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A Review on Human-Computer Interaction (HCI)

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Project-based learning in human–computer interaction: a service‐dominant logic approach

Purpose This study aims to propose a service-dominant logic (S-DL)-informed framework for teaching innovation in the context of human–computer interaction (HCI) education involving large industrial projects. Design/methodology/approach This study combines S-DL from the field of marketing with experiential and constructivist learning to enable value co-creation as the primary method of connecting diverse actors within the service ecology. The approach aligns with the current conceptualization of central university activities as a triad of research, education and innovation. Findings The teaching framework based on the S-DL enabled ongoing improvements to the course (a project-based, bachelor’s-level HCI course in the computer science department), easier management of stakeholders and learning experiences through students’ participation in real-life projects. The framework also helped to provide an understanding of how value co-creation works and brought a new dimension to HCI education. Practical implications The proposed framework and the authors’ experience described herein, along with examples of projects, can be helpful to educators designing and improving project-based HCI courses. It can also be useful for partner companies and organizations to realize the potential benefits of collaboration with universities. Decision-makers in industry and academia can benefit from these findings when discussing approaches to addressing sustainability issues. Originality/value While HCI has successfully contributed to innovation, HCI education has made only moderate efforts to include innovation as part of the curriculum. The proposed framework considers multiple service ecosystem actors and covers a broader set of co-created values for the involved partners and society than just learning benefits.

Recommender Systems: Past, Present, Future

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm

In this paper, we use machine learning algorithms to conduct in-depth research and analysis on the construction of human-computer interaction systems and propose a simple and effective method for extracting salient features based on contextual information. The method can retain the dynamic and static information of gestures intact, which results in a richer and more robust feature representation. Secondly, this paper proposes a dynamic planning algorithm based on feature matching, which uses the consistency and accuracy of feature matching to measure the similarity of two frames and then uses a dynamic planning algorithm to find the optimal matching distance between two gesture sequences. The algorithm ensures the continuity and accuracy of the gesture description and makes full use of the spatiotemporal location information of the features. The features and limitations of common motion target detection methods in motion gesture detection and common machine learning tracking methods in gesture tracking are first analyzed, and then, the kernel correlation filter method is improved by designing a confidence model and introducing a scale filter, and finally, comparison experiments are conducted on a self-built gesture dataset to verify the effectiveness of the improved method. During the training and validation of the model by the corpus, the complementary feature extraction methods are ablated and learned, and the corresponding results obtained are compared with the three baseline methods. But due to this feature, GMMs are not suitable when users want to model the time structure. It has been widely used in classification tasks. By using the kernel function, the support vector machine can transform the original input set into a high-dimensional feature space. After experiments, the speech emotion recognition method proposed in this paper outperforms the baseline methods, proving the effectiveness of complementary feature extraction and the superiority of the deep learning model. The speech is used as the input of the system, and the emotion recognition is performed on the input speech, and the corresponding emotion obtained is successfully applied to the human-computer dialogue system in combination with the online speech recognition method, which proves that the speech emotion recognition applied to the human-computer dialogue system has application research value.

Human–Computer Interaction-Oriented African Literature and African Philosophy Appreciation

African literature has played a major role in changing and shaping perceptions about African people and their way of life for the longest time. Unlike western cultures that are associated with advanced forms of writing, African literature is oral in nature, meaning it has to be recited and even performed. Although Africa has an old tribal culture, African philosophy is a new and strange idea among us. Although the problem of “universality” of African philosophy actually refers to the question of whether Africa has heckling of philosophy in the Western sense, obviously, the philosophy bred by Africa’s native culture must be acknowledged. Therefore, the human–computer interaction-oriented (HCI-oriented) method is proposed to appreciate African literature and African philosophy. To begin with, a physical object of tablet-aid is designed, and a depth camera is used to track the user’s hand and tablet-aid and then map them to the virtual scene, respectively. Then, a tactile redirection method is proposed to meet the user’s requirement of tactile consistency in head-mounted display virtual reality environment. Finally, electroencephalogram (EEG) emotion recognition, based on multiscale convolution kernel convolutional neural networks, is proposed to appreciate the reflection of African philosophy in African literature. The experimental results show that the proposed method has a strong immersion and a good interactive experience in navigation, selection, and manipulation. The proposed HCI method is not only easy to use, but also improves the interaction efficiency and accuracy during appreciation. In addition, the simulation of EEG emotion recognition reveals that the accuracy of emotion classification in 33-channel is 90.63%, almost close to the accuracy of the whole channel, and the proposed algorithm outperforms three baselines with respect to classification accuracy.

Wearable devices in diving: A systematic review (Preprint)

BACKGROUND Wearable devices have grown enormously in importance in recent years. While wearables have generally been well studied, they have not yet been discussed in the underwater environment. OBJECTIVE The reason for this systematic review was to systematically search for the wearables for underwater operation used in the scientific literature, to make a comprehensive map of their capabilities and features, and to discuss the general direction of development. METHODS In September 2021, we conducted an extensively search of existing literature in the largest databases using keywords. For this purpose, only articles were used that contained a wearable or device that can be used in diving. Only articles in English were considered, as well as peer-reviewed articles. RESULTS In the 36 relevant studies that were found, four device categories could be identified: safety devices, underwater communication devices, head-up displays and underwater human-computer interaction devices. CONCLUSIONS The possibilities and challenges of the respective technologies were considered and evaluated separately. Underwater communication has the most significant influence on future developments. Another topic that has not received enough attention is human-computer interaction.

Analyzing the mental states of the sports student based on augmentative communication with human–computer interaction

Recognition of facial expressions and its application to human computer interaction, physical education system and training framework based on human–computer interaction for augmentative and alternative communication, enhancing the human-computer interaction through the application of artificial intelligence, machine learning, and data mining, applications of human-computer interaction for improving erp usability in education systems, export citation format, share document.

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Reflections on emerging HCI–AI research

Curmudgeon corner.

• Published: 09 March 2022
• Volume 39 , pages 407–409, ( 2024 )

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• Swaroop Panda 1 &
• Shatarupa Thakurta Roy 1  

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Human computer interaction (HCI) has grown into a mature field of research. With artificial intelligence (AI) finding ubiquitous applications, HCI research is now moving ahead towards accommodating and integrating these approaches. A part of the research community is of the opinion that HCI and AI are fundamentally opposed to each other: with AI-powered devices being demonic and humans are to lose in a race against them and HCI-oriented products being human centred being the flag bearers of the human race. In this paper, we look at and analyse this perspective of how HCI relates to AI and the potential HCI–AI integration. We suggest that humans evolve by having dynamic identities. We further discuss its implications on the AI critique and suggest some new perspectives on the possible integration of HCI and AI research which we hope would be useful for the community.

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1 Introduction

Human computer interaction (HCI), primarily, is the study of how people interact with computers. It has now grown into an independent research discipline with many researchers completely dedicating their research interests. HCI still remains at the core an interdisciplinary topic. Such an interdisciplinary nature has helped HCI to propagate into many independent dimensions such as UI/UX design, ubiquitous computing or visualization. Like other research disciplines, HCI intends to explore more areas of application.

One such potential application that HCI research intends to explore is the thriving artificial intelligence (AI) research. AI, broadly, is intelligence exhibited by machines. Artificial intelligence has found plenty of applications in research areas such as image recognition, speech processing, and image generation among others. These developments can be seen in self-driving cars, AI assistants and many other applications. These devices, applications and artefacts have found their way into everyday lives across multiple cultures. The ubiquitous applications of AI across digital devices have elicited the need of user interfaces. HCI-propelled technologies, such as UI/UX frameworks, which have been already successful with existing technologies, have the potential to prove very useful in these AI-driven technologies.

However, there remains a bridge for HCI to cross. A formidable critique of AI research tends to view AI as agents that have completely opposed interests with respect to humans. AI, in a more developed form, would compete with man for resources. In contrast, HCI historically has been viewed as an area of research that is dedicated to building computing systems that prioritize human interests. So, in a way, HCI stands directly opposed to AI. In this paper, we look at the relationship between AI and HCI. For this purpose, we briefly look at the history and analyse the discussion that has been taking place around HCI and AI integration. We also look at how AI has an impact on human lives, derive insights from them and conclude by suggesting a few implications of these insights into HCI research.

2 The critique of AI and the existing HCI–AI discussions

A principal part of the criticism of AI focuses on the fact that AI at some point in time will compete with, outlive humans and make them obsolete. This sort of argument is usually dismissed by AI researchers, other technologists and investors as some sort of witless petrified doomsday speculation. However, we think that there lies some sort of merit in the argument and countering this criticism can yield a lot of insights for the HCI community. Before diving in, it is imperative to examine some existing literature on HCI–AI integration. Winograd ( 4 ) writes that the AI and HCI communities have had opposing views of how humans and computers should interact. The work by Grudin ( 1 ) has described how both these fields received attention and funding alternatively across the 1950s to the 2000s. This apparently proved to be a very fertile period of understanding AI as well as for HCI. Shneiderman and Maes ( 3 ) have a very interesting discussion about the nature of the human–computer interface. Should the human interact with the computer as if it is another human or is it that it is not possible (with technical and philosophical constraints) to imagine a computer with human-like attributes? A major part of the debate was also between direct manipulation and interface agents. The basic idea is that whether agents learn the user's likes and dislikes and learn on behalf of them and whether the users really want that to happen. There are many interesting remarks in the discussion. Markoff ( 2 ) writes about the philosophical divide between John McCarthy and Douglas Engelbart. McCarthy set up his research system to come up with a superbrain kind of artificial intelligence (something that would be similar to, if not superior to) the human brain. Engelbart, on the other hand, thought of computing as a method to 'augment' the human. Such perspectives are really helpful in providing ground to the inevitable HCI–AI integration.

3 The AI critiques and the HCI–AI discussion

The basic arguments contained in most of the AI critiques boil down to a few ideas: What is to be done when machines take over redundant and low-level jobs? And what if machines and humans have opposing interests? We will try to analyse these basic ideas and see how the HCI–AI discussion pans out.

3.1 How humans have changed

Most of these discussions, if examined closely, maintain a very seemingly innocuous, but highly precarious position that humans throughout have remained the same, unchanged with time. This particular position serves as a substructure to most of the arguments governing the HCI and AI debates. To reassess this position, let us look at a saying in common parlance: 'First the man shapes the tool, and then the tool shapes the man'. Man's relationship with his tools is very intriguing. Say a man builds an axe and uses it to cut a tree. Now the axe has a prescribed technique to be used. It can only be used in such a way; it has to be hit hard on a tree in a direction and not just touched or rubbed on the surface of the tree to cut it. In other words, the axe has prescribed (or restricted) the degrees of freedom available to man to cut the tree, though allowing him to do so. Thus, the man, eventually, acquires the properties of the axe and the tool shapes the man. With this logic, technology as a tool can potentially change the human; take as an example, a technology like social media. Twitter takes up thinking with 140 characters at a time, Instagram takes up pictures of food and LinkedIn takes up details of jobs. A lot of humans do fail to realize that their choices are affected by their interaction, or they are a part of the giant social computer. There are a number of studies that study the direct and indirect impact of social media on humans.

3.2 What is human?

The answer to what humans really are is a never ending discussion across the social sciences and humanities disciplines. In HCI, Ben Shneiderman, a leading researcher, intends to get rid of the term, 'user-friendly', because it presupposes a universal overarching generalized definition of the "user". HCI research, no doubt, has gone deep into resolving this issue by including users of different age groups, cultures, and genders in their research, but without paying adequate attention to the idea that these users are conditioned by the tools that have been using for hundreds of years. The idea is not about inclusivity or diversity, but about being aware of the fact that the users are dynamic evolving entities. The question of what is human is then best left as an open question for the researcher to meticulously probe.

3.3 On the AI critique

In the light of the above insights, let us now look at the critiques that AI has been facing. One of the critiques is that AI would outcompete the humans and slowly make them obsolete. Competition requires a bounded environment and clear metrics to operate. One competes in a 100 m sprint race. Ideas, such as competing for life, simply do not make any sense because there can exist no consensus over the metrics used for such a measurement. In a 100 m race, time and distance are measured and there lies absolutely no disagreement over what each of them means. Competing for life thus is absurd because of the lack of consensus over the metrics. By this logic, the idea that AI is competing with humans (say for jobs) is slightly misplaced. It is necessary to determine what sort of jobs we are really looking at. Is it a simple repeatable low-level job description that can be replaced by writing a few lines of code? Is it a morally complex but low-level job that a machine is not supposed to do? Or is it a risk-laden, loosely defined job that a machine cannot do? Routine, bounded, morally resolvable bureaucratic jobs are ones that can be taken up by machines. These are jobs with succinct job descriptions. Creative, non-bureaucratic and other jobs requiring high agency and a moral calculus are examples of jobs that have loosely or undefined metrics and, by definition, machines cannot participate in.

The other idea of humans becoming obsolete is grounded upon the notion that humans are fundamentally functional in nature. This means to say that the basic human attribute is to be able to function in a given setting (economy, family or others). The word functional means performing a function for fulfilling a certain end. Feeling, experiencing and thinking are not always the means for a specific end, neither are ends in themselves yet fully human activities.

3.4 The emerging HCI–AI

With the new arsenal of insights, where do we stand at the HCI–AI junction? HCI then has not only to deal with a new AI, but also with a new human. As AI evolves, so does the human in a yin-yang recursive manner. One feeds the other. Trying to dissect or disentangle such an arrangement is to undermine the impact of one or the other. A visible implication on the HCI community is that the comforting ideas of human or user-centric need to be reformed, because the idea of human itself evolves with the tools. A physical implication of this is not that a designer must impose tools upon humans, but rather the designer keeps an open and evolving idea of a human or user while designing tools. Humans shape the tools, then tools shape the humans.

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Winograd T (2006) Shifting viewpoints: Artificial intelligence and human–computer interaction. Artif Intell 170(18):1256–1258

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Curmudgeon Corner is a short opinionated column on trends in technology, arts, science and society, commenting on issues of concern to the research community and wider society. Whilst the drive for super-human intelligence promotes potential benefits to wider society, it also raises deep concerns of existential risk, thereby highlighting the need for an ongoing conversation between technology and society. At the core of Curmudgeon concern is the question: What is it to be human in the age of the AI machine? -Editor.

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Panda, S., Roy, S.T. Reflections on emerging HCI–AI research. AI & Soc 39 , 407–409 (2024). https://doi.org/10.1007/s00146-022-01409-y

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Title: leave no context behind: efficient infinite context transformers with infini-attention.

Abstract: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.

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