do you find to be the most important ways that the findings have been interpreted?
What are the weaknesses in their argument?
Do you believe conclusions about the significance of the study and its findings are valid?
What limitations of the study do the author(s) describe and how might this help formulate my own research?
Does the conclusion contain any recommendations for future research?

5. Methods/Methodology

The methods section describes the materials, techniques, and procedures for gathering information used to examine the research problem. If what you have read so far closely supports your understanding of the topic, then move on to examining how the author(s) gathered information during the research process. Questions to consider when reading the methods section include:

Did the study use qualitative [based on interviews, observations, content analysis], quantitative [based on statistical analysis], or a mixed-methods approach to examining the research problem?
What was the type of information or data used?
Could this method of analysis be repeated and can I adopt the same approach?
Is enough information available to repeat the study or should new data be found to expand or improve understanding of the research problem?

6. Results

After reading the above sections, you should have a clear understanding of the general findings of the study. Therefore, read the results section to identify how key findings were discussed in relation to the research problem. If any non-textual elements [e.g., graphs, charts, tables, etc.] are confusing, focus on the explanations about them in the text. Questions to consider when reading the results section include:

W hat did the author(s) find and how did they find it?
Does the author(s) highlight any findings as most significant?
Are the results presented in a factual and unbiased way?
Does the analysis of results in the discussion section agree with how the results are presented?
Is all the data present and did the author(s) adequately address gaps?
What conclusions do you formulate from this data and does it match with the author's conclusions?

7. References

The references list the sources used by the author(s) to document what prior research and information was used when conducting the study. After reviewing the article or research paper, use the references to identify additional sources of information on the topic and to examine critically how these sources supported the overall research agenda. Questions to consider when reading the references include:

Do the sources cited by the author(s) reflect a diversity of disciplinary viewpoints, i.e., are the sources all from a particular field of study or do the sources reflect multiple areas of study?
Are there any unique or interesting sources that could be incorporated into my study?
What other authors are respected in this field, i.e., who has multiple works cited or is cited most often by others?
What other research should I review to clarify any remaining issues or that I need more information about?

NOTE : A final strategy in reviewing research is to copy and paste the title of the source [journal article, book, research report] into Google Scholar . If it appears, look for a "cited by" followed by a hyperlinked number [e.g., Cited by 45]. This number indicates how many times the study has been subsequently cited in other, more recently published works. This strategy, known as citation tracking, can be an effective means of expanding your review of pertinent literature based on a study you have found useful and how scholars have cited it. The same strategies described above can be applied to reading articles you find in the list of cited by references.

Reading Tip

Specific Reading Strategies

Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.

As You are Reading

Focus on information that is most relevant to the research problem; skim over the other parts.
As noted above, read content out of order! This isn't a novel; you want to start with the spoiler to quickly assess the relevance of the study.
Think critically about what you read and seek to build your own arguments; not everything may be entirely valid, examined effectively, or thoroughly investigated.
Look up the definitions of unfamiliar words, concepts, or terminology. A good scholarly source is Credo Reference .

Taking notes as you read will save time when you go back to examine your sources. Here are some suggestions:

Mark or highlight important text as you read [e.g., you can use the highlight text feature in a PDF document]
Take notes in the margins [e.g., Adobe Reader offers pop-up sticky notes].
Highlight important quotations; consider using different colors to differentiate between quotes and other types of important text.
Summarize key points about the study at the end of the paper. To save time, these can be in the form of a concise bulleted list of statements [e.g., intro has provides historical background; lit review has important sources; good conclusions].

Write down thoughts that come to mind that may help clarify your understanding of the research problem. Here are some examples of questions to ask yourself:

Do I understand all of the terminology and key concepts?
Do I understand the parts of this study most relevant to my topic?
What specific problem does the research address and why is it important?
Are there any issues or perspectives the author(s) did not consider?
Do I have any reason to question the validity or reliability of this research?
How do the findings relate to my research interests and to other works which I have read?

Adapted from text originally created by Holly Burt, Behavioral Sciences Librarian, USC Libraries, April 2018.

Another Reading Tip

When is it Important to Read the Entire Article or Research Paper

Laubepin argues, "Very few articles in a field are so important that every word needs to be read carefully." However, this implies that some studies are worth reading carefully. As painful and time-consuming as it may seem, there are valid reasons for reading a study in its entirety from beginning to end. Here are some examples:

Studies Published Very Recently . The author(s) of a recent, well written study will provide a survey of the most important or impactful prior research in the literature review section. This can establish an understanding of how scholars in the past addressed the research problem. In addition, the most recently published sources will highlight what is currently known and what gaps in understanding currently exist about a topic, usually in the form of the need for further research in the conclusion .
Surveys of the Research Problem . Some papers provide a comprehensive analytical overview of the research problem. Reading this type of study can help you understand underlying issues and discover why scholars have chosen to investigate the topic. This is particularly important if the study was published very recently because the author(s) should cite all or most of the key prior research on the topic. Note that, if it is a long-standing problem, there may be studies that specifically review the literature to identify gaps that remain. These studies often include the word review in their title [e.g., Hügel, Stephan, and Anna R. Davies. "Public Participation, Engagement, and Climate Change Adaptation: A Review of the Research Literature." Wiley Interdisciplinary Reviews: Climate Change 11 (July-August 2020): https://doi.org/10.1002/ wcc.645].
Highly Cited . If you keep coming across the same citation to a study while you are reviewing the literature, this implies it was foundational in establishing an understanding of the research problem or the study had a significant impact within the literature [positive or negative]. Carefully reading a highly cited source can help you understand how the topic emerged and motivated scholars to further investigate the problem. It also could be a study you need to cite as foundational in your own paper to demonstrate to the reader that you understand the roots of the problem.
Historical Overview . Knowing the historical background of a research problem may not be the focus of your analysis. Nevertheless, carefully reading a study that provides a thorough description and analysis of the history behind an event, issue, or phenomenon can add important context to understanding the topic and what aspect of the problem you may want to examine further.
Innovative Methodological Design . Some studies are significant and worth reading in their entirety because the author(s) designed a unique or innovative approach to researching the problem. This may justify reading the entire study because it can motivate you to think creatively about pursuing an alternative or non-traditional approach to examining your topic of interest. These types of studies are generally easy to identify because they are often cited in others works because of their unique approach to studying the research problem.
Cross-disciplinary Approach . R eviewing studies produced outside of your discipline is an essential component of investigating research problems in the social and behavioral sciences. Consider reading a study that was conducted by author(s) based in a different discipline [e.g., an anthropologist studying political cultures; a study of hiring practices in companies published in a sociology journal]. This approach can generate a new understanding or a unique perspective about the topic . If you are not sure how to search for studies published in a discipline outside of your major or of the course you are taking, contact a librarian for assistance.

Laubepin, Frederique. How to Read (and Understand) a Social Science Journal Article . Inter-University Consortium for Political and Social Research (ISPSR), 2013; Shon, Phillip Chong Ho. How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students . 2nd edition. Thousand Oaks, CA: Sage, 2015; Lockhart, Tara, and Mary Soliday. "The Critical Place of Reading in Writing Transfer (and Beyond): A Report of Student Experiences." Pedagogy 16 (2016): 23-37; Maguire, Moira, Ann Everitt Reynolds, and Brid Delahunt. "Reading to Be: The Role of Academic Reading in Emergent Academic and Professional Student Identities." Journal of University Teaching and Learning Practice 17 (2020): 5-12.

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CAREER COLUMN
07 July 2022

How to find, read and organize papers

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Maya Gosztyla is a PhD student in biomedical sciences at the University of California, San Diego.

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The science of reading should be informed by an evolving evidence base built upon the scientific method. Decades of basic research and randomized controlled trials of interventions and instructional routines have formed a substantial evidence base to guide best practices in reading instruction, reading intervention, and the early identification of at-risk readers. The recent resurfacing of questions about what constitutes the science of reading is leading to misinformation in the public space that may be viewed by educational stakeholders as merely differences of opinion among scientists. Our goals in this paper are to revisit the science of reading through an epistemological lens to clarify what constitutes evidence in the science of reading and to offer a critical evaluation of the evidence provided by the science of reading. To this end, we summarize those things that we believe have compelling evidence, promising evidence, or a lack of compelling evidence. We conclude with a discussion of areas of focus that we believe will advance the science of reading to meet the needs of all children in the 21st century.

For more than 100 years, the question of how best to teach children to read has been debated in what has been termed the “reading wars”. The debate cyclically fades into the background only to reemerge, often with the same points of conflict. We believe that this cycle is not helpful for promoting the best outcomes for children’s educational success. Our goal in this paper is to make an honest and critical appraisal of the science of reading, defining what it is, how we build a case for evidence, summarizing those things for which the science of reading has provided unequivocal answers, providing a discussion of things we do not know but that may have been “oversold,” identifying areas for which evidence is promising but not yet compelling, and thinking ahead about how the science of reading can better serve all stakeholders in children’s educational achievements.

At its core, scientific inquiry is the same in all fields. Scientific research, whether in education, physics, anthropology, molecular biology, or economics, is a continual process of rigorous reasoning supported by a dynamic interplay among methods, theories, and findings. It builds understandings in the form of models or theories that can be tested. Advances in scientific knowledge are achieved by the self-regulating norms of the scientific community over time, not, as sometimes believed, by the mechanistic application of a particular scientific method to a static set of questions (National Research Council, 2002, p. 2).

What is the Science of Reading and Why are we Still Debating it?

The “science of reading” is a phrase representing the accumulated knowledge about reading, reading development, and best practices for reading instruction obtained by the use of the scientific method. We recognize that the accrual of scientific knowledge related to reading is ever evolving, at times circuitous, and not without controversy. Nonetheless, the knowledge base on the science of reading is vast. In the last decade alone, over 14,000 peer-reviewed articles have been published in journals that included the keyword “reading” based on a PsycINFO search. Although many of these studies likely focused on a sliver of the reading process individually, collectively, research studies with a focus on reading have yielded a substantial knowledge base of stable findings based on the science of reading. Taken together, the science of reading helps a diverse set of educational shareholders across institutions (e.g., preschools, schools, universities), communities, and families to make informed choices about how to effectively promote literacy skills that foster healthy and productive lives ( DeWalt & Hink, 2009 ; Rayner et al., 2001 ).

An interesting question concerning the science of reading is “Why is there a debate surrounding the science of reading?” Although there are certainly disputes within the scientific community regarding best practices and new areas of research inquiry, most of the current debate seems to settle upon what constitutes scientific evidence, how much value we should place on scientific evidence as opposed to other forms of knowledge, and how preservice teachers should be instructed to teach reading ( Brady, 2020 ). The current disagreement in what constitutes the scientific evidence of reading (e.g., Calkins, 2020 ) is not new. During the last round of the “reading wars” in the late 1990’s and early 2000’s these same issues were discussed and debated. Much of the debate focused on conflicting views in epistemology between constructivists and positivists on the basic mechanisms associated with reading development. Constructivists, such as Goodman (1967) and Smith (1971) , believed that reading was a “natural act” akin to learning language and thus emphasized giving children the opportunity to discover meaning through experiences in a literacy-rich environment. In contrast, positivists, such as Chall (1967) and Flesch (1955) , made strong distinctions between innate language learning and the effortful learning required to acquire reading skills. Positivists argued for explicit instruction to help foster understanding of how the written code mapped onto language, whereas constructivists encouraged children to engage in a “psycholinguistic guessing game” in which readers use their graphic, semantic, and syntactic knowledge (known as the three cuing system) to guess the meaning of a printed word.

Research clearly indicates that skilled reading involves the consolidation of orthographic and phonological word forms ( Dehene, 2011 ). Work in cognitive neuroscience indicates that a small region of the left ventral visual cortex becomes specialized for this purpose. As children learn to read, they recruit neurons from a small region of the left ventral visual cortex within the left occipitotemporal cortex region (i.e., visual word form area) that are tuned to language-dependent parameters through connectivity to perisylvian language areas ( Dehaene-Lambertz et al., 2018 ). This provides an efficient circuit for grapheme-phoneme conversion and lexical access allowing efficient word-reading skills to develop. These studies provide direct evidence for how teaching alters the human brain by repurposing some visual regions toward the shapes of letters, suggesting that cultural inventions, such as written language, modify evolutionarily older brain regions. Furthermore, studies suggest that instruction focusing on the link between orthography and phonology promote this brain reorganization (e.g., Dehaene, 2011 ). Yet, arguments between philosophical constructivists and philosophical positivists on what constitutes the science of reading and how it informs instruction remain active today (e.g., Castles et al., 2018 ). In a recent interview with Emily Hanford, Ken Goodman defended his advocacy for the three cuing system saying that the three-cueing theory is based on years of observational research. In his view, three cueing is perfectly valid, drawn from a different kind of evidence than what scientists collect in their lab and later he stated that “my science is different” ( Hanford, 2019 ).

As scientists at the Florida Center for Reading Research, we are often frustrated when what we view to be the empirically supported evidence base about the reading process are distorted or denied in communications directed to the public and to teachers. However, Stanovich (2003) posited that “in many cases, the facts are secondary—what is being denied are the styles of reasoning that gave rise to the facts; what is being denied is closer to a worldview than an empirical finding. Many of these styles are implicit; we are not conscious of them as explicit rules of behavior” (pp. 106-107). Stanovich proposed five different dimensions that represent “styles” of generating knowledge about reading. For our purposes, here, we focus on the first dimension: the correspondence versus coherence theory of truth. It hits at the heart of how people believe something to be true. People who believe that a real world exists independent of their beliefs, and that interrogating this world using rigorous principles to gain knowledge is a fruitful activity are said to subscribe to the correspondence theory of truth. In contrast, those who subscribe to the coherence theory of truth believe that something is “true” if the beliefs about something fit together in a logical way. In essence, something is true if it makes sense.

Stanovich believed these differing truth systems might lie at the heart of the disagreements surrounding the science of reading. One side shouting, “Look at this mountain of evidence! How can you not believe it?” and the other side shouting, “It doesn’t make sense! It doesn’t match up with our experiences! Why should we value your knowledge above our own?!” By approaching the science of reading from the perspective of the correspondence theory of truth, we consider how compelling evidence can be generated, what we believe is the compelling evidence, what we think lacks evidence, and what we think is promising evidence.

How We Build a Case for Compelling Evidence

Research is the means by which we acquire and understand knowledge about the world ( Dane, 1990 ) to create scientific principles. Relatively few scientists would argue with the importance of using research evidence to support a principle or to make claims about reading development and the quality of reading instruction. Where significant divergence often occurs is in response to policy statements that categorize research claims and instructional strategies into those with greater or lesser levels of evidence. This divergence is typically rooted in applied epistemology, which can be understood as the study of whether the means by which we study evidence are themselves well designed to lead to valid conclusions. Researchers often frame the science of reading from divergent applied epistemological perspectives. Thus, two scientists who approach the science of reading with different epistemologies will both suggest that they have principled understandings and explanations for how children learn to read; yet, the means by which those understandings and explanations were derived are often distinct.

The correspondence and coherence theories of truth described above are examples of explanations from contrasting epistemological perspectives. Consistent with these perspectives, researchers approaching the science of reading using a correspondence theory typically prioritize deductive methods, which embed hypothesis testing, precise operationalization of constructs, and efforts to decouple the researchers’ beliefs from their interpretation and generalization of empirical evidence. Researchers approaching the science of reading using a coherence theory of truth typically prioritize more inductive methods, such as phenomenological, ethnographic, and grounded theory approaches that embed focus on the meaning and understanding that comes through a person’s lived experience and where the scientist’s own observations shape meaning and principles (e.g., Israel & Duffy, 2014 ).

When the National Research Council published Scientific Research in Education (2002), a significant amount of criticism levied against the report boiled down to differences in epistemological perspectives. Yet, these genuine contrasts can often obscure contributions to the science of reading that derive from multiple applied epistemologies. Observational research, using both inductive (e.g., case studies) and deductive (e.g., correlational studies) approaches, substantively informs the development of theories and of novel instructional approaches (e.g., Scruggs et al., 2007 ). Public health research offers a useful parallel. As it would be unethical to establish a causal link from smoking cigarettes to lung cancer through a randomized controlled trial, that field instead used well-designed observational studies to derive claims and principles. These findings then informed later stages in the broader program of research, including randomized controlled trials of interventions for smoking cessation.

In the science of reading, principles and instructional strategies should indeed capitalize on a program of research inclusive of multiple methodologies. Yet, as the public health domain ultimately takes direction from the efficacy of smoking cessation programs, so too must the science of reading take direction from theoretically informed and well-designed experimental and quasi-experimental studies of promising strategies when the intention is to evaluate instructional practices. The use of experimental (i.e., randomized trials) and quasi-experimental (e.g., regression discontinuity, propensity score matching, interrupted time series) designs, in which an intervention is competed against counterfactual conditions, such as typical practice or alternative interventions, provides the strongest causal credibility regarding which instructional strategies are effective. The What Works Clearinghouse (WWC) of the Institute of Education Sciences (e.g., What Works Clearinghouse, 2020) and the Every Student Succeeds Act (ESSA; Every Student Succeeds Act, 2015 ) are efforts by the US Department of Education to hierarchically characterize the levels of evidence currently available for instructional practices in education. The WWC uses a review framework, developed by methodological and statistical experts, for evaluating the quality and scope of evidence for specific instructional practices based on features of the design, implementation, and analysis of studies. Similarly, ESSA uses four tiers that focus on both the design of the study and the results of the study in which the tiers differ based on the quantity of evidence and quality of evidence supporting an approach. For both WWC and ESSA, quantity of evidence refers to the number of well-designed and well-implemented studies, and quality of evidence is defined by the ability of a study’s methods to allow for alternative explanations of a finding to be ruled out, for which the randomized controlled trial provides the strongest method.

As outlined above, the “science of reading” utilizes multiple research approaches to generate ideas about reading. Ultimately, the highest priority in the science of reading should be the replicable and generalizable knowledge from observational and experimental methods, rooted in a deductive research approach to knowledge generation that is framed in a correspondence theory of truth. In this manner, the accumulated evidence is built on a research foundation by which theories, principles, and hypotheses have been subjected to rigorous empirical scrutiny to determine the degree to which they hold up across variations in samples, measures, and contexts. In the following sections, we summarize issues related to the nature, development, and instruction of reading for which we believe the science of reading either has or has not yielded compelling evidence, identify what we believe are promising areas for which sufficient evidence has not yet accumulated, and suggest a number of areas that we believe will help move the science of reading forward, increasing knowledge and enhancing its positive impacts for a variety of stakeholders.

Compelling Evidence in the Science of Reading

In this section, we focus on a number of findings centrally important for understanding the development and teaching of reading in alphabetic languages. The evidence base provides answers varying across orthographic regularity (e.g., English vs. Spanish), reading subskill (i.e., decoding vs. comprehension), grade range or developmental level (e.g., early childhood, elementary, adolescence), and linguistic diversity (e.g., English language learners, dialect speakers).

There are large differences among alphabetic languages in the rules for how graphemes represent sounds in words (i.e., a language’s orthography). In languages like Spanish and Finnish there is a near one-to-one relation between letters and sounds. The letter-sound coding in these languages is transparent, and they have shallow orthographies. In other languages, most notably English, there is often not a one-to-one relation between letters and sounds. The letter-sound coding in these languages is opaque, and they have deep orthographies. Children must learn which words cannot be decoded based solely on letter-sound correspondence (e.g., two, knight, laugh) and learn to match these irregular spellings to the words they represent. Where a language’s orthography falls on the shallow-deep dimension affects how quickly children develop accurate and fluent word-reading skills ( Ellis et al., 2004 ; Ziegler & Goswami, 2005 ) and how much instruction on foundational reading skills is likely needed. Studies indicate that children learning to read in English are slower to acquire decoding skills (e.g., Caravolas et al., 2013 ). Ziegler et al. (1997) reported that 69% of monosyllabic words in English were consistent in spelling-to-phonology mappings and 31% of the phonology-to-spelling mappings were consistent. Thus, in teaching children to read in English, the “grain size” of phoneme, onset-rime, and whole word matters ( Ziegler & Goswami, 2005 ) and the preservation of morphological regularities in English spelling matters (e.g., vine vs. vineyard ).

Gough and Tunmer’s (1986) “simple view of reading” model, which is supported by a significant amount of research, provides a useful framework for conceptualizing the development of reading skills across time. It also frames the elements for which it is necessary to provide instructional support. The ultimate goal of reading is to extract and construct meaning from text for a purpose. For this task to be successful, however, the reader needs skills in both word decoding and linguistic comprehension. Weaknesses in either area will reduce the capacity to achieve the goal of reading. Decoding skills and linguistic comprehension make independent contributions to the prediction of reading comprehension across diverse populations of readers ( Kershaw & Schatschneider, 2012 ; Sabatini et al., 2010 ; Vellutino, et al., 2007 ). Results of several studies employing measurement strategies that allow modeling of each component as a latent variable indicate that decoding and linguistic comprehension account for almost all of the variance in reading comprehension (e.g., Foorman et al., 2015 ; Lonigan et al., 2018 ). The relative influence of these skill domains, however, changes across development. The importance of decoding skill in explaining variance in reading comprehension decreases across grades whereas the importance of linguistic comprehension increases (e.g., Catts et al., 2005 ; Foorman et al., 2018 ; García & Cain, 2014 ; Lonigan et al., 2018 ). By the time children are in high school linguistic comprehension and reading comprehension essentially form a single dimension (e.g., Foorman et al., 2018 ).

Children’s knowledge of the alphabetic principle (i.e., how letters and sounds connect) and knowledge of the morphophonemic nature of English are necessary to create the high-quality lexical representations essential to accurate and efficient decoding ( Ehri, 2005 ; Perfetti, 2007 ). Acquiring the alphabetic principle is dependent on understanding that words are composed of smaller sounds (i.e., phonological awareness, PA) and alphabet knowledge (AK). Both PA and AK are substantial correlates and predictors of decoding skills (e.g., Wagner & Torgesen, 1987 ; Wagner et al., 1994 ). Prior to formal reading instruction, children are developing PA and AK as well as other early literacy skills that are related to later decoding skills following formal reading instruction ( Lonigan et al., 2009 ; Lonigan et al., 1998 ; National Early Literacy Panel [NELP], 2008 ; Whitehurst & Lonigan, 1998 ). Reading comprehension takes advantage of the reader’s ability to understand language. In most languages, written language and spoken language have high levels of overlap in their basic structure. Longitudinal studies indicate that linguistic comprehension skills from early childhood predict reading comprehension at the end of elementary school ( Catts et al., 2015 ; Language and Reading Research Consortium & Chiu, 2018 ; Mancilla-Martinez & Lesaux, 2010 ; Storch & Whitehurst, 2002 ; Verhoeven & Van Leeuwe, 2008 ). The developmental precursors to skilled reading are present prior to school entry. Consequently, differences between children in the development of these skills forecast later differences in reading skills and are useful for identifying children at risk for reading difficulties.

The science of reading provides numerous clear answers about the type and focus of reading instruction for the subskills of reading, depending on where children are on the continuum of reading development and children’s linguistic backgrounds. Much of this knowledge is summarized in the practice guides produced by the Institute of Education Sciences ( Baker et al., 2014 ; Foorman et al., 2016a ; Gersten et al., 2007 , 2008 ; Kamil et al., 2008 ; Shanahan et al., 2010 ) and in meta-analytic summaries of research (e.g., Berkeley et al., 2012 ; Ehri, Nunes, Stahl et al., 2001 ; Ehri, Nunes, Willows et al., 2001 ; NELP, 2008 ; Therrien, 2004 ; Wanzek et al., 2013 , 2016 ). Whereas the practice guides list several best practices, here we emphasize those practices classified as supported by strong or moderate evidence based on WWC standards.

Since the publication of the Report of the National Reading Panel ( National Institute of Child Health and Human Development, 2000 ) and supported by subsequent research (e.g., Gersten et al., 2017a ; Foorman et al., 2016a ), it is clear that a large evidence base provides strong support for the explicit and systematic instruction of the component and foundational skills of decoding and decoding itself. That is, teaching children phonological awareness and letter knowledge, particularly when combined, results in improved word-decoding skills. Teaching children to decode words using systematic and explicit phonics instruction results in improved word-decoding skills. Such instruction is effective both for monolingual English-speaking children and children whose home language is other than English (i.e., dual-language learners; Baker et al., 2014 ; Gersten et al., 2007 ) as well as children who are having difficulties learning to read or who have an identified reading disability ( Ehri, Nunes, Stahl et al., 2001 ; Gersten et al., 2008 ). Additionally, providing children with frequent opportunities to read connected text supports the development of word-reading accuracy and fluency as well as comprehension skills ( Foorman et al., 2016a ; Therrien, 2004 ).

Similarly, a number of instructional activities to promote the development of reading comprehension have strong or moderate supporting evidence. For younger children, teaching children how to use comprehension strategies and how to utilize the organizational structure of a text to understand, learn, and retain content supports better reading comprehension ( Shanahan et al., 2010 ). For older children, teaching the use of comprehension strategies also enhances reading comprehension ( Kamil et al., 2008 ) as does explicit instruction in key vocabulary, providing opportunities for extended discussion of texts, and providing instruction on foundational reading skills when children lack these skills; such instructional approaches are also effective for children with significant reading difficulties ( Berkeley et al., 2012 ; Kamil et al., 2008 ).

Lack of Compelling Evidence in the Science of Reading

In the above section, practices were highlighted that have sufficient evidence to warrant their widespread use. In this section, we address reading practices for which there is a lack of compelling evidence. Some practices have simply not yet been scientifically evaluated. Other practices have been evaluated, but either the evidence does not support their use based on the generalizability of the results or the studies in which they were evaluated were not of sufficient quality to meet a minimal standard of evidence (e.g., WWC standards). Although we lack sufficient space to present a comprehensive list of practices that do not have compelling evidence, we provide examples of practices that are commonplace and vary in the degree to which they have been scientifically studied.

Evidence-based decision making regarding effective literacy programs and practices for classroom use can be difficult. Often, there is no evidence of effectiveness for a program or the evidence is of poor quality. For instance, of the five most popular reading programs used nationwide (i.e., Units of Study for Teaching Reading, Journeys, Into Reading, Leveled Literacy Intervention and Reading Recovery; Schwartz, 1999) only Leveled Literacy Intervention and Reading Recovery, both interventions for struggling readers, have studies that meet WWC standards. The evidence indicates that there were mixed effects across outcomes for Leveled Literacy Intervention and positive or potentially positive effects for Reading Recovery (e.g., Chapman & Tunmer, 2016 ). Classroom reading programs are typically built around the notion of evidence-informed practices – teaching approaches that are grounded in quality research – but have not been subjected to direct scientific evaluation. As a consequence, it is currently impossible for schools to select basal reading programs that adhere to strict evidence-based standards (e.g., ESSA, 2015 ). As an alternative, schools must develop selection criteria for choosing classroom reading programs informed by the growing scientific evidence on instructional factors that support early reading development (e.g., Castles et al., 2018 ; Foorman et al.2017 ; Rayner et al., 2001 ).

Common instructional approaches that lack generalizable empirical support include such practices as close reading ( Welsch et al., 2019 ), use of decodable text ( Jenkins et al., 2004 ), sustained silent reading ( NICHD, 2000 ), multisensory approaches ( Birsh, 2011 ), and the three-cueing system to support word recognition development (Seidenberg, 2017). Some of these instructional approaches rest on sound theoretical and pedagogical grounds. For example, giving beginning readers the opportunity to read decodable texts provides practice applying the grapheme-phoneme relations they have learned to successfully decode words ( Foorman et al., 2016a ), thus building lexical memory to support word reading accuracy and automaticity (Ehri, this issue). However, the only study to experimentally examine the impact of reading more versus less decodable texts as part of an early intervention phonics program for at risk first graders found no differences between the two groups on any of the posttest measures ( Jenkins et al., 2004 ). Such a result does not rule out the possibility of the usefulness of decodable texts but rather indicates the need to disentangle the active ingredients of effective interventions to specify what to use, when, how often, and for whom.

Similarly, multisensory approaches (e.g., Orton-Gillingham) that teach reading by using multiple senses (i.e., sight, hearing, touch, and movement) to help children make systematic connections between language, letters, and words ( Birsh, 2011 ) are commonplace and have considerable clinical support for facilitating reading development in children who struggle to learn to read. However, there is little scientific evidence that indicates that a multisensory approach is more effective than similarly structured phonological-based approaches that do not include a strong multisensory component (e.g., Boyer & Ehri, 2011 ; Ritchey & Goeke, 2006 ; Torgesen et al., 2001 ). With further research, we may find that a multisensory component is a critical ingredient of intervention for struggling readers, but we lack this empirical evidence currently.

Instruction in reading comprehension is another area where despite some studies showing moderate or strong support (see section on compelling evidence) other practices are employed despite limited support for them (e.g., Boulay et al., 2015 ). The complexity of reading comprehension relies on numerous cognitive resources and background knowledge; as a result, intervention directed exclusively at one component or another is not likely to be that impactful. For example, research shows a clear relation between breadth and depth of vocabulary and reading comprehension ( Wagner et al., 2007 ). One implication of this relation is that teaching vocabulary could improve reading comprehension. Numerous studies have tested this implication using instructional approaches that vary from teaching words in isolation to practices that involve instruction in the use of context to learn the meaning of unfamiliar words. Instruction has also included strategies to determine meaning of words through word study and morphological analysis (e.g., Beck & McKeown, 2007 ; Lesaux et al., 2014 ). Although these practices have been effective in increasing vocabulary knowledge of the words taught, there is limited evidence of transfer to untaught words (as measured by standardized measures) or to improvement in general reading comprehension ( Elleman et al., 2009 ; Lesaux et al., 2010 ). Such findings do not mean that vocabulary instruction is not a useful practice; rather, by itself, it is not sufficient to improve reading comprehension. To make meaningful gains, intervention for reading comprehension likely requires addressing multiple components of language as well as teaching content knowledge (see next section) to make sizable gains.

Other instructional practices go directly against what is known from the science of reading. For example, the three-cueing approach to support early word recognition (i.e., relying on a combination of semantic, syntactic, and graphophonic cues simultaneously to formulate an intelligent hypothesis about a word’s identity) ignores 40 years of overwhelming evidence that orthographic mapping involves the formation of letter-sound connections to bond spelling, pronunciation, and meaning of specific words in memory (see Ehri, this issue). Moreover, relying on alternative cuing systems impedes the building of automatic word-recognition skill that is the hallmark of skilled word reading ( Stanovich, 1990 ; 1991 ). The English orthography, being both alphabetic-phonemic and morpho-phonemic, clearly privileges the use of various levels of grapheme-phoneme correspondences to read words ( Frost, 2012 ), with rapid context-free word recognition being the process that most clearly distinguishes good from poor readers ( Perfetti, 1992 ; Stanovich, 1980 ). Guessing at a word amounts to a lost learning trial to help children learn the orthography of the word and thus reduce the need to guess the word in the future ( Castles et al., 2018 ; Share, 1995 ).

Similarly, alternative approaches to improving reading skills for struggling readers often fall well outside the scientific consensus regarding sources of reading difficulties. Some of these approaches are based on the tenet that temporal processing deficits in the auditory (e.g., Tallal, 1984 ) and visual (e.g., Stein, 2019 ) systems of the brain are causally related to poor word-reading development. Although there is some evidence that typically developing and struggling readers differ on measures tapping auditory ( Casini et al., 2018 ; Protopapas, 2014 ) and visual (e.g., Eden et al., 1995; Olson & Datta, 2002 ) processing skill, there is little evidence to support the use of instructional programs designed to improve auditory or visual systems to ameliorate reading problems ( Strong et al., 2011 ). Further, interventions designed to decrease visual confusion (e.g., Dyslexie font) or modify transient channel processing (e.g., Irlen lenses) to improve reading skill for children with reading disability have also failed to garner scientific support ( Hyatt et al., 2009 ; Iovino et al., 1998 ; Marinus et al., 2016 ). Similarly, although use of video games to improve reading via enhanced visual attention is reported to be an effective intervention for children with reading disability ( Peters et al., 2019 ), studies of this supplemental intervention approach have not compared it to standard supplemental approaches. Finally, studies of interventions designed to enhance other cognitive processes, such as working memory, also lack evidence effectiveness in terms of improved reading-related outcomes (e.g., Melby-Lervåg et al., 2016 ).

Promising but Not (Yet) Compelling Evidence in the Science of Reading

There are many promising areas of research that are poised to provide compelling evidence to inform the science of reading in the coming years. As we do not have space to provide a comprehensive list, we highlight only a few promising areas in prevention research and elementary education research.

Promising Directions in Prevention Research

Research on the prevention of reading problems is critical for our ability to reduce the number of children who struggle learning to read. One area of prevention research that has great promise but needs more evidence is how to more fully develop preschoolers’ language abilities that support later reading success. Both correlational and experimental findings indicate that providing children with opportunities to engage in high-quality conversations, coupled with exposure to advanced language models, matters for language development ( Cabell et al., 2015 ; Dickinson & Porche, 2011 ; Lonigan et al., 2011 ; Wasik & Hindman, 2018). Yet, most programs have a more robust impact on children’s proximal language learning (i.e., learning taught words) than on generalized language learning as measured with standardized assessments ( Marulis & Neuman, 2010 ).

Promising studies that have demonstrated significant effects on children’s general language development elucidate potential points of leverage. First, improving the connection between the school and home contexts by including parents as partners can promote synergistic learning for children as language-learning activities in school and home settings are increasingly aligned (e.g., Lonigan & Whitehurst, 1998 ). A second leverage point is increasing attention to children’s active use of language in the classroom to promote a rich dialogue between children and adults (e.g., Lonigan et al., 2011 ; Wasik & Hindman, 2018). A third leverage point is integrating content area instruction into early literacy instruction to improve language learning, for example, building children’s conceptual knowledge of the social and natural world and teaching vocabulary words within the context of related ideas (e.g., Gonzalez et al., 2011 ).

Promising Directions in Elementary Education Research

We present two promising areas in reading research with elementary-age students, one focused on improving linguistic comprehension and one focused on improving decoding, consistent with the simple view of reading.

The knowledge a reader brings to a text is the chief determinant of whether the reader will understand that text ( Anderson & Pearson, 1984 ). Thus, building knowledge is an essential, yet neglected, part of improving linguistic comprehension (Cabell & Hwang, this issue). Teaching reading is most often approached in early elementary classrooms as a subject that is independent from other subjects, such as science and social studies ( Palinscar & Duke, 2004 ). As such, reading is taught using curricula that do not systematically build children’s knowledge of the social and natural world. Instruction in reading and the content areas does not have to be an either/or proposition. Rather, the teaching of reading and of content-area learning can be simultaneously taught and integrated to powerfully impact children’s learning of both reading and content knowledge (e.g., Connor et al., 2017 ; Kim et al., 2020 ; Williams et al., 2014 ). This area of research is promising but not yet compelling, due to the small number of experimental and quasi-experimental studies that have examined either integrated content-area and literacy instruction or content-rich English Language Arts instruction in K-5 settings (approximately 31 studies). Through meta-analysis, this corpus of studies demonstrates that combining knowledge building and literacy approaches has a positive impact on both vocabulary and comprehension outcomes for elementary-age children ( Hwang et al., 2019 ). Further rigorous studies are needed that test widely used content-rich English Language Arts curricula (Cabell & Hwang, 2020, this issue); also required is new development of integrative and interdisciplinary approaches in this area.

There is also promising research on helping students to decode words more efficiently. It is widely accepted that students with reading difficulties often have underlying deficits in phonological processing (e.g., Brady & Schankweiler, 1991 ; Stanovich & Siegel, 1994 ; Torgesen, 2000 ; Vellutino et al., 1996 ) and these deficits are believed to disrupt the acquisition of spelling-to-sound translation routines that form the basis of early decoding-skill development (e.g., van IJzendoorn & Bus, 1994 ; Rack et al., 1992 ). For developing readers, decoding an unfamiliar letter string can result in either full or partial decoding. During partial decoding, the reader must match the assembled phonology from decoding with their lexical representation of a word ( Venezky, 1999 ). For example, encountering the word island might render the incorrect but partial decoding attempt, “izland”. A child’s flexibility with the partially decoded word is referred to as their “set for variability” or their ability to go from the decoded form to the correct pronunciation of a word. This skill serves as a bridge between decoding and lexical pronunciations and may be an important second step in the decoding process ( Elbro et al., 2012 ).

The matching of partial phonemic-decoding output is facilitated by the child’s decoding skills, the quality of the child’s lexical word representation, and by the potential contextual support of text ( Nation & Castles, 2017 ). Correlational studies indicate that students’ ability to go from a decoded form of a word to a correct pronunciation (their set for variability) predicts the reading of irregular words ( Tunmer & Chapman, 2012 ), regular words ( Elbro, et al., 2012 ), and nonwords ( Steacy et al., 2019a ). Set for variability has also been found to be a stronger predictor of word reading than phonological awareness in students in grades 2-5 (e.g., Steacy et al., 2019b ). Recent studies in this area suggest that children can benefit from being encouraged to engage with the irregularities of English ( Dyson et al., 2017 ) to promote the implicit knowledge structures needed to read and spell these complex words. Additional research suggests that set for variability training can be effective in promoting early word reading skills (e.g., Savage et al., 2018 ; Zipke, 2016 ). The work done in this area to date suggests that set for variability requires child knowledge structures and strategies, which can be developed through instruction, that allow successful matching of partial phonemic-decoding output with the corresponding phonological, morphological, and semantic lexical representations.

Where Do We Go Next in the Science of Reading?

Basic science research.

The science of reading has reached some consensus on the typical development of reading skill and how individual differences may alter this trajectory (e.g., Boscardin et al., 2008 ; Hjetland et al., 2019; Peng et al., 2019 ). Less is known about factors and mechanisms related to reading among diverse learners, a critical barrier to the field’s ability to address and prevent reading difficulty when it arises. Investigations with large and diverse participant samples are needed to improve understanding of how child characteristics additively and synergistically affect reading acquisition ( Hernandez, 2011 ; Lonigan et al., 2013 ). Insufficient research disentangles the influence of English-learner status for children who also have identified disabilities (Solari et al., 2014; Wagner et al., 2005 ). Greater attention to how language variation (e.g., dialect use) and differences in language experience affect reading development is crucial ( Patton Terry et al., 2010 ; Seidenberg & MacDonald, 2018; Washington et al., 2018). New realizations of the interaction between child characteristics and the depth of the orthography have also highlighted the importance of implicit learning in early reading ( Seidenberg, 2005 ; Steacy et al., 2019). Innovative cross-linguistic research is exploring how diverse methods of representing pronunciation and meaning within different orthographies, and children’s developing awareness of these methods, jointly predict reading skills (e.g., Kuo & Anderson, 2006 ; Wade-Woolley, 2016 ). Furthermore, a better understanding of the role of executive function, socio-emotional resilience factors, and biopsychosocial risk variables (e.g., poverty and trauma) on reading development is critical. Additional research like this, in English and across languages, is needed to develop effective instruction and assessments for all leaners.

A clearer understanding of child and contextual influences on the development of reading also will support improvements in how early and accurately children at risk for reading difficulties and disabilities are identified. Currently, numerous challenges remain in identifying children early enough to maximize benefits of interventions ( Colenbrander et al., 2018 ; Gersten et al., 2017b ). Investigators often use behavioral precursors or correlates of reading to estimate children’s risk for reading failure. Whereas this work has shown some promise ( Catts et al., 2015 ; Compton et al., 2006 , 2010 ; Lyytinen et al., 2015 ; Thompson et al., 2015 ), identification of risk typically involves high error rates, especially for preschoolers and kindergarteners who might benefit most from early identification and intervention. Similar challenges to accuracy have emerged when identifying older children with reading disabilities. Historically, this process has relied on discrepancy models (e.g., such as between reading skill and general cognitive aptitude), often yielding a just single comparison on which decisions are based (Waesche et al., 2011).

Challenges to identification for both younger and older children may be best met with frameworks that recognize the multifactorial casual basis of reading problems ( Pennington et al., 2012 ). Newer models of identification that combine across multiple indicators of risk derived from current skill, and that augment these indicators with other metrics of potential risk, may yield improved identification and interventions (e.g., Erbeli et al., 2018 ; Spencer et al., 2011). In particular, future research will need to consider and combine, while considering both additive and interactive effects, a wide array of measures, which may include genetic, neurological, and biopsychosocial indicators ( Wagner et al., 2019 ). Furthermore, more evaluation is needed of some new models of identification that integrate both risk and protective, or resiliency, factors, to see if these models increase the likelihood of correctly identifying those children most in need of additional instructional support (e.g., Catts & Petscher, 2020 ; Haft et al., 2016 ). Even if beneficial, it is likely that for early identification to be maximally effective, early risk assessments will need to be combined with progress monitoring of response to instruction ( Miciak & Fletcher, 2020 ). Of course, for such an approach to be successful, all children must receive high-quality reading instruction from the beginning and interventions need to be in place to address children who show varying levels of risk ( Foorman et al., 2016a ). Identifying children at risk and providing appropriate intervention early on has the potential to significantly improve reading outcomes and reduce the negative consequences of reading failure.

Intervention Innovations

Despite successes, too many children still struggle to read novel text with understanding, and intervention design efforts have not fully met this challenge ( Compton et al., 2014 ; Phillips et al., 2016 ; Vaughn et al., 2017 ). Greater creativity and integration of research from a broader array of complementary fields, including cognitive science and behavioral genetics may be required to deal with long-standing problems. For example, genetic information may have causal explanatory power; randomized trials are needed to evaluate the efficacy of using such information to select and individualize instruction and intervention ( Hart, 2016 ).

The field would benefit from increased attention to the problem of fading intervention effects over time. Although there can be detectable effects of interventions several years after they are completed (e.g., Blachman et al., 2014 ; Vadasy et al., 2011 ; Vadasy & Sanders, 2013 ), invariably effect sizes reduce over time. A meta-analysis of long-term effects of interventions for phonemic awareness, fluency, and reading comprehension found a 40 percent reduction in effect sizes within one year post-intervention ( Suggate, 2016 ). Perhaps reading interventions with larger initial effects or sequential reading interventions with smaller but cumulating effects would be more resistant to fade-out.

Solutions to the problem of diminishing effects may be inspired by examples from other fields. The field of memory includes examples of content that appears immune from forgetting. This phenomenon has been called permastore ( Bahrick, 1984 ). For example, people only meaningfully exposed to a foreign language in school classes will still retain some knowledge of the language 50 years later. Additionally, expertise in the form of world-class performance appears to result from cumulative effects of long-term deliberate practice ( Ericsson, 1996 ), and skilled reading can be viewed as an example of expert performance ( Wagner & Stanovich, 1996 ). Informed by these concepts and by advances in early math instruction (e.g., Sarama et al., 2012 ; Kang et al., 2019 ), reading intervention studies should prioritize follow-up evaluations, including direct comparisons of follow-through strategies aimed at sustaining benefits from earlier instruction. For example, studies should evaluate booster interventions, professional development that better aligns cross-grade instruction, and how re-teaching and cumulative review may consolidate skill acquisition across time (e.g., Cepeda et al., 2006 ; Smolen et al., 2016 ).

Translational and Implementation Science

If the science of reading is to be applied in a manner resulting in achievement for all learners, the field must increase its focus on processes supporting implementation of evidence-based reading practices in schools. The field can leverage its considerable evidence-base to systematically investigate, with replication, both the effectiveness of reading instructional practices with diverse learners and to investigate processes that facilitate or prevent adoption, implementation, and sustainability of these practices (National Research Council, 2002; Schneider, 2018 ; Slavin, 2002 ). Research on these processes in educational contexts may be best facilitated by making use of methodological and conceptual tools developed within the traditions of translation and implementation science research ( Gilliland et al., 2019 ; Eccles & Mittman, 2006 ). For example, these frameworks can support studies on whether and how educators and policymakers use information about evidence to inform decision making (e.g., Farley-Ripple et al., 2018 ) and studies on how institutional routines may need to be adapted to best integrate new procedures and practices (e.g., scheduling changes in the school day; Foorman et al., 2016b ).

Reading research that uses translational and implementation science frameworks and methodologies will make more explicit the processes of adoption, implementation and sustainability and how these interact within diverse settings and with multiple populations ( Brown et al., 2017 ; Fixsen et al., 2005 , 2013 ). This work will be guided by new questions, not only asking “what works” but also “what works for whom under what conditions” and “what factors promote sustainability of implementation.” Innovative studies would adhere to rigorous scientific standards, prioritize hypothesis testing within a deductive, experimental framework, and leverage qualitative methodologies to systematically explore implementation processes and factors ( Brown et al., 2017 ). Results could iteratively inform the breadth of scientific reading research, including basic mechanisms related to reading and the development of novel assessments and interventions to support achievement among diverse learners in diverse settings ( Cook & Odom, 2013 ; Douglas et al., 2015 ; Forman et al., 2013 ).

There has recently been a resurgence of the debate on the science of reading, and in this article, we described the existing evidence base and possible future directions. Compelling evidence is available to guide understanding of how reading develops and identify proven instructional practices that impact both decoding and linguistic comprehension. Whereas there is some evidence that is either not compelling or has yet to be generated for instructional practices and programs that are widely used, the scientific literature on reading is ever-expanding through contributions from the fields education, psychology, linguistics, communication science, neuroscience, and computational sciences. As these additions to the literature mature and contribute to an evidence base, we anticipate they will inform and shape the science of reading as well as the science of teaching reading.

Acknowledgments

First author was determined by group consensus. Authors equally contributed and are listed and alphabetically. The authors’ work was supported by funding from the Chan Zuckerberg Initiative, the Institute of Education Sciences (R305A160241, R305A170430, R305F100005, R305F100027, R324A180020, R324B19002) and Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD52120, P20HD091013, HD095193, HD072286).

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Subject Guides

Being critical: a practical guide

Reading academic articles
Being critical
Critical thinking
Evaluating information
Critical reading
Critical writing

"Academic texts are not meant to be read through from beginning to end."

Academic literature is pitched at an ‘academic audience’ who will already have an understanding of the topic. Academic texts can be complicated and difficult to read, but you don't necessarily have to read every word of a piece of academic writing to get what you need from it. On this page we'll take a look at strategies for reading the most common form of academic literature: the academic journal article . But these strategies may also be applied to other forms of academic writing (and in some cases even to non-academic sources of information). We'll ask ourselves why we're doing the reading in the first place, before examining the typical structure(s) of an article , from abstract to conclusion , and considering the best route through . We'll also take a look at the best strategies for reading .

Journal articles

One of the most common academic sources is the journal article . Researchers publish their research in academic journals which usually cover a specific discipline. Journals used to be printed magazines but now they're mostly published online. Some journals have stronger reputations and more rigorous editorial controls than others.

Types of article

There are all sorts of different types of journal article. The article's title might make it clear what type it is, but other aspects of the article will also give you a clue.

Research / Empirical

Results of studies or experiments, written by those who conducted them. They're built around observation or experiment, and generally start with (or at least have a prominent) methodology.

Descriptions of an individual situation in detail, identify characteristics, findings, or issues, and analyse the case using relevant methodologies or theoretical frameworks.

Summaries of other studies, identifying trends to draw broader conclusions. We look at these in more detail in our section on review articles .

Theoretical

Scholarly articles regarding abstract principles in a specific field of knowledge, not tied to empirical research or data. They may be predictive, and based upon an understanding of the field. They generally start with a background section or a literature review.

Real world techniques, workflows etc. This type of article is generally found in trade / professional journals which are aimed at a professional or practicing audience rather than an academic one.

Peer review

Most good quality journals (and even some bad ones) employ a process called peer-review whereby submitted articles are vetted by a panel of fellow experts in the field. The peer-review panel may demand extensive re-writes of an article to bring it to an acceptable standard for publication. Flaws in the methodology may be highlighted and the author will then have to address these in the text. The result should be that the published work is reliable and of a high standard, and this is usually the case (though not always, as this blog post on the problems with Peer Review explains). Many databases will let you filter to exclude work that hasn't been peer-reviewed.

Finding articles

You could read every journal that's published on your subject, but that's probably a lot of journals. Fortunately, there are databases which catalogue the contents of a selection of journals. You can search these databases to find the articles that will be of use to you.

What are we reading it for anyway?

Maybe we're reading an academic article or similar text for fun, or for our own personal enlightenment, in which case we'll probably want to savour every word of it. But more often than not there are other interests at play:

To update your knowledge on progress in a particular area or field of study
To find a solution to a specific problem
To understand the causes of a particular issue, problem, or situation
To understand certain fundamental aspects, concepts, or theories
To inform your own research and help you select an appropriate methodology
To find support for your own views and arguments
To impress others
Because the article has been assigned to you by your tutor and so you've got to read it!

Why we're reading the article will inform how we go about it. If we're after a specific piece of information we just need to find that information; there's no point reading every single word.

Ask yourself:

Why am I reading this?
What do I want to get out of it?
What do I already know?
How will I know when I have read enough?

The structure of an academic article

Broadly speaking there are two main categories of academic article: empirical and theoretical . The former tends to be associated with the sciences (including social sciences), and the latter with the arts and humanities, though there may be cases where a science or social science paper is theoretical and an arts or humanities paper is empirical.

The typical sections of an article

These are the typical sections you'll find in an academic article (obviously, these are only a guide, and headings and structures may vary in practice):

Empirical paper

Abstract — a summary of the content.

Introduction — identifies the gaps in the existing knowledge, and outlines the aims of the paper.

Methodology — explains the design of the study, and what took place.

Results — explains what the outcome of the study was.

Discussion & Conclusion — interprets the results and makes recommendations based on that interpretation.

Theoretical paper

Body — considers the background of the topic and any competing analyses.

Summary — considers how the various arguments relate.

Discussion & Conclusion — interprets the analysis and makes recommendations accordingly.

What to get from each section

Each of the sections can tell you some useful information. You don't need to read every section to get what you need.

Abstract — a good starting point for understanding the scope and outcome.

Introduction — you can generally skip an introduction, though it may help give you some context.

Methodology — pay attention to the validity of the study design – is it appropriate?

Results — have any results been ignored?

Discussion & Conclusion — is the analysis valid?

Body — has anything been missed?

Summary — are the arguments well founded?

The route through

You don't need to read every word of an article to get what you need from it. Academic articles are pretty-much always split up into sections, and these sections tend to follow a fairly consistent pattern. Skipping around these sections (rather than reading them in order) allows you to appraise the article more quickly, helping you decide whether or not you need to read any more of it.

Title & abstract

"Let's start at the very beginning / a very good place to start"

– Maria Rainer

If by 'the very beginning' Maria meant 'the title ', then yes, it is a pretty decent starting point. It will give us a clue as to the type of article we're looking at, which will help determine our next steps.

The abstract is another obvious place to begin the journey. The abstract provides a summary of the article, including the key findings, so reading an abstract is a lot quicker than reading a whole article.

But be aware that the abstract will have been written by the authors of the article, and so won’t be a neutral account of the research finding. Don’t be too accepting of what is presented: make sure you think critically about what's being said. The abstract may be glossing over certain shortcomings of the article, or may be spinning a stronger outcome than is reached in the text.

The conclusion

Skip to the end. That's where all the action is! There's not really such a thing as spoilers in academic texts, so if the butler did it it's good to know from the outset. What conclusions are the authors reaching, and do they seem relevant to what you're needing?

Like the abstract, the conclusion may reflect the writers' biases, so we can't rely on it entirely. But, as with all the steps on this journey, it may help us determine whether or not we need to spend any more time reading the article.

Moving on from there...

Your next step depends largely on discipline: for an empirical (science or social science) research paper you'll want to look at the method and results to start to look at what was actually carried out, and what happened. You can then start to think about whether the conclusion being reached is valid given the approaches taken and the observations made.

In a theoretical (arts & humanities, and some social science) paper you'll probably need to pick through the body of the article and maybe focus on the summary section.

Reading strategies

When you’re reading you don’t have to read everything with the same amount of care and attention. Sometimes you need to be able to read a text very quickly.

There are three different techniques for reading:

Scanning — looking over material quite quickly in order to pick out specific information;
Skimming — reading something fairly quickly to get the general idea;
Close reading — reading something in detail.

You'll need to use a combination of these methods when you are reading an academic text: generally, you would scan to determine the scope and relevance of the piece, skim to pick out the key facts and the parts to explore further, then read more closely to understand in more detail and think critically about what is being written.

These strategies are part of your filtering strategy before deciding what to read in more depth. They will save you time in the long run as they will help you focus your time on the most relevant texts!

You might scan when you are...

...browsing a database for texts on a specific topic;
...looking for a specific word or phrase in a text;
...determining the relevance of an article;
...looking back over material to check something;
...first looking at an article to get an idea of its shape.

Scan-reading essentially means that you know what you are looking for. You identify the chapters or sections most relevant to you and ignore the rest. You're scanning for pieces of information that will give you a general impression of it rather than trying to understand its detailed arguments.

You're mostly on the look-out for any relevant words or phrases that will help you answer whatever task you're working on. For instance, can you spot the word "orange" in the following paragraph?

Being able to spot a word by sight is a useful skill, but it's not always straightforward. Fortunately there are things to help you. A book might have an index, which might at least get you to the right page. An electronic text will let you search for a specific word or phrase. But context will also help. It might be that the word you're looking for is surrounded by similar words, or a range of words associated with that one. I might be looking for something about colour, and see reference to pigment, light, or spectra, or specific colours being called out, like red or green. I might be looking for something about fruit and come across a sentence talking about apples, grapes and plums. Try to keep this broader context in mind as you scan the page. That way, you're never really just going to be looking for a single word or orange on its own. There will normally be other clues to follow to help guide your eye.

Approaches to scanning articles:

Make a note of any questions you might want to answer – this will help you focus;
Pick out any relevant information from the title and abstract – Does it look like it relates to what you're wanting? If so, carry on...
Flick or scroll through the article to get an understanding of its structure (the headings in the article will help you with this) – Where are certain topics covered?
Scan the text for any facts , illustrations , figures , or discussion points that may be relevant – Which parts do you need to read more carefully? Which can be read quickly?
Look out for specific key words . You can search an electronic text for key words and phrases using Ctrl+F / Cmd+F. If your text is a book, there might even be an index to consult. In either case, clumps of results could indicate an area where that topic is being discussed at length.

Once you've scanned a text you might feel able to reject it as irrelevant, or you may need to skim-read it to get more information.

You might skim when you are...

...jumping to specific parts such as the introduction or conclusion;
...going over the whole text fairly quickly without reading every word;

Skim-reading, or speed-reading, is about reading superficially to get a gist rather than a deep understanding. You're looking to get a feel for the content and the way the topic is being discussed.

Skim-reading is easier to do if the text is in a language that's very familiar to you, because you will have more of an awareness of the conventions being employed and the parts of speech and writing that you can gloss over. Not only will there be whole sections of a text that you can pretty-much ignore, but also whole sections of paragraphs. For instance, the important sentence in this paragraph is the one right here where I announce that the important part of the paragraph might just be one sentence somewhere in the middle. The rest of the paragraph could just be a framework to hang around this point in order to stop the article from just being a list.

However, it may more often be that the important point for your purposes comes at the start of the paragraph. Very often a paragraph will declare what it's going to be about early on, and will then start to go into more detail. Maybe you'll want to do some closer reading of that detail, or maybe you won't. If the first paragraph makes it clear that this paragraph isn't going to be of much use to you, then you can probably just stop reading it. Or maybe the paragraph meanders and heads down a different route at some point in the middle. But if that's the case then it will probably end up summarising that second point towards the end of the paragraph. You might therefore want to skim-read the last sentence of a paragraph too, just in case it offers up any pithy conclusions, or indicates anything else that might've been covered in the paragraph!

For example, this paragraph is just about the 1980s TV gameshow "Treasure Hunt", which is something completely irrelevant to the topic of how to read an article. "Treasure Hunt" saw two members of the public (aided by TV newsreader Kenneth Kendall) using a library of books and tourist brochures to solve a series of five clues (provided, for the most part, by TV weather presenter Wincey Willis). These clues would generally be hidden at various tourist attractions within a specific county of the British Isles. The contestants would be in radio contact with a 'skyrunner' (Anneka Rice) who had a map and the use of a helicopter (piloted by Keith Thompson). Solving a clue would give the contestants the information they needed to direct the skyrunner (and her crew of camera operator Graham Berry and video engineer Frank Meyburgh) to the location of the next clue, and, ultimately, to the 'treasure' (a token object such as a little silver brooch). All of this was done against the clock, the contestants having only 45' to solve the clues and find the treasure. This, necessarily, required the contestants to be able to find relevant information quickly: they would have to select the right book from the shelves, and then navigate that text to find the information they needed. This, inevitably, involved a considerable amount of skim-reading. So maybe this paragraph was slightly relevant after all? No, probably not...

Skim-reading, then, is all about picking out the bits of a text that look like they need to be read, and ignoring other bits. It's about understanding the structure of a sentence or paragraph, and knowing where the important words like the verbs and nouns might be. You'll need to take in and consider the meaning of the text without reading every single word...

Approaches to skim-reading articles:

Pick out the most relevant information from the title and abstract – What type of article is it? What are the concepts? What are the findings?;
Scan through the article and note the headings to get an understanding of structure;
Look more closely at the illustrations or figures ;
Read the conclusion ;
Read the first and last sentences in a paragraph to see whether the rest is worth reading.

After skimming, you may still decide to reject the text, or you may identify sections to read in more detail.

Close reading

You might read closely when you are...

...doing background reading;
...trying to get into a new or difficult topic;
...examining the discussions or data presented;
...following the details or the argument.

Again, close reading isn't necessarily about reading every single word of the text, but it is about reading deeply within specific sections of it to find the meaning of what the author is trying to convey. There will be parts that you will need to read more than once, as you'll need to consider the text in great detail in order to properly take in and assess what has been written.

Approaches to the close reading of articles:

Focus on particular passages or a section of the text as a whole and read all of its content – your aim is to identify all the features of the text;
Make notes and annotate the text as you read – note significant information and questions raised by the text;
Re-read sections to improve understanding;
Look up any concepts or terms that you don’t understand.

In conclusion...

Did you read every word of this page up to this point, or did you skip straight to the conclusion? Whichever approach you took, here's our summary of how to go about reading an article:

Come up with some questions you need the text to answer – this will help you focus;
Read the abstract to get an idea about what the article is about;
Scan the text for signs of relevance, and to get an understanding of the scope of the article – which parts might you need to read?
Skim through the useful parts of the article (e.g. the conclusion) to get a flavour of what's being said;
If there are any sections of interest, read them closely ;
Consider the validity of the research process (method, sample size, etc.) or arguments being employed;
Make a note of what you find, and any questions the text raises.

How to read an article

Where do you start when looking at academic literature ? How can you successfully engage with the literature you find? This bitesized tutorial explores the structure of academic articles , shows where to look to check the validity of findings , and offers tips for navigating online texts.

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Effective strategies for improving reading comprehension.

Meenakshi Gajria Meenakshi Gajria St. Thomas Aquinas College
and Athena Lentini McAlenney Athena Lentini McAlenney St. Thomas Aquinas College
https://doi.org/10.1093/acrefore/9780190264093.013.1225
Published online: 27 October 2020

Reading comprehension, or the ability to extract information accurately from reading narrative or content area textbooks, is critical for school success. Many students identified with learning disabilities struggle with comprehending or acquiring knowledge from text despite adequate word-recognition skills. These students experience greater difficulty as they move from elementary to middle school where the focus shifts from “learning to read” to “reading to learn.” Although the group of students with learning disabilities vary with respect to their challenges in reading, some general characteristics of this group include problems identifying central ideas of a text, including its relationship to supporting ideas, differentiating between important and unimportant details, asking questions, drawing inferences, creating a summary, and recalling textual ideas. Typically, these students are passive readers that do not spontaneously employ task appropriate cognitive strategies nor monitor their ongoing understanding of the text, resulting in limited understanding of both narrative and expository texts. An evidence-based approach to comprehension instruction is centered on teaching students the cognitive strategies used by proficient readers. Within the framework of reading comprehension, the goal of cognitive strategies is to teach students to actively engage with the text, to make connections with it and their prior knowledge, so that learning becomes more purposeful, deliberate, and self-regulated.

Texts differ in the level of challenge that they present to students. Narrative texts are generally simpler to read as these are based on a temporal sequence of events and have a predictable story structure. In contrast, expository texts, such as social studies and science, can be particularly demanding as there are multiple and complex text structures based on the relationship of ideas about a particular concept or topic. Using principles of explicit instruction, all learners, including students with learning disabilities and English language learners, can be taught cognitive strategies that have been proven effective for increasing reading comprehension. Early research focused on the instruction in a single cognitive strategy to promote reading comprehension such as identifying story grammar elements and story mapping for narrative texts and identifying the main idea, summarizing, and text structure for expository texts. Later researchers embedded a metacognitive component, such as self-monitoring with a specific cognitive strategy, and also developed multicomponent reading packages, such as reciprocal teaching, that integrated the use of several cognitive strategies. Instruction in cognitive and metacognitive strategies is a promising approach for students with learning disabilities to support their independent use of reading comprehension strategies and for promoting academic achievement across content areas and grade levels.

reading comprehension
story grammar
story mapping
summarization
text structure
multicomponent
cognitive strategies

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Four Tips on How To Read AI Research Papers Effectively

AI In the Enterprise
Large Language Models

Amber Roberts

Machine learning engineer.

According to a recent survey, over two-thirds (66.9%) of developers and machine learning teams are planning production deployments of LLM apps in the next 12 months or “as fast as possible” – and 14.1% are already in production!

Given the rapid rate of progress and constant drumbeat of new foundation models, orchestration frameworks and open source libraries – as well as the workaday challenges of getting an app into production – it can be difficult to find the time to digest and read the dizzying array of cutting-edge AI research papers hitting arXiv.

That task has never been more critical, however, as the time between academic discovery and industry application moves from years to weeks. How can teams read AI research papers without losing nuance, with an eye toward pragmatic application, while balancing real-world challenges?

In a recent webinar with Deep Learning AI, we explored strategies for understanding and applying the latest research, reducing mean time to application. Here are four takeaways.

Follow the Right People

Identify the Type of Paper and Break It Down Accordingly

While there are probably dozens of archetypes of AI research papers across academia and industry, many fall under three general categories that are a useful shorthand for practitioners.

Surveys typically give a detailed overview of a certain topic, providing a summary spread of where the field is right now in a specified area. Generally, the goal with a survey paper is to get an overview of what is happening in a given field to identify trends and common patterns for research opportunities.

An example is illustrative. Say you are wondering whether to read “ A Survey of Large Language Models .” This might be useful if you want to:

Use one of the LLMs or compare your current LLM
Compare open vs. closed source capabilities
Compare pre-training, data curation and prompting methods
Compare architectures and parameters (Encoder/Decoder, Size, Normalization, Activation, Bias, Attention patterns)
Compare cost, compute or hardware components
Review the comparative capacities and evaluations

It’s worth keeping in mind that survey papers aren’t as good at offering a technical deep dive into a specific model or introducing any new or novel ideas.

Benchmarking and Dataset Papers

Benchmarking papers are usually the first step after a breakthrough paper because they often define how we evaluate new breakthroughs. Examples in the world of LLMs many will recognize include MMLU, HellaSwag, and TruthfulQA. These papers typically introduce a dataset for testing or roll out a new evaluation approach on a dataset with a goal of using a new dataset or evaluation metric to evaluate capabilities of an LLM, learning the limitations of a model based on what and how they are evaluated, or considering how to expand benchmark capabilities.

To readers, these papers are worth reading when you want to use the metric to benchmark your current LLM, compare model costs to performance benchmarks, or potentially modify a benchmark to be better for your use case.

A few things to look out for on these papers:

Bias in the dataset
Does a single definitive answer exist?
Does the question provide enough context?
Does it count if they get the right answer the wrong way?

Generally, these papers are less useful for introducing new capabilities or breakthroughs or detailed breakdowns.

Breakthrough Papers

Breakthrough papers – think Mixtral of Experts , QLoRA: Efficient Fine Tuning of Quantized LLMs, or LLaMA: Open and Efficient Foundation Language Models – are must-reads because they represent major leaps forward in the field. Reading these effectively generally means understanding what novel idea is being introduced and how it impacts the current environment, with potential applications. These papers do not usually provide a good overview of a whole space or show the datasets launched to evaluate a model.

Be An Active, Agile Reader

Approach each paper knowing that it’s a piece of a larger puzzle. Recognize that what you’re reading today might be challenged or built upon tomorrow. This field evolves rapidly, and maintaining an open, inquisitive mind is essential. For technical readers, this means constantly questioning and validating findings, even if they come from reputed sources or established theories. To that end, getting hands-on is critical. Implementing a model from a paper in a notebook, replicating a study, or even proposing an alternative approach can all help not only understand the paper better but also contribute to the field.

Follow Real-Time Progress In the Field

(1/6) Can LLMs Do Time Series Analysis ⏲️? GPT-4 vs Claude 3 Opus 🥊 We have seen a lot of customers trying to apply LLMs to all kinds of data, but have not seen many Evals that show how well LLMs can analyze patterns in data that are not text related – especially timeseries🕰️… pic.twitter.com/7t95VEG9aQ — Aparna Dhinakaran (@aparnadhinak) March 29, 2024

Traditional, peer-reviewed research takes a long time. Even preprint papers involve long review processes and tight reviews of results, with collaboration between many authors. In an industry where foundation model breakthroughs and new frameworks are upending traditional machine learning use cases overnight, however, it is important to stay abreast of research in all of its forms.

To that end, our co-founder and Chief Product Officer Aparna Dhinakaran recently started releasing bi-weekly research on social media and our blog on burning questions from customers, publishing repeatable open source results – reviewing with internal teams prior to publishing to test for “holes.” We are encouraged to see others embracing this approach as well on fast-moving topics, with the understanding that we are going fast so we might make mistakes – and that’s OK so long as we own up to them.

As AI continues to grow, these skills will be increasingly crucial for staying abreast of the latest developments and making meaningful contributions.

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Open access
Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

James Shaw 1 , 13 ,
Joseph Ali 2 , 3 ,
Caesar A. Atuire 4 , 5 ,
Phaik Yeong Cheah 6 ,
Armando Guio Español 7 ,
Judy Wawira Gichoya 8 ,
Adrienne Hunt 9 ,
Daudi Jjingo 10 ,
Katherine Littler 9 ,
Daniela Paolotti 11 &
Effy Vayena 12

BMC Medical Ethics volume 25 , Article number: 46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table 1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table 2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Modern computer systems and applications, with unprecedented scale, complexity, and security needs, require careful co-design and co-evolution of hardware and software. The ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (opens in new tab) , is the main forum where researchers bridge the gap between architecture, programming languages, and operating systems to advance the state of the art.

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We are pleased to share that eight papers from Microsoft researchers and their collaborators have been accepted to the conference, spanning a broad spectrum of topics. In the field of AI and deep learning, subjects include power and frequency management for GPUs and LLMs, the use of Process-in-Memory for deep learning, and instrumentation frameworks. Regarding infrastructure, topics include memory safety with CHERI, I/O prefetching in modern storage, and smart oversubscription of burstable virtual machines. This post highlights some of this work.

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Characterizing power management opportunities for llms in the cloud.

The rising popularity of LLMs and generative AI has led to an unprecedented demand for GPUs. However, the availability of power is a key limiting factor in expanding a GPU fleet. This paper characterizes the power usage in LLM clusters, examines the power consumption patterns across multiple LLMs, and identifies the differences between inference and training power consumption patterns. This investigation reveals that the average and peak power consumption in inference clusters is not very high, and that there is substantial headroom for power oversubscription. Consequently, the authors propose POLCA: a framework for power oversubscription that is robust, reliable, and readily deployable for GPU clusters. It can deploy 30% more servers in the same GPU clusters for inference tasks, with minimal performance degradation.

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization

PIM-DL is the first deep learning framework specifically designed for off-the-shelf processing-in-memory (PIM) systems, capable of offloading most computations in neural networks. Its goal is to surmount the computational limitations of PIM hardware by replacing traditional compute-heavy matrix multiplication operations with Lookup Tables (LUTs). PIM-DL first enables neural networks to operate efficiently on PIM architectures, significantly reducing the need for complex arithmetic operations. PIM-DL demonstrates significant speed improvements, achieving up to ~37x faster performance than traditional GEMM-based systems and showing competitive speedups against CPUs and GPUs.

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety

Memory safety bugs have persistently plagued software for over 50 years and underpin some 70% of common vulnerabilities and exposures (CVEs) every year. The CHERI capability architecture (opens in new tab) is an emerging technology (opens in new tab) (especially through Arm’s Morello (opens in new tab) and Microsoft’s CHERIoT (opens in new tab) platforms) for spatial memory safety and software compartmentalization. In this paper, the authors demonstrate the viability of object-granularity heap temporal safety built atop CHERI with considerably lower overheads than prior work.

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines

Burstable virtual machines (BVMs) are a type of virtual machine in the cloud that allows temporary increases in resource allocation. This paper shows how to oversubscribe BVMs. It first studies the characteristics of BVMs on Microsoft Azure and explains why traditional approaches based on using a fixed oversubscription ratio or based on the Central Limit Theorem do not work well for BVMs: they lead to either low utilization or high server capacity violation rates. Based on the lessons learned from the workload study, the authors developed a new approach, called AUDIBLE, using a nonparametric statistical model. This makes the approach lightweight and workload independent. This study shows that AUDIBLE achieves high system utilization while enforcing stringent requirements on server capacity violations.

Complete list of accepted publications by Microsoft researchers

Amanda: Unified Instrumentation Framework for Deep Neural Networks Yue Guan, Yuxian Qiu, and Jingwen Leng; Fan Yang , Microsoft Research; Shuo Yu, Shanghai Jiao Tong University; Yunxin Liu, Tsinghua University; Yu Feng and Yuhao Zhu, University of Rochester; Lidong Zhou , Microsoft Research; Yun Liang, Peking University; Chen Zhang, Chao Li, and Minyi Guo, Shanghai Jiao Tong University

AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines Seyedali Jokar Jandaghi and Kaveh Mahdaviani, University of Toronto; Amirhossein Mirhosseini, University of Michigan; Sameh Elnikety , Microsoft Research; Cristiana Amza and Bianca Schroeder, University of Toronto, Cristiana Amza and Bianca Schroeder, University of Toronto

Characterizing Power Management Opportunities for LLMs in the Cloud (opens in new tab) Pratyush Patel, Microsoft Azure and University of Washington; Esha Choukse (opens in new tab) , Chaojie Zhang (opens in new tab) , and Íñigo Goiri (opens in new tab) , Azure Research; Brijesh Warrier (opens in new tab) , Nithish Mahalingam, Ricardo Bianchini (opens in new tab) , Microsoft AzureResearch

Cornucopia Reloaded: Load Barriers for CHERI Heap Temporal Safety Nathaniel Wesley Filardo , University of Cambridge and Microsoft Research; Brett F. Gutstein, Jonathan Woodruff, Jessica Clarke, and Peter Rugg, University of Cambridge; Brooks Davis, SRI International; Mark Johnston, University of Cambridge; Robert Norton , Microsoft Research; David Chisnall, SCI Semiconductor; Simon W. Moore, University of Cambridge; Peter G. Neumann, SRI International; Robert N. M. Watson, University of Cambridge

CrossPrefetch: Accelerating I/O Prefetching for Modern Storage Shaleen Garg and Jian Zhang, Rutgers University; Rekha Pitchumani, Samsung; Manish Parashar, University of Utah; Bing Xie , Microsoft; Sudarsun Kannan, Rutgers University

Kimbap: A Node-Property Map System for Distributed Graph Analytics Hochan Lee, University of Texas at Austin; Roshan Dathathri, Microsoft Research; Keshav Pingali, University of Texas at Austin

PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization Cong Li and Zhe Zhou, Peking University; Yang Wang , Microsoft Research; Fan Yang, Nankai University; Ting Cao and Mao Yang , Microsoft Research; Yun Liang and Guangyu Sun, Peking University

Predict; Don’t React for Enabling Efficient Fine-Grain DVFS in GPUs Srikant Bharadwaj , Microsoft Research; Shomit Das, Qualcomm; Kaushik Mazumdar and Bradford M. Beckmann, AMD; Stephen Kosonocky, Uhnder

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Research Focus: Week of April 15, 2024

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Research at Microsoft 2023: A year of groundbreaking AI advances and discoveries

Flowchart showing natural language is transformed into a program in domain specific language using an LLM. This step is called Intent formalization. The user is able to modify, repair and query. The Program in DSL is then converted into natural language representation that can be in text or visual formats. The Program in DSL is also separatedly converted into Code via the Code Generation pipeline. This step is called Robust Code Generation.

PwR: Using representations for AI-powered software development

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Research Focus: Week of November 22, 2023

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Reading a Scholarly Article
6 Reading Test Taking Strategies That Work
Research-Based Reading Intervention Strategies
Reading research challenges: Strategies for reading research papers
Reading an Academic Article
Reading Strategies Free Posters

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6 Effective Reading Strategies for Comprehension
Critical Reading & Engaging with Sources: Critical Reading Strategies
The Reading Strategies Book 2.0
How to read a paper quickly and effectively with AI
TECHNIQUES OF EFFECTIVE READING. #EffectiveReading #ReadingSkills #Comprehension @agsacademy3888
How to Read Articles for Improving reading Skills ?

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The Effectiveness of Reading Strategies on Reading Comprehension
Abstract —This research aimed to investigate the effectiveness. of reading strategies on reading comprehension of the second. year English major students who enrolled to study English. Reading ...
Improving Reading Skills Through Effective Reading Strategies
The research question is, The purpose of this study was to analyze the improvement of the students reading skills after they have taken presentations on reading strategies. 712 HÃ¼lya KÃ¼Ã§Ã¼koÄŸlu / Procedia - Social and Behavioral Sciences 70 ( 2013 ) 709 â€" 714 3.Method Reading proficiency is the most fundamental skill for ...
Organizing Your Social Sciences Research Paper
Specific Reading Strategies. Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.
Reading Comprehension Research: Implications for Practice and Policy
Reading comprehension is one of the most complex behaviors in which humans engage. Reading theorists have grappled with how to comprehensively and meaningfully portray reading comprehension and many different theoretical models have been proposed in recent decades (McNamara & Magliano, 2009; Perfetti & Stafura, 2014).These models range from broad theoretical models depicting the relationships ...
A comprehensive review of research on reading comprehension strategies
Considering the research foci and findings, we identified seven categories: (a) comparison of the strategy use in L1 and L2 reading; (b) comparison of EAL readers' and monolinguals' comprehension strategy use; (c) different L1 groups' strategy use; (d) the role of languages in the strategy use; (e) the relationship between reading proficiency and comprehension strategy use; (f) strategies in ...
Improving Reading Skills Through Effective Reading Strategies
To improve student comprehension, teachers can instruct reading strategies (Küçükoglu 2013). Strategies such as predicting, making connections, visualizing, inferring, questioning, and ...
The Science of Reading Comprehension Instruction
Decades of research offer important understandings about the nature of comprehension and its development. Drawing on both classic and contemporary research, in this article, we identify some key understandings about reading comprehension processes and instruction, including these: Comprehension instruction should begin early, teaching word-reading and bridging skills (including ...
Ten simple rules for reading a scientific paper
1. You are new to reading scientific papers. 1. For each panel of each figure, focus particularly on the questions outlined in Rule 3. 2. You are entering a new field and want to learn what is important in that field.
The Relationship Between Reading Strategy and Reading Comprehension: A
Abstract. This study synthesized the correlation between reading strategy and reading comprehension of four categories based on Weinstein and Mayer's reading strategy model. The current meta-analysis obtained 57 effect sizes that represented 21,548 readers, and all selected materials came from empirical studies published from 1998 to 2019.
Reading research challenges: Strategies for reading research papers
In fact, a study finds that researchers are expected to spend 23% of their total work time reading research publications. 1 In 2012, scientists in the US read, on average, 22 scholarly articles per month (or 264 per year). 2. The academic language used in research papers is concise, precise, and authoritative, and a readers' familiarity with ...
How to find, read and organize papers
Step 1: find. I used to find new papers by aimlessly scrolling through science Twitter. But because I often got distracted by irrelevant tweets, that wasn't very efficient. I also signed up for ...
(PDF) Reading Comprehension: Theories and Strategies Toward an
Academia.edu is a platform for academics to share research papers. Reading Comprehension: Theories and Strategies Toward an Effective Reading Instruction ... In pre-reading stage, students applied six reading strategies: previewing, creating question, making prediction, writing the main points of what reader already know about the text, reading ...
(PDF) Reading comprehension strategies in elementary school students
The purpose of this review article is to analyze publications made on reading comprehension strategies in primary school students in a pandemic. To carry out this work, 36 articles that were found ...
How to Read Research Papers— Unveiling AI Tool for Reading
These strategies include active reading, note-taking, and using AI tools for summarizing and understanding research papers. Active reading involves engaging with the text, asking questions, and making connections. Note-taking helps you remember important information and organize your thoughts.
PDF The Effectiveness of Reading Strategies on Reading Comprehension
through reading strategies. Reading strategies are purposeful means of comprehending the author's message [3].They are believed to influence readers in adjusting their reading behaviours to work on text difficulty, task demands and other contextual variables. Adams [4] identified the types of reading strategies as follows: A. Skimming
How the Science of Reading Informs 21st‐Century Education
The science of reading should be informed by an evolving evidence base built upon the scientific method. Decades of basic research and randomized controlled trials of interventions and instructional routines have formed a substantial evidence base to guide best practices in reading instruction, reading intervention, and the early identification of at-risk readers.
Reading academic articles
Academic literature is pitched at an 'academic audience' who will already have an understanding of the topic. Academic texts can be complicated and difficult to read, but you don't necessarily have to read every word of a piece of academic writing to get what you need from it. On this page we'll take a look at strategies for reading the ...
The Writing Center
This handout is adapted from Karen Rosenberg's article "Reading Games: Strategies for Reading Scholarly Sources". Reading scholarly sources can be difficult. This handout provides strategies to help you read dense, lengthy academic articles efficiently and effectively. 1: Examine the article for its audience.
PDF Reading Comprehension and Reading Strategies
A Research Paper Submitted in Partial Fulfillment of the Requirements for the t Master -. of~u~on Degree , r;l Approved: 2 Semester Credits ... students then began a six-week long study of the Self-Questioning Reading Strategy. At the conclusion of the study the students were again given the Qualitative Reading Inventory - 4 reading ...
Effective Research and Reading Strategies
Active strategies like asking questions, drawing connections, and reflecting can aid comprehension and retention. Practical tips such as discussing reading with others or taking breaks may further deepen understanding. Finally, changing reading speed as necessary may prove especially helpful when dealing with longer or more complex texts.
How to (seriously) read a scientific paper
I first get a general idea by reading the abstract and conclusions. The conclusions help me understand if the goal summarized in the abstract has been reached, and if the described work can be of interest for my own study. I also always look at plots/figures, as they help me get a first impression of a paper.
Effective Strategies for Improving Reading Comprehension
Within the framework of reading comprehension, the goal of cognitive strategies is to teach students to actively engage with the text, to make connections with it and their prior knowledge, so that learning becomes more purposeful, deliberate, and self-regulated.Texts differ in the level of challenge that they present to students.
Four Tips on How To Read AI Research Papers Effectively
These papers do not usually provide a good overview of a whole space or show the datasets launched to evaluate a model. Be An Active, Agile Reader. Approach each paper knowing that it's a piece of a larger puzzle. Recognize that what you're reading today might be challenged or built upon tomorrow.
Research ethics and artificial intelligence for global health
The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town ...
(PDF) A Review of Effective Reading Strategies to Teach Text
This paper reviews the reading strategies and their theoretical perspectives in reading comprehension inside/outside the classroom. Reading strategies aim to build vocabulary and help integrate ...
Microsoft at ASPLOS 2024: Advancing hardware and software for high
Microsoft welcomes talented individuals across various roles at Microsoft Research, Azure Research, and other departments. We are always pushing the boundaries of computer systems to improve the scale, efficiency, and security of all our offerings. You can review our open research-related positions here. Opens in a new tab