essay on types of ai

Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

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Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training.

For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests. Human intervention was required to expand Siri’s knowledge base and functionality.

However, AI capabilities have been evolving steadily since the breakthrough development of  artificial neural networks  in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information.

Unlike basic machine learning models, deep learning models allow AI applications to learn how to perform new tasks that need human intelligence, engage in new behaviors and make decisions without human intervention. As a result, deep learning has enabled task automation, content generation, predictive maintenance and other capabilities across  industries .

Due to deep learning and other advancements, the field of AI remains in a constant and fast-paced state of flux. Our collective understanding of realized AI and theoretical AI continues to shift, meaning AI categories and AI terminology may differ (and overlap) from one source to the next. However, the types of AI can be largely understood by examining two encompassing categories: AI capabilities and AI functionalities.

1. Artificial Narrow AI

Artificial Narrow Intelligence, also known as Weak AI (what we refer to as Narrow AI), is the only type of AI that exists today. Any other form of AI is theoretical. It can be trained to perform a single or narrow task, often far faster and better than a human mind can.

However, it can’t perform outside of its defined task. Instead, it targets a single subset of cognitive abilities and advances in that spectrum. Siri, Amazon’s Alexa and IBM Watson are examples of Narrow AI. Even OpenAI’s ChatGPT is considered a form of Narrow AI because it’s limited to the single task of text-based chat.

2. General AI

Artificial General Intelligence (AGI), also known as  Strong AI , is today nothing more than a theoretical concept. AGI can use previous learnings and skills to accomplish new tasks in a different context without the need for human beings to train the underlying models. This ability allows AGI to learn and perform any intellectual task that a human being can.

3. Super AI

Super AI is commonly referred to as artificial superintelligence and, like AGI, is strictly theoretical. If ever realized, Super AI would think, reason, learn, make judgements and possess cognitive abilities that surpass those of human beings.

The applications possessing Super AI capabilities will have evolved beyond the point of understanding human sentiments and experiences to feel emotions, have needs and possess beliefs and desires of their own.

Underneath Narrow AI, one of the three types based on capabilities, there are two functional AI categories:

1. Reactive Machine AI

Reactive machines are AI systems with no memory and are designed to perform a very specific task. Since they can’t recollect previous outcomes or decisions, they only work with presently available data. Reactive AI stems from statistical math and can analyze vast amounts of data to produce a seemingly intelligent output.

Examples of Reactive Machine AI  

• IBM Deep Blue: IBM’s chess-playing supercomputer AI beat chess grandmaster Garry Kasparov in the late 1990s by analyzing the pieces on the board and predicting the probable outcomes of each move.
• The Netflix Recommendation Engine: Netflix’s viewing recommendations are powered by models that process data sets collected from viewing history to provide customers with content they’re most likely to enjoy.

2. Limited Memory AI

Unlike Reactive Machine AI, this form of AI can recall past events and outcomes and monitor specific objects or situations over time. Limited Memory AI can use past- and present-moment data to decide on a course of action most likely to help achieve a desired outcome.

However, while Limited Memory AI can use past data for a specific amount of time, it can’t retain that data in a library of past experiences to use over a long-term period. As it’s trained on more data over time, Limited Memory AI can improve in performance.

Examples of Limited Memory AI  

• Generative AI: Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating.
• Virtual assistants and chatbots: Siri, Alexa, Google Assistant, Cortana and IBM Watson Assistant combine natural language processing (NLP) and Limited Memory AI to understand questions and requests, take appropriate actions and compose responses.
• Self-driving cars: Autonomous vehicles use Limited Memory AI to understand the world around them in real-time and make informed decisions on when to apply speed, brake, make a turn, etc.

3. Theory of Mind AI

Theory of Mind AI is a functional class of AI that falls underneath the General AI. Though an unrealized form of AI today, AI with Theory of Mind functionality would understand the thoughts and emotions of other entities. This understanding can affect how the AI interacts with those around them. In theory, this would allow the AI to simulate human-like relationships.

Because Theory of Mind AI could infer human motives and reasoning, it would personalize its interactions with individuals based on their unique emotional needs and intentions. Theory of Mind AI would also be able to understand and contextualize artwork and essays, which today’s generative AI tools are unable to do.

Emotion AI is a theory of mind AI currently in development. AI researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.  

4. Self-Aware AI

Self-Aware AI is a kind of functional AI class for applications that would possess super AI capabilities. Like theory of mind AI, Self-Aware AI is strictly theoretical. If ever achieved, it would have the ability to understand its own internal conditions and traits along with human emotions and thoughts. It would also have its own set of emotions, needs and beliefs.

Emotion AI is a Theory of Mind AI currently in development. Researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.

Computer vision

Narrow AI applications with  computer vision  can be trained to interpret and analyze the visual world. This allows intelligent machines to identify and classify objects within images and video footage.

Applications of computer vision include:

• Image recognition and classification
• Object detection
• Object tracking
• Facial recognition
• Content-based image retrieval

Computer vision is critical for use cases that involve AI machines interacting and traversing the physical world around them. Examples include self-driving cars and machines navigating warehouses and other environments.

Robots in industrial settings can use Narrow AI to perform routine, repetitive tasks that involve materials handling, assembly and quality inspections. In healthcare, robots equipped with Narrow AI can assist surgeons in monitoring vitals and detecting potential issues during procedures.

Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. And smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress.

Expert systems

Expert systems equipped with Narrow AI capabilities can be trained on a corpus to emulate the human decision-making process and apply expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns to make decisions. They can also help businesses predict future events and understand why past events occurred.

IBM has pioneered AI from the very beginning, contributing breakthrough after breakthrough to the field. IBM most recently released a big upgrade to its cloud-based, generative AI platform known as watsonx.  IBM watsonx.ai  brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the entire AI lifecycle. With watsonx.ai, data scientists can build, train and deploy machine learning models in a single collaborative studio environment.

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Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

Get the huge list of more than 500 Essay Topics and Ideas

Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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• Artificial Intelligence Essay

Essay on Artificial Intelligence

Artificial Intelligence is the intelligence possessed by the machines under which they can perform various functions with human help. With the help of A.I, machines will be able to learn, solve problems, plan things, think, etc. Artificial Intelligence, for example, is the simulation of human intelligence by machines. In the field of technology, Artificial Intelligence is evolving rapidly day by day and it is believed that in the near future, artificial intelligence is going to change human life very drastically and will most probably end all the crises of the world by sorting out the major problems. 

Our life in this modern age depends largely on computers. It is almost impossible to think about life without computers. We need computers in everything that we use in our daily lives. So it becomes very important to make computers intelligent so that our lives become easy. Artificial Intelligence is the theory and development of computers, which imitates the human intelligence and senses, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has brought a revolution in the world of technology. 

Artificial Intelligence Applications

AI is widely used in the field of healthcare. Companies are attempting to develop technologies that will allow for rapid diagnosis. Artificial Intelligence would be able to operate on patients without the need for human oversight. Surgical procedures based on technology are already being performed.

Artificial Intelligence would save a lot of our time. The use of robots would decrease human labour. For example, in industries robots are used which have saved a lot of human effort and time. 

In the field of education, AI has the potential to be very effective. It can bring innovative ways of teaching students with the help of which students will be able to learn the concepts better. 

Artificial intelligence is the future of innovative technology as we can use it in many fields. For example, it can be used in the Military sector, Industrial sector, Automobiles, etc. In the coming years, we will be able to see more applications of AI as this technology is evolving day by day. 

Marketing: Artificial Intelligence provides a deep knowledge of consumers and potential clients to the marketers by enabling them to deliver information at the right time. Through AI solutions, the marketers can refine their campaigns and strategies.

Agriculture: AI technology can be used to detect diseases in plants, pests, and poor plant nutrition. With the help of AI, farmers can analyze the weather conditions, temperature, water usage, and condition of the soil.

Banking: Fraudulent activities can be detected through AI solutions. AI bots, digital payment advisers can create a high quality of service.

Health Care: Artificial Intelligence can surpass human cognition in the analysis, diagnosis, and complication of complicated medical data.

History of Artificial Intelligence

Artificial Intelligence may seem to be a new technology but if we do a bit of research, we will find that it has roots deep in the past. In Greek Mythology, it is said that the concepts of AI were used. 

The model of Artificial neurons was first brought forward in 1943 by Warren McCulloch and Walter Pits. After seven years, in 1950, a research paper related to AI was published by Alan Turing which was titled 'Computer Machinery and Intelligence. The term Artificial Intelligence was first coined in 1956 by John McCarthy, who is known as the father of Artificial Intelligence. 

To conclude, we can say that Artificial Intelligence will be the future of the world. As per the experts, we won't be able to separate ourselves from this technology as it would become an integral part of our lives shortly. AI would change the way we live in this world. This technology would prove to be revolutionary because it will change our lives for good. 

Branches of Artificial Intelligence:

Knowledge Engineering

Machines Learning

Natural Language Processing

Types of Artificial Intelligence

Artificial Intelligence is categorized in two types based on capabilities and functionalities. 

Artificial Intelligence Type-1

Artificial intelligence type-2.

Narrow AI (weak AI): This is designed to perform a specific task with intelligence. It is termed as weak AI because it cannot perform beyond its limitations. It is trained to do a specific task. Some examples of Narrow AI are facial recognition (Siri in Apple phones), speech, and image recognition. IBM’s Watson supercomputer, self-driving cars, playing chess, and solving equations are also some of the examples of weak AI.

General AI (AGI or strong AI): This system can perform nearly every cognitive task as efficiently as humans can do. The main characteristic of general AI is to make a system that can think like a human on its own. This is a long-term goal of many researchers to create such machines.

Super AI: Super AI is a type of intelligence of systems in which machines can surpass human intelligence and can perform any cognitive task better than humans. The main features of strong AI would be the ability to think, reason, solve puzzles, make judgments, plan and communicate on its own. The creation of strong AI might be the biggest revolution in human history.

Reactive Machines: These machines are the basic types of AI. Such AI systems focus only on current situations and react as per the best possible action. They do not store memories for future actions. IBM’s deep blue system and Google’s Alpha go are the examples of reactive machines.

Limited Memory: These machines can store data or past memories for a short period of time. Examples are self-driving cars. They can store information to navigate the road, speed, and distance of nearby cars.

Theory of Mind: These systems understand emotions, beliefs, and requirements like humans. These kinds of machines are still not invented and it’s a long-term goal for the researchers to create one. 

Self-Awareness: Self-awareness AI is the future of artificial intelligence. These machines can outsmart the humans. If these machines are invented then it can bring a revolution in human society. 

Artificial Intelligence will bring a huge revolution in the history of mankind. Human civilization will flourish by amplifying human intelligence with artificial intelligence, as long as we manage to keep the technology beneficial.

FAQs on Artificial Intelligence Essay

1. What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that emphasizes the development of intelligent machines that would think and work like humans.

2. How is Artificial Intelligence Categorised?

Artificial Intelligence is categorized in two types based on capabilities and functionalities. Based on capabilities, AI includes Narrow AI (weak AI), General AI, and super AI. Based on functionalities, AI includes Relative Machines, limited memory, theory of mind, self-awareness.

3. How Does AI Help in Marketing?

AI helps marketers to strategize their marketing campaigns and keep data of their prospective clients and consumers.

4. Give an Example of a Relative Machine?

IBM’s deep blue system and Google’s Alpha go are examples of reactive machines.

5. How can Artificial Intelligence help us?

Artificial Intelligence can help us in many ways. It is already helping us in some cases. For example, if we think about the robots used in a factory, they all run on the principle of Artificial Intelligence. In the automobile sector, some vehicles have been invented that don't need any humans to drive them, they are self-driving. The search engines these days are also AI-powered. There are many other uses of Artificial Intelligence as well.

The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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114 Artificial Intelligence Essay Topic Ideas & Examples

Inside This Article

Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize various aspects of our lives. From autonomous vehicles to virtual assistants, AI technologies are becoming increasingly prevalent. If you have been assigned an essay on artificial intelligence and are struggling to come up with a topic, look no further. Here are 114 AI essay topic ideas and examples to inspire your writing:

• The impact of AI on job automation: How will AI technologies reshape the workforce?
• The ethical implications of AI: Should there be limits on how AI can be used?
• The future of AI in healthcare: How can AI enhance medical diagnosis and treatment?
• The role of AI in education: How can AI technologies improve the learning experience?
• AI and privacy concerns: What are the risks associated with AI technologies and personal data?
• The use of AI in criminal justice: Can AI systems make fair and unbiased decisions?
• The potential dangers of superintelligent AI: Should we be concerned about AI surpassing human intelligence?
• AI and creativity: Can AI systems be creative in the same way humans are?
• The impact of AI on mental health: How can AI technologies assist in diagnosing and treating mental illnesses?
• The role of AI in climate change mitigation: How can AI help reduce carbon emissions?
• The future of transportation with AI: How will autonomous vehicles change the way we travel?
• AI and cybersecurity: Can AI technologies enhance our ability to detect and prevent cyber attacks?
• The impact of AI on social interactions: How will AI-powered virtual assistants affect human relationships?
• Bias in AI algorithms: How can we ensure fairness and impartiality in AI decision-making?
• The ethical implications of using AI in warfare: Should autonomous weapons be allowed?
• AI and the arts: How can AI technologies be used in creative fields such as music and painting?
• The role of AI in disaster response: How can AI help in predicting and managing natural disasters?
• The impact of AI on journalism: How will AI technologies influence news reporting and media?
• The use of AI in agriculture: How can AI optimize farming practices?
• AI and financial markets: How can AI algorithms be used for better investment decisions?
• The challenges of regulating AI: How can governments ensure safe and responsible development of AI technologies?
• AI and human rights: What are the potential threats to privacy and freedom posed by AI?
• The role of AI in space exploration: How can AI assist in exploring the universe?
• AI and language translation: How can AI technologies improve communication across different languages?
• The impact of AI on creativity: Will AI systems replace human creativity or enhance it?
• The use of AI in customer service: How can AI-powered chatbots improve customer experiences?
• AI and the future of work: How will AI technologies affect employment opportunities?
• The role of AI in personalized medicine: How can AI help tailor treatments for individual patients?
• AI and education inequality: How can AI technologies bridge the gap between privileged and underprivileged students?
• The impact of AI on the economy: Will AI lead to job creation or job displacement?
• AI and augmented reality: How can AI enhance the AR experience?
• The use of AI in sports: How can AI technologies optimize performance and training?
• AI and natural language processing: How can AI understand and generate human language?
• The impact of AI on the legal profession: Will AI replace lawyers in the future?
• The role of AI in combating fake news: How can AI technologies detect and prevent misinformation?
• AI and emotional intelligence: Can AI systems develop emotional intelligence?
• The use of AI in wildlife conservation: How can AI technologies help protect endangered species?
• AI and transportation infrastructure: How can AI improve traffic management and reduce congestion?
• The impact of AI on the entertainment industry: How will AI technologies shape the future of movies and gaming?
• AI and personalized advertising: How can AI algorithms target ads to individual preferences?
• The role of AI in disaster recovery: How can AI assist in rebuilding after natural disasters?
• AI and mental well-being: Can AI technologies provide therapy and support for mental health?
• The use of AI in social media: How can AI detect and prevent harmful content?
• AI and the future of energy: How can AI optimize energy consumption and production?
• The impact of AI on democracy: What are the implications of AI for political systems?
• AI and robotics: How can AI enhance the capabilities and interactions of robots?
• AI and the aging population: How can AI technologies improve the quality of life for elderly individuals?
• The use of AI in retail: How can AI technologies personalize the shopping experience?
• AI and virtual reality: How can AI enhance the VR experience?
• The impact of AI on creativity in the workplace: Will AI systems replace or empower human creativity?
• AI and autonomous drones: What are the potential applications and risks?
• The role of AI in social justice: How can AI technologies address systemic biases and discrimination?
• AI and disaster prediction: How can AI assist in predicting natural disasters?
• The use of AI in architecture and design: How can AI technologies optimize building design?
• AI and sustainable development: How can AI help achieve environmental and social sustainability?
• The impact of AI on the music industry: How will AI technologies shape music production and consumption?
• AI and the future of democracy: Can AI improve citizen engagement and participation?
• The role of AI in personalized learning: How can AI technologies adapt educational content to individual students?
• AI and autonomous robots in healthcare: What are the benefits and risks?
• The use of AI in supply chain management: How can AI optimize logistics and inventory management?
• AI and emotional recognition: How can AI systems understand and respond to human emotions?
• The impact of AI on urban planning: How can AI technologies create smarter and more sustainable cities?
• AI and cybersecurity threats: How can AI be used to detect and prevent cyber attacks?
• The role of AI in personalized news curation: How can AI algorithms tailor news articles to individual interests?
• AI and personalized fashion: How can AI technologies help consumers find their unique style?
• The impact of AI on social inequality: Will AI exacerbate or alleviate existing inequalities?
• AI and decision-making: Can AI systems make better decisions than humans?
• The use of AI in cultural preservation: How can AI technologies help protect and restore cultural heritage?
• AI and the future of transportation infrastructure: How can AI technologies improve roads, bridges, and public transportation?
• The role of AI in early detection of diseases: How can AI assist in diagnosing illnesses at an early stage?
• AI and personalized entertainment: How can AI technologies tailor movies, music, and games to individual preferences?
• The impact of AI on customer behavior analysis: How can AI algorithms predict and influence consumer choices?
• AI and the future of democracy: How can AI technologies promote transparency and accountability in governance?
• The use of AI in disaster relief: How can AI assist in coordinating rescue and aid efforts?
• AI and sustainable agriculture: How can AI technologies optimize farming practices while minimizing environmental impact?
• The role of AI in personalized marketing: How can AI algorithms target advertisements to individual preferences?
• AI and the future of privacy: How can AI technologies protect personal data in an increasingly connected world?
• The impact of AI on creative industries: Will AI systems replace or collaborate with human artists?
• AI and autonomous ships: What are the potential benefits and challenges?
• The use of AI in wildlife monitoring: How can AI technologies help track and protect endangered species?
• AI and the future of cybersecurity: How can AI technologies stay ahead of evolving cyber threats?
• The role of AI in personalized fitness: How can AI technologies optimize exercise routines and nutrition plans?
• AI and personalized travel recommendations: How can AI algorithms suggest tailored itineraries to individual travelers?
• The impact of AI on income inequality: Will AI exacerbate or reduce economic disparities?
• AI and the future of journalism: How can AI technologies assist in news reporting and fact-checking?
• The use of AI in waste management: How can AI technologies optimize recycling and waste disposal?
• AI and autonomous farming: How can AI technologies improve crop yield and reduce resource consumption?
• The role of AI in personalized financial advice: How can AI algorithms help individuals make better financial decisions?
• AI and the future of privacy: How can AI technologies protect personal information in the age of big data?
• The impact of AI on the film industry: How will AI technologies influence movie production and special effects?
• AI and autonomous construction: What are the potential applications and challenges?
• The use of AI in marine conservation: How can AI technologies help protect marine ecosystems?
• AI and the future of transportation logistics: How can AI optimize the movement of goods and reduce carbon emissions?
• The role of AI in personalized healthcare: How can AI technologies tailor treatments to individual patients?
• AI and personalized gaming: How can AI algorithms create unique gaming experiences for individual players?
• The impact of AI on voting systems: Can AI technologies improve the accuracy and security of elections?
• AI and sustainable urban planning: How can AI technologies create greener and more livable cities?
• The use of AI in personalized nutrition: How can AI algorithms optimize diets for individual health goals?
• AI and the future of privacy: How can AI technologies balance the benefits of data analysis with privacy concerns?
• The role of AI in personalized advertising: How can AI algorithms target ads to individual preferences without invading privacy?
• AI and autonomous underwater vehicles: What are the potential applications and challenges?
• The impact of AI on wildlife conservation: How will AI technologies enhance conservation efforts?
• AI and the future of transportation safety: How can AI technologies prevent accidents and improve road conditions?
• The use of AI in personalized fashion design: How can AI algorithms create customized clothing?
• AI and sustainable energy management: How can AI technologies optimize energy usage in homes and buildings?
• The role of AI in personalized learning platforms: How can AI technologies adapt educational content to individual students' needs?
• AI and the future of privacy: How can AI technologies protect personal information from unauthorized access?
• The impact of AI on the gaming industry: How will AI technologies enhance gameplay and virtual worlds?
• AI and autonomous construction robots: What are the potential benefits and risks?
• The use of AI in personalized travel planning: How can AI algorithms create tailored itineraries based on individual preferences?
• AI and sustainable transportation: How can AI technologies optimize public transportation and reduce carbon emissions?
• The role of AI in personalized mental health support: How can AI technologies provide therapy and counseling?
• AI and personalized music creation: How can AI algorithms compose music based on individual preferences?
• The impact of AI on social media manipulation: Can AI technologies detect and prevent the spread of fake news and misinformation?

These 114 artificial intelligence essay topic ideas and examples cover a wide range of areas where AI technologies can make a significant impact. Whether you're interested in the ethical implications of AI or its potential applications in various industries, there is a topic here for you. Choose one that sparks your curiosity and start writing an insightful and engaging essay on artificial intelligence.

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'ZDNET Recommends': What exactly does it mean?

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ZDNET's editorial team writes on behalf of you, our reader. Our goal is to deliver the most accurate information and the most knowledgeable advice possible in order to help you make smarter buying decisions on tech gear and a wide array of products and services. Our editors thoroughly review and fact-check every article to ensure that our content meets the highest standards. If we have made an error or published misleading information, we will correct or clarify the article. If you see inaccuracies in our content, please report the mistake via this form .

What is AI? Everything to know about artificial intelligence

What is artificial intelligence?

If you hear the term artificial intelligence (AI), you might think of self-driving cars , robots , ChatGPT ,  other AI chatbots , and artificially created images . But it's also important to look behind the outputs of AI and understand how the technology works and its impacts on this and future generations.

AI is a concept that has been around formally  since the 1950s  when it was defined as a machine's ability to perform a task that would've previously required human intelligence. This is quite a broad definition that has been modified over decades of research and technological advancements.

When you consider assigning intelligence to a machine, such as a computer, it makes sense to start by defining the term 'intelligence' -- especially when you want to determine if an artificial system truly deserves it. 

Also: ChatGPT vs. Microsoft Copilot vs. Gemini: Which is the best AI chatbot?

Our level of intelligence sets us apart from other living beings and is essential to the human experience. Some experts define intelligence as the ability to adapt, solve problems, plan, improvise in new situations, and learn new things. 

With intelligence sometimes seen as the foundation for being human, it's perhaps no surprise that we'd try and recreate it artificially in scientific endeavors. 

Today's AI systems might demonstrate some traits of human intelligence, including learning, problem-solving, perception, and even a limited spectrum of creativity and social intelligence.

How can I use AI?

AI comes in different forms and has become widely available in everyday life. The smart speakers on your mantle with Alexa or Google voice assistant built-in are two great examples of AI. Other good examples include popular AI chatbots, such as  ChatGPT , the new Bing Chat , and Google Bard . 

When you ask ChatGPT for the capital of a country, or you ask Alexa to give you an update on the weather, the responses come from machine-learning algorithms.

Also:  How does ChatGPT work?

Though these systems aren't a replacement for human intelligence or social interaction, they can use their training to adapt and learn new skills for tasks they weren't explicitly programmed to perform. 

What are the different types of AI?

Artificial intelligence can be divided into three widely accepted subcategories: narrow AI, general AI, and super AI.

What is narrow AI?

Artificial narrow intelligence (ANI) is crucial to voice assistants like Siri, Alexa, and Google Assistant. This category includes intelligent systems designed or trained to carry out specific tasks or solve particular problems without being explicitly designed. 

ANI might often be called weak AI, as it doesn't possess general intelligence. Still, some examples of the power of narrow AI include voice assistants, image-recognition systems, technologies that respond to simple customer service requests, and tools that flag inappropriate content online. 

Also: Microsoft Copilot Pro vs. OpenAI's ChatGPT Plus: What is $20 a month worth?

ChatGPT is an example of ANI, as it is programmed to perform a specific task: generate text responses to prompts it's given.

What is general AI?

Artificial general intelligence (AGI), or strong AI, is still a hypothetical concept as it involves a machine understanding and performing vastly different tasks based on accumulated experience. This type of intelligence is more on the level of human intellect, as AGI systems would be able to reason and think like a human.

Also: AI's true goal may no longer be intelligence

Like a human, AGI could potentially understand any intellectual task, think abstractly, learn from its experiences, and use that knowledge to solve new problems. Essentially, we're talking about a system or machine capable of common sense, which is currently unachievable with any available AI.

Developing a system with consciousness is still, presumably, a fair way in the distance, but it is the ultimate goal of AI research.

What is super AI?

Artificial superintelligence (ASI) is a system that wouldn't only rock humankind to its core but could also destroy it. If that sounds like something straight out of a science fiction novel, it's because it kind of is. ASI is a system where the intelligence of a machine surpasses all forms of human intelligence in all aspects and outperforms humans in every function.

Also: Mechanics of the future: Meet the specialists assembling AI

An intelligent system that can learn and continuously improve itself is still a hypothetical concept. However, if applied effectively and ethically, the system could lead to extraordinary progress and achievements in medicine, technology, and more. 

What are some recent examples of AI?

Overall, the most notable advancements in AI are the development and release of GPT 3.5 and GPT 4. But there have been many other revolutionary achievements in artificial intelligence -- too many to include here.

Here are some of the most notable:

ChatGPT (and the GPTs)

ChatGPT is an AI chatbot capable of generating and translating natural language and answering questions. Though it's arguably the most popular AI tool, thanks to its widespread accessibility, OpenAI made significant waves in artificial intelligence by creating GPTs 1, 2, and 3 before releasing ChatGPT.

Also:  5 ways to use chatbots to make your life easier

GPT stands for Generative Pre-trained Transformer, and GPT-3 was the largest language model at its 2020 launch, with 175 billion parameters. Then came GPT-3.5, which powers the free tier of ChatGPT. The largest version, GPT-4, accessible through ChatGPT Plus or Microsoft Copilot, has one trillion parameters. 

Self-driving cars

Though the safety of self-driving cars is a top concern of potential users , the technology continues to advance and improve with breakthroughs in AI. These vehicles use machine-learning algorithms to combine data from sensors and cameras to perceive their surroundings and determine the best course of action. 

Also: An autonomous car that wakes up and greets you could be in your future

Tesla's autopilot feature in its electric vehicles is probably what most people think of when considering self-driving cars. Still, Waymo, from Google's parent company, Alphabet, makes autonomous rides, like a taxi without a taxi driver, in San Francisco, CA, and Phoenix, AZ.

Cruise is another robotaxi service, and auto companies like Audi, GM, and Ford are also presumably working on self-driving vehicle technology. 

The achievements of Boston Dynamics stand out in the area of AI and robotics. Though we're still a long way away from creating AI at the level of technology seen in the movie Terminator, watching Boston Dyanmics' robots use AI to navigate and respond to different terrains is impressive. 

Google's sister company  DeepMind  is an AI pioneer making strides toward the ultimate goal of artificial general intelligence (AGI). Though not there yet, the company initially made headlines in 2016 with AlphaGo, a system that beat a human professional Go player. 

Since then, DeepMind has created a protein-folding prediction system that can predict the complex 3D shapes of proteins. It has also developed programs to diagnose eye diseases as effectively as the top doctors worldwide.

What is machine learning?

The biggest quality that sets AI aside from other computer science topics is the ability to easily automate tasks by employing machine learning, which lets computers learn from different experiences rather than being explicitly programmed to perform each task. This capability is what many refer to as AI, but machine learning is a subset of artificial intelligence.

Machine learning involves a system being trained on large amounts of data to learn from mistakes and recognize patterns to accurately make predictions and decisions, whether they've been exposed to the specific data. 

Also: What is machine learning? Everything you need to know

Examples of machine learning include image and speech recognition, fraud protection, and more. One specific example is the image recognition system when users upload photos to Facebook. The social media network can analyze the image and recognize faces, which leads to recommendations to tag different friends. With time and practice, the system hones this skill and learns to make more accurate recommendations.

What are the elements of machine learning?

As mentioned above, machine learning is a subset of AI and is generally split into two main categories: supervised and unsupervised learning.

Supervised learning

This common technique for teaching AI systems uses many labeled examples that people have categorized. These machine-learning systems are fed huge amounts of data, which has been annotated to highlight the features of interest -- you're essentially teaching by example. 

Suppose you wanted to train a machine-learning model to recognize and differentiate images of circles and squares. In that case, you'd get started by gathering a large dataset of images of circles and squares in different contexts, such as a drawing of a planet for a circle or a table for a square, for example, complete with labels for what each shape is. 

The algorithm would then learn this labeled collection of images to distinguish the shapes and their characteristics, such as circles with no corners and squares with four equal sides. After training on the dataset of images, the system can see a new image and determine what shape it finds. 

Unsupervised learning

In contrast, unsupervised learning uses a different approach, where algorithms try to identify patterns in data, looking for similarities that can be used to categorize that data.

An example might be clustering together fruits that weigh a similar amount or cars with a similar engine size.

Also: Machine learning is going real-time: Here's why and how

The algorithm isn't set up in advance to pick out specific types of data; it simply looks for data with similarities that it can group, for example, grouping customers based on shopping behavior to target them with personalized marketing campaigns. 

Reinforcement learning

In reinforcement learning, the system attempts to maximize a reward based on input data, going through a trial-and-error process until it arrives at the best possible outcome.

Consider training a system to play a video game, where it can receive a positive reward if it gets a higher score and a negative reward for a low score. The system learns to analyze the game and make moves and then learns solely from the rewards it receives, reaching the point of playing on its own, and earning a high score without human intervention.

Reinforcement learning is also used in research, where it can help teach autonomous robots the optimal way to behave in real-world environments.

What are large language models?

One of the most renowned types of AI right now is large language models (LLM). These models use unsupervised machine learning and are trained on massive amounts of text to learn how human language works. These texts include articles, books, websites, and more. 

In the training process, LLMs process billions of words and phrases to learn patterns and relationships between them, enabling the models to generate human-like answers to prompts. 

Also: AI will unleash the next level of human potential. Here's how

The most popular LLM is GPT 3.5, on which the free ChatGPT is based, and the largest LLM is GPT-4 at supposedly 1.78 trillion parameters. Gemini is powered by an LLM of the same name developed by Google, which is the second-largest LLM at 1.5 million parameters.

What is deep learning?

Deep learning is part of the machine-learning family, which involves training artificial neural networks with three or more layers to perform different tasks. These neural networks are expanded into sprawling networks with a large number of deep layers that are trained using massive amounts of data. 

Deep-learning models tend to have more than three layers and can have hundreds of layers. Deep learning can use supervised or unsupervised learning or both in training processes.

Also: What is deep learning? Everything you need to know

Because deep-learning technology can learn to recognize complex patterns in data using AI, it is often used in natural language processing (NLP), speech recognition, and image recognition.

What are neural networks?

The success of machine learning relies on neural networks. These are mathematical models whose structure and functioning are loosely based on the connection between neurons in the human brain, mimicking how they signal to one another.

Imagine a group of robots that are working together to solve a puzzle. Each is programmed to recognize a different shape or color in the puzzle pieces. The robots combine their abilities to solve the puzzle together. A neural network is like a group of robots.

Neural networks can tweak internal parameters to change what they output. Each is fed databases to learn what it should put out when presented with certain data during training. 

Also: Six skills you need to become an AI prompt engineer

They comprise interconnected layers of algorithms that feed data into each other. Neural networks can be trained to perform specific tasks by modifying the importance attributed to data as it passes between layers. During the training of these neural networks, the weights attached to data as it passes between layers will continue to be varied until the output from the neural network is very close to what is desired. 

At that point, the network will have 'learned' how to carry out a particular task. The desired output could be anything from correctly labeling fruit in an image to predicting when an elevator might fail based on its sensor data.

What is conversational AI?

Conversational AI includes systems programmed to have conversations with a user, trained to listen (input) and respond (output) in a conversational manner. Conversational AI uses natural language processing to understand and respond naturally.

Also: Why conversational AI is now ready for prime time

Some examples of conversational AI are chatbots like Gemini, smart speakers with a voice assistant like Amazon Alexa, or virtual assistants on your smartphone like Siri. 

Which AI services are available to use?

Consumers and businesses alike have a wealth of AI services available to expedite tasks and add convenience to day-to-day life -- you probably have something in your home that uses AI in some capacity.

Here are some common examples of artificial intelligence available to the public, both free and for a fee:

• Voice assistants: Amazon Alexa on the Echo device on your shelf, Apple's Siri on your iPhone, and Google Assistant on a Pixel device all use natural language processing to understand and respond to your questions or commands.
• Chatbots: AI chatbots are another form of virtual assistant that can interact with people and, in some cases, hold human-like conversations, even mimicking empathy and concern. 
• Language translation: Machine learning reaches far and wide, and services like Google Translate, Microsoft Translator, Amazon Translate, and ChatGPT all use the technology to translate text.
• Productivity: Microsoft Copilot for Microsoft 365  is a great example of an LLM used as an AI productivity tool embedded within Word, PowerPoint, Outlook, Excel, Teams, and more to automate tasks. Simply asking, 'Email the team about the latest status on the project' will trigger Copilot to automatically gather information from emails and documents to generate a text with what you asked.
• Image and video recognition: Different programs use AI to find information about the content in images and videos, such as the faces, text, and objects within them. Clarifai, which employs machine learning to organize unstructured data from sources, and Amazon Rekognition , an AWS service that lets users upload images to receive information, are two examples of this.
• Software development: Many developers have been using ChatGPT to write and debug code for over a year, but many other AI tools are available to make a programmer's job easier. One example is the AI pair programmer GitHub Copilot by OpenAI Codex, a generative language model that can write code faster with less effort by autocompleting comments and code instantly.
• Building a business: Aside from an everyday user availing themselves of artificial intelligence around them, services are offering AI tools for businesses, including OpenAI's GPT-4 API  to build applications and services using the LLM or Amazon Bedrock , a suite of cloud-based AI tools for developers.

What company is leading the AI race?

Though generative AI leads the artificial intelligence breakthroughs , other top companies are working on pioneering technologies.

It's not surprising that OpenAI has taken the lead in the AI race after making generative AI tools available for free, such as the AI chatbot ChatGPT and Dall-E 3, which is an image generator.

Google's parent company, Alphabet, has its hands in several different AI systems through companies, including DeepMind, Waymo, and the aforementioned Google. 

Also:  Is AI in software engineering reaching an 'Oppenheimer moment'? Here's what you need to know

Google had a rough start in the AI chatbot race with an underperforming tool called Google Bard, originally powered by LaMDA. The company then switched the LLM behind Bard twice -- the first time for PaLM 2, and then for Gemini, the LLM currently powering it. With the last change, Google also renamed the bot Bard for Gemini.

DeepMind continues to pursue artificial general intelligence, as evidenced by the scientific solutions it strives to achieve through AI systems. It's developed machine-learning models for Document AI, optimized the viewer experience on Youtube, made AlphaFold available for researchers worldwide, and more.

Also: Have 10 hours? IBM will train you in AI fundamentals - for free

Though you may not hear of Alphabet's artificial intelligence endeavors in the news every day, its works in deep learning and AI in general have the potential to change the future for human beings. 

Aside from creating Microsoft Copilot for its 365 applications, Microsoft provides a suite of AI tools for developers on Azure , such as platforms for developing machine learning, data analytics, conversational AI, and customizable APIs that achieve human parity in computer vision, speech, and language.

Also:  Microsoft CEO Nadella: 'Expect us to incorporate AI in every layer of the stack'

Microsoft has also invested heavily in OpenAI's development. The tech giant uses GPT-4 in Copilot, its AI chatbot formerly known as Bing chat , and in a more advanced version of Dall-E 3 to generate images through  Microsoft Designer .

Other companies

These are just a few examples of companies leading the AI race but others worldwide are also making strides into artificial intelligence, including  Baidu , Alibaba , Cruise , Lenovo , Tesla , and more.

How will AI change the world?

Artificial intelligence can change how we work, our health, how we consume media and get to work, our privacy, and more. 

Consider the impact that certain AI systems can have on the world. People can ask a voice assistant on their phones to hail rides from autonomous cars to get them to work, where they can use AI tools to be more efficient than ever before.

Also: The ethics of generative AI: How we can harness this powerful technology

Doctors and radiologists could make cancer diagnoses using fewer resources, spot genetic sequences related to diseases, and identify molecules that could lead to more effective medications, potentially saving countless lives.

Alternatively, it's worth considering the disruption that could result from having neural networks that can create realistic images, such as Dall-E 3, Midjourney, and Copilot, that can replicate someone's voice or create deepfake videos using a person's resemblance. These deepfakes could undermine the photos, videos, or audio people consider genuine.

Also:  Why your ChatGPT conversations may not be as secure as you think

Another ethical issue concerns facial recognition and surveillance, and how this technology could intrude on people's privacy, with many experts looking to ban it altogether.

Will an AI steal your job?

The possibility of artificially intelligent systems replacing a considerable chunk of modern labor is a credible near-future possibility.

While commonplace artificial intelligence won't replace all jobs, what seems certain is that AI will change the nature of work, with the only question being how rapidly and profoundly automation will alter the workplace.

Also: These are the jobs most likely to be taken over by AI

However, artificial intelligence can't run independently. While many jobs with routine, repetitive data work might be automated, workers in other  jobs can  use tools like generative AI to become more productive and efficient.

There's a broad range of opinions among AI experts about how quickly artificially intelligent systems will surpass human capabilities.

Also:  Can AI be a team player in collaborative software development?

Fully autonomous self-driving vehicles aren't a reality yet, but  by some predictions , the self-driving trucking industry alone is poised to take over 500,000 jobs in the US inevitably, even without considering the impact on couriers and taxi drivers. 

Artificial Intelligence

How i test an ai chatbot's coding ability - and you can too, virtue, intellect, and trust: how chatgpt beat humans 3-0 in a moral turing test, how chatgpt (and other ai chatbots) can help you write an essay.

How to Write an Essay Using AI

Artificial intelligence has progressed to the point where AI tools can now assist with writing everything from essays and articles to books. AI writing assistants act as an editor, wordsmith or even creator to help students, academics, bloggers and authors produce better quality content with greater efficiency.

In this comprehensive guide, we’ll overview everything you need to know to get started writing essays with AI including:

• How AI essay writing assistants work
• The top apps and sites providing AI writing help
• What types of essay writing they can help with
• Templates and examples for getting started
• Capabilities and limitations to be aware of
• Tips to incorporate AI writing effectively in your workflow

Let’s dive in to harnessing this transformative technology for your next essay!

How Does AI Help With Writing Essays?

AI applications approach essay writing assistance in a few key ways:

• Brainstorming - AI can provide relevant ideas, concepts, perspectives and arguments around a topic to include as evidence.
• Outlining - The best tools create organized essay outlines ensuring logical flow and structure.
• Drafting - Apps can generate complete multi-paragraph drafts on a topic which writers can then edit.
• Editing - Most offer grammar, spelling, style and even depth of analysis checks to improve drafts.
• Paraphrasing - This helps rewrite existing text or source information in your own words.
• Citing sources - Some apps automatically cite sources and generate bibliographies properly formatted.

The level of actual writing versus enhancing human writing varies across solutions, so research thoroughly to find your best match.

Top 5 AI Essay Writing Assistants

After evaluating dozens of writing apps, these 5 provide the best results for essay creation specifically:

Just Think AI

An AI chat with A Human Touch . Enhance your productivity with Just Think Chat, a user-friendly AI chatbot. Overcome writers block, generate ideas, revise content, or get answer effortlessly.

Article Forge

Article Forge has a strong academic focus, with an Essay mode specifically for crafting logical arguments and persuasive essays filled with cited facts. Ideal for research paper help.

Rytr boasts specialized intelligence for writing clear, compelling arguments in blogs, essays and more. It asks probing questions then incorporates your answers into drafts.

INK Associate

INK Associate enhances your writing productivity by suggesting ways to improve clarity, concision and comprehension. Great for proofreading and editing.

GPT-3 Essay Writer

Tools harnessing OpenAI's GPT-3 like Essay Writer create remarkably human-like essays. Requires more guidance to stay on-topic.

Essay Types AI Can Assist With

Some apps are specialized for specific types of essays required in academia and standardized testing. Here are key genres supported:

Argumentative Essays

Arguing a position with logic and evidence cited. Tools help craft sound premises, counterarguments and impactful conclusions.

Persuasive Essays

Similar to argumentative but specifically aimed at swaying the reader regarding policies, interpretations, theories or actions.

Compare and Contrast

Analyzing the similarities and differences between concepts, works or phenomena. Tools ensure clear links are made.

Research Papers

Long form essays synthesizing research around a thesis. AI helps format like academic papers with citations and bibliographies.

Narrative Essays

Telling an entertaining or impactful personal story. Apps assist with structures that emotionally resonate with readers.

Scholarship Essays

Presenting your background, accomplishments and goals in the best possible light. AI ensures relevance to prompt.

Essay Writing Templates and Examples

When first utilizing an AI writing assistant, it's helpful to have templates and examples to base your prompt off.

Here are prompts for common academic essay types you can customize:

Argumentative Essay

Topic: {Topic to argue}

Length: {Word count}

Words Style: Academic argumentative essay

Arguments for: {2-3 strongest arguments}

Arguments against: {The main counterargument}

Conclusion: {Your position} is the better policy because {reasons}.

For example:

Topic: All students should be required to learn coding Length: 800 words Style: Academic argumentative essay Arguments for: Coding teaches logical thinking, prepares for most careers, provides problem solving skills Arguments against: Adds additional schoolwork burden, benefits small subset of students   Conclusion: Mandatory coding classes are the better policy because the skills help nearly all future career paths.

Persuasive Essay

Audience: {Teacher, peer students, university alumni, etc} Thesis: Convince the audience that {position, interpretation, action} is {superior, correct, moral, just, etc} because {2-3 strongest reasons}

Supporting evidence: {facts, expert opinions, personal anecdotes} Counterarguments to address: {The main opposition position}

Style: {Formal or casual} persuasive essay

Audience: School principal Thesis: Convince the principal that allowing cell phones in class is the better policy because it enables learning tech skills, accessing information, and improved safety. Supporting evidence: Studies show benefits, students can film dangerous behaviors Counterarguments: It enables distractions, cheating and unwanted sharing Style: Semi-formal persuasive essay

Capabilities and Limitations of AI Essay Help

Understanding what essay writing AI can and cannot yet do will lead to the most successful experience:

Capabilities:

• Analyze prompts and determine optimal content and structure
• Generate complete drafts with readable flow and style
• Check spelling, grammar, and catch insensitive phrasing
• Format citations and bibliographies for academic integrity
• Develop arguments backed by evidence and reasoning

Limitations:

• Cannot deeply understand nuanced contexts and sentiments
• Struggles evaluating logical consistencies and factual accuracy
• Limited ability checking overall coherence and impact
• Does not build long-term memory, concepts and wisdom
• Cannot object in any way or cease participation

Use AI as a production assistant rather than independent author until the technology matures further in key reasoning areas in the coming years.

Tips for Using AI Effectively To Write Essays

To leverage these amazing tools most effectively and ethically, we recommend:

• Curate source materials - Feed the AI relevant, factual sources representing diverse perspectives.
• Outline the draft first - Guide arguments and structure before having AI compose full paragraphs and sentences.
• Check accuracy - Ensure factual truth and logical reasoning in arguments made.
• Maintain authorship - Edit, rearrange and rewrite elements in your own voice and style.
• Let the tool inspire new directions and arguments you may not have considered.
• Enjoy the productivity boost these tools offer our constrained time and minds!

We hope this guide has been helpful in getting started with AI assistants for leveling up your essay writing across school, work and passion projects!

The brief history of artificial intelligence: the world has changed fast — what might be next?

Despite their brief history, computers and ai have fundamentally changed what we see, what we know, and what we do. little is as important for the world’s future and our own lives as how this history continues..

To see what the future might look like, it is often helpful to study our history. This is what I will do in this article. I retrace the brief history of computers and artificial intelligence to see what we can expect for the future.

How did we get here?

How rapidly the world has changed becomes clear by how even quite recent computer technology feels ancient today. Mobile phones in the ‘90s were big bricks with tiny green displays. Two decades before that, the main storage for computers was punch cards.

In a short period, computers evolved so quickly and became such an integral part of our daily lives that it is easy to forget how recent this technology is. The first digital computers were only invented about eight decades ago, as the timeline shows.

Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial intelligence (AI) systems and describes what they were capable of.

The first system I mention is the Theseus. It was built by Claude Shannon in 1950 and was a remote-controlled mouse that was able to find its way out of a labyrinth and could remember its course. 1 In seven decades, the abilities of artificial intelligence have come a long way.

The language and image recognition capabilities of AI systems have developed very rapidly

The chart shows how we got here by zooming into the last two decades of AI development. The plotted data stems from a number of tests in which human and AI performance were evaluated in different domains, from handwriting recognition to language understanding.

Within each of the domains, the initial performance of the AI system is set to –100, and human performance in these tests is used as a baseline set to zero. This means that when the model’s performance crosses the zero line is when the AI system scored more points in the relevant test than the humans who did the same test. 2

Just 10 years ago, no machine could reliably provide language or image recognition at a human level. But, as the chart shows, AI systems have become steadily more capable and are now beating humans in tests in all these domains. 3

Outside of these standardized tests, the performance of these AIs is mixed. In some real-world cases, these systems are still performing much worse than humans. On the other hand, some implementations of such AI systems are already so cheap that they are available on the phone in your pocket: image recognition categorizes your photos and speech recognition transcribes what you dictate.

From image recognition to image generation

The previous chart showed the rapid advances in the perceptive abilities of artificial intelligence. AI systems have also become much more capable of generating images.

This series of nine images shows the development over the last nine years. None of the people in these images exist; all were generated by an AI system.

The series begins with an image from 2014 in the top left, a primitive image of a pixelated face in black and white. As the first image in the second row shows, just three years later, AI systems were already able to generate images that were hard to differentiate from a photograph.

In recent years, the capability of AI systems has become much more impressive still. While the early systems focused on generating images of faces, these newer models broadened their capabilities to text-to-image generation based on almost any prompt. The image in the bottom right shows that even the most challenging prompts — such as “A Pomeranian is sitting on the King’s throne wearing a crown. Two tiger soldiers are standing next to the throne” — are turned into photorealistic images within seconds. 5

Timeline of images generated by artificial intelligence 4

Language recognition and production is developing fast

Just as striking as the advances of image-generating AIs is the rapid development of systems that parse and respond to human language.

The image shows examples of an AI system developed by Google called PaLM. In these six examples, the system was asked to explain six different jokes. I find the explanation in the bottom right particularly remarkable: the AI explains an anti-joke specifically meant to confuse the listener.

AIs that produce language have entered our world in many ways over the last few years. Emails get auto-completed, massive amounts of online texts get translated, videos get automatically transcribed, school children use language models to do their homework, reports get auto-generated, and media outlets publish AI-generated journalism.

AI systems are not yet able to produce long, coherent texts. In the future, we will see whether the recent developments will slow down — or even end — or whether we will one day read a bestselling novel written by an AI.

Output of the AI system PaLM after being asked to interpret six different jokes 6

Where we are now: AI is here

These rapid advances in AI capabilities have made it possible to use machines in a wide range of new domains:

When you book a flight, it is often an artificial intelligence, no longer a human, that decides what you pay. When you get to the airport, it is an AI system that monitors what you do at the airport. And once you are on the plane, an AI system assists the pilot in flying you to your destination.

AI systems also increasingly determine whether you get a loan , are eligible for welfare, or get hired for a particular job. Increasingly, they help determine who is released from jail .

Several governments have purchased autonomous weapons systems for warfare, and some use AI systems for surveillance and oppression .

AI systems help to program the software you use and translate the texts you read. Virtual assistants , operated by speech recognition, have entered many households over the last decade. Now self-driving cars are becoming a reality.

In the last few years, AI systems have helped to make progress on some of the hardest problems in science.

Large AIs called recommender systems determine what you see on social media, which products are shown to you in online shops, and what gets recommended to you on YouTube. Increasingly they are not just recommending the media we consume, but based on their capacity to generate images and texts, they are also creating the media we consume.

Artificial intelligence is no longer a technology of the future; AI is here, and much of what is reality now would have looked like sci-fi just recently. It is a technology that already impacts all of us, and the list above includes just a few of its many applications .

The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals — and some extraordinarily bad ones, too. For such “dual-use technologies”, it is important that all of us develop an understanding of what is happening and how we want the technology to be used.

Just two decades ago, the world was very different. What might AI technology be capable of in the future?

What is next?

The AI systems that we just considered are the result of decades of steady advances in AI technology.

The big chart below brings this history over the last eight decades into perspective. It is based on the dataset produced by Jaime Sevilla and colleagues. 7

The rise of artificial intelligence over the last 8 decades: As training computation has increased, AI systems have become more powerful 8

Each small circle in this chart represents one AI system. The circle’s position on the horizontal axis indicates when the AI system was built, and its position on the vertical axis shows the amount of computation used to train the particular AI system.

Training computation is measured in floating point operations , or FLOP for short. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers.

All AI systems that rely on machine learning need to be trained, and in these systems, training computation is one of the three fundamental factors that are driving the capabilities of the system. The other two factors are the algorithms and the input data used for the training. The visualization shows that as training computation has increased, AI systems have become more and more powerful.

The timeline goes back to the 1940s when electronic computers were first invented. The first shown AI system is ‘Theseus’, Claude Shannon’s robotic mouse from 1950 that I mentioned at the beginning. Towards the other end of the timeline, you find AI systems like DALL-E and PaLM; we just discussed their abilities to produce photorealistic images and interpret and generate language. They are among the AI systems that used the largest amount of training computation to date.

The training computation is plotted on a logarithmic scale so that from each grid line to the next, it shows a 100-fold increase. This long-run perspective shows a continuous increase. For the first six decades, training computation increased in line with Moore’s Law , doubling roughly every 20 months. Since about 2010, this exponential growth has sped up further, to a doubling time of just about 6 months. That is an astonishingly fast rate of growth. 9

The fast doubling times have accrued to large increases. PaLM’s training computation was 2.5 billion petaFLOP, more than 5 million times larger than AlexNet, the AI with the largest training computation just 10 years earlier. 10

Scale-up was already exponential and has sped up substantially over the past decade. What can we learn from this historical development for the future of AI?

Studying the long-run trends to predict the future of AI

AI researchers study these long-term trends to see what is possible in the future. 11

Perhaps the most widely discussed study of this kind was published by AI researcher Ajeya Cotra. She studied the increase in training computation to ask at what point the computation to train an AI system could match that of the human brain. The idea is that, at this point, the AI system would match the capabilities of a human brain. In her latest update, Cotra estimated a 50% probability that such “transformative AI” will be developed by the year 2040, less than two decades from now. 12

In a related article , I discuss what transformative AI would mean for the world. In short, the idea is that such an AI system would be powerful enough to bring the world into a ‘qualitatively different future’. It could lead to a change at the scale of the two earlier major transformations in human history, the agricultural and industrial revolutions. It would certainly represent the most important global change in our lifetimes.

Cotra’s work is particularly relevant in this context as she based her forecast on the kind of historical long-run trend of training computation that we just studied. But it is worth noting that other forecasters who rely on different considerations arrive at broadly similar conclusions. As I show in my article on AI timelines , many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

Building a public resource to enable the necessary public conversation

Computers and artificial intelligence have changed our world immensely, but we are still in the early stages of this history. Because this technology feels so familiar, it is easy to forget that all of these technologies we interact with are very recent innovations and that the most profound changes are yet to come.

Artificial intelligence has already changed what we see, what we know, and what we do. This is despite the fact that this technology has had only a brief history.

There are no signs that these trends are hitting any limits anytime soon. On the contrary, particularly over the course of the last decade, the fundamental trends have accelerated: investments in AI technology have rapidly increased , and the doubling time of training computation has shortened to just six months.

All major technological innovations lead to a range of positive and negative consequences. This is already true of artificial intelligence. As this technology becomes more and more powerful, we should expect its impact to still increase.

Because of the importance of AI, we should all be able to form an opinion on where this technology is heading and understand how this development is changing our world. For this purpose, we are building a repository of AI-related metrics, which you can find on OurWorldinData.org/artificial-intelligence .

We are still in the early stages of this history, and much of what will become possible is yet to come. A technological development as powerful as this should be at the center of our attention. Little might be as important for how the future of our world — and the future of our lives — will play out.

Acknowledgments: I would like to thank my colleagues Natasha Ahuja, Daniel Bachler, Julia Broden, Charlie Giattino, Bastian Herre, Edouard Mathieu, and Ike Saunders for their helpful comments to drafts of this essay and their contributions in preparing the visualizations.

On the Theseus see Daniel Klein (2019) — Mighty mouse , Published in MIT Technology Review. And this video on YouTube of a presentation by its inventor Claude Shannon.

The chart shows that the speed at which these AI technologies developed increased over time. Systems for which development was started early — handwriting and speech recognition — took more than a decade to approach human-level performance, while more recent AI developments led to systems that overtook humans in only a few years. However, one should not overstate this point. To some extent, this is dependent on when the researchers started to compare machine and human performance. One could have started evaluating the system for language understanding much earlier, and its development would appear much slower in this presentation of the data.

It is important to remember that while these are remarkable achievements — and show very rapid gains — these are the results from specific benchmarking tests. Outside of tests, AI models can fail in surprising ways and do not reliably achieve performance that is comparable with human capabilities.

The relevant publications are the following:

2014: Goodfellow et al.: Generative Adversarial Networks

2015: Radford, Metz, and Chintala: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016: Liu and Tuzel: Coupled Generative Adversarial Networks

2017: Karras et al.: Progressive Growing of GANs for Improved Quality, Stability, and Variation

2018: Karras, Laine, and Aila: A Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN from NVIDIA)

2019: Karras et al.: Analyzing and Improving the Image Quality of StyleGAN

AI-generated faces generated by this technology can be found on thispersondoesnotexist.com .

2020: Ho, Jain, and Abbeel: Denoising Diffusion Probabilistic Models

2021: Ramesh et al: Zero-Shot Text-to-Image Generation (first DALL-E from OpenAI; blog post ). See also Ramesh et al. (2022) — Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2 from OpenAI; blog post ).

2022: Saharia et al: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Google’s Imagen; blog post )

Because these systems have become so powerful, the latest AI systems often don’t allow the user to generate images of human faces to prevent abuse.

From Chowdhery et al. (2022) —  PaLM: Scaling Language Modeling with Pathways . Published on arXiv on 7 Apr 2022.

See the footnote on the chart's title for the references and additional information.

The data is taken from Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos (2022) — Compute Trends Across Three eras of Machine Learning . Published in arXiv on March 9, 2022. See also their post on the Alignment Forum .

The authors regularly update and extend their dataset, a helpful service to the AI research community. At Our World in Data, my colleague Charlie Giattino regularly updates the interactive version of this chart with the latest data made available by Sevilla and coauthors.

See also these two related charts:

Number of parameters in notable artificial intelligence systems

Number of datapoints used to train notable artificial intelligence systems

At some point in the future, training computation is expected to slow down to the exponential growth rate of Moore's Law. Tamay Besiroglu, Lennart Heim, and Jaime Sevilla of the Epoch team estimate in their report that the highest probability for this reversion occurring is in the early 2030s.

The training computation of PaLM, developed in 2022, was 2,700,000,000 petaFLOP. The training computation of AlexNet, the AI with the largest training computation up to 2012, was 470 petaFLOP. 2,500,000,000 petaFLOP / 470 petaFLOP = 5,319,148.9. At the same time, the amount of training computation required to achieve a given performance has been falling exponentially.

The costs have also increased quickly. The cost to train PaLM is estimated to be $9–$23 million, according to Lennart Heim, a researcher in the Epoch team. See Lennart Heim (2022) — Estimating PaLM's training cost .

Scaling up the size of neural networks — in terms of the number of parameters and the amount of training data and computation — has led to surprising increases in the capabilities of AI systems. This realization motivated the “scaling hypothesis.” See Gwern Branwen (2020) — The Scaling Hypothesis ⁠.

Her research was announced in various places, including in the AI Alignment Forum here: Ajeya Cotra (2020) —  Draft report on AI timelines . As far as I know, the report always remained a “draft report” and was published here on Google Docs .

The cited estimate stems from Cotra’s Two-year update on my personal AI timelines , in which she shortened her median timeline by 10 years.

Cotra emphasizes that there are substantial uncertainties around her estimates and therefore communicates her findings in a range of scenarios. She published her big study in 2020, and her median estimate at the time was that around the year 2050, there will be a 50%-probability that the computation required to train such a model may become affordable. In her “most conservative plausible”-scenario, this point in time is pushed back to around 2090, and in her “most aggressive plausible”-scenario, this point is reached in 2040.

The same is true for most other forecasters: all emphasize the large uncertainty associated with their forecasts .

It is worth emphasizing that the computation of the human brain is highly uncertain. See Joseph Carlsmith's New Report on How Much Computational Power It Takes to Match the Human Brain from 2020.

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Artificial Intelligence and Its Impact on Education Essay

Introduction, ai’s impact on education, the impact of ai on teachers, the impact of ai on students, reference list.

Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting robotic technologies in learning (Mikropoulos, 2018). Their mission was to help learners to study conveniently and efficiently. Today, some of the events and impact of AI on the education sector are concentrated in the fields of online learning, task automation, and personalization learning (Chen, Chen and Lin, 2020). The COVID-19 pandemic is a recent news event that has drawn attention to AI and its role in facilitating online learning among other virtual educational programs. This paper seeks to find out the possible impact of artificial intelligence on the education sector from the perspectives of teachers and learners.

Technology has transformed the education sector in unique ways and AI is no exception. As highlighted above, AI is a relatively new area of technological development, which has attracted global interest in academic and teaching circles. Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field (Fengchun et al. , 2021). This change has affected different facets of learning to the extent that government agencies and companies are looking to replicate the same success in their respective fields (IBM, 2020). However, while the advantages of AI are widely reported in the corporate scene, few people understand its impact on the interactions between students and teachers. This research gap can be filled by understanding the impact of AI on the education sector, as a holistic ecosystem of learning.

As these gaps in education are minimized, AI is contributing to the growth of the education sector. Particularly, it has increased the number of online learning platforms using big data intelligence systems (Chen, Chen and Lin, 2020). This outcome has been achieved by exploiting opportunities in big data analysis to enhance educational outcomes (IBM, 2020). Overall, the positive contributions that AI has had to the education sector mean that it has expanded opportunities for growth and development in the education sector (Rexford, 2018). Therefore, teachers are likely to benefit from increased opportunities for learning and growth that would emerge from the adoption of AI in the education system.

The impact of AI on teachers can be estimated by examining its effects on the learning environment. Some of the positive outcomes that teachers have associated with AI adoption include increased work efficiency, expanded opportunities for career growth, and an improved rate of innovation adoption (Chen, Chen and Lin, 2020). These benefits are achievable because AI makes it possible to automate learning activities. This process gives teachers the freedom to complete supplementary tasks that support their core activities. At the same time, the freedom they enjoy may be used to enhance creativity and innovation in their teaching practice. Despite the positive outcomes of AI adoption in learning, it undermines the relevance of teachers as educators (Fengchun et al., 2021). This concern is shared among educators because the increased reliance on robotics and automation through AI adoption has created conditions for learning to occur without human input. Therefore, there is a risk that teacher participation may be replaced by machine input.

Performance Evaluation emerges as a critical area where teachers can benefit from AI adoption. This outcome is feasible because AI empowers teachers to monitor the behaviors of their learners and the differences in their scores over a specific time (Mikropoulos, 2018). This comparative analysis is achievable using advanced data management techniques in AI-backed performance appraisal systems (Fengchun et al., 2021). Researchers have used these systems to enhance adaptive group formation programs where groups of students are formed based on a balance of the strengths and weaknesses of the members (Live Tiles, 2021). The information collected using AI-backed data analysis techniques can be recalibrated to capture different types of data. For example, teachers have used AI to understand students’ learning patterns and the correlation between these configurations with the individual understanding of learning concepts (Rexford, 2018). Furthermore, advanced biometric techniques in AI have made it possible for teachers to assess their student’s learning attentiveness.

Overall, the contributions of AI to the teaching practice empower teachers to redesign their learning programs to fill the gaps identified in the performance assessments. Employing the capabilities of AI in their teaching programs has also made it possible to personalize their curriculums to empower students to learn more effectively (Live Tiles, 2021). Nonetheless, the benefits of AI to teachers could be undermined by the possibility of job losses due to the replacement of human labor with machines and robots (Gulson et al. , 2018). These fears are yet to materialize but indications suggest that AI adoption may elevate the importance of machines above those of human beings in learning.

The benefits of AI to teachers can be replicated in student learning because learners are recipients of the teaching strategies adopted by teachers. In this regard, AI has created unique benefits for different groups of learners based on the supportive role it plays in the education sector (Fengchun et al., 2021). For example, it has created conditions necessary for the use of virtual reality in learning. This development has created an opportunity for students to learn at their pace (Live Tiles, 2021). Allowing students to learn at their pace has enhanced their learning experiences because of varied learning speeds. The creation of virtual reality using AI learning has played a significant role in promoting equality in learning by adapting to different learning needs (Live Tiles, 2021). For example, it has helped students to better track their performances at home and identify areas of improvement in the process. In this regard, the adoption of AI in learning has allowed for the customization of learning styles to improve students’ attention and involvement in learning.

AI also benefits students by personalizing education activities to suit different learning styles and competencies. In this analysis, AI holds the promise to develop personalized learning at scale by customizing tools and features of learning in contemporary education systems (du Boulay, 2016). Personalized learning offers several benefits to students, including a reduction in learning time, increased levels of engagement with teachers, improved knowledge retention, and increased motivation to study (Fengchun et al., 2021). The presence of these benefits means that AI enriches students’ learning experiences. Furthermore, AI shares the promise of expanding educational opportunities for people who would have otherwise been unable to access learning opportunities. For example, disabled people are unable to access the same quality of education as ordinary students do. Today, technology has made it possible for these underserved learners to access education services.

Based on the findings highlighted above, AI has made it possible to customize education services to suit the needs of unique groups of learners. By extension, AI has made it possible for teachers to select the most appropriate teaching methods to use for these student groups (du Boulay, 2016). Teachers have reported positive outcomes of using AI to meet the needs of these underserved learners (Fengchun et al., 2021). For example, through online learning, some of them have learned to be more patient and tolerant when interacting with disabled students (Fengchun et al., 2021). AI has also made it possible to integrate the educational and curriculum development plans of disabled and mainstream students, thereby standardizing the education outcomes across the divide. Broadly, these statements indicate that the expansion of opportunities via AI adoption has increased access to education services for underserved groups of learners.

Overall, AI holds the promise to solve most educational challenges that affect the world today. UNESCO (2021) affirms this statement by saying that AI can address most problems in learning through innovation. Therefore, there is hope that the adoption of new technology would accelerate the process of streamlining the education sector. This outcome could be achieved by improving the design of AI learning programs to make them more effective in meeting student and teachers’ needs. This contribution to learning will help to maximize the positive impact and minimize the negative effects of AI on both parties.

The findings of this study demonstrate that the application of AI in education has a largely positive impact on students and teachers. The positive effects are summarized as follows: improved access to education for underserved populations improved teaching practices/instructional learning, and enhanced enthusiasm for students to stay in school. Despite the existence of these positive views, negative outcomes have also been highlighted in this paper. They include the potential for job losses, an increase in education inequalities, and the high cost of installing AI systems. These concerns are relevant to the adoption of AI in the education sector but the benefits of integration outweigh them. Therefore, there should be more support given to educational institutions that intend to adopt AI. Overall, this study demonstrates that AI is beneficial to the education sector. It will improve the quality of teaching, help students to understand knowledge quickly, and spread knowledge via the expansion of educational opportunities.

Chen, L., Chen, P. and Lin, Z. (2020) ‘Artificial intelligence in education: a review’, Institute of Electrical and Electronics Engineers Access , 8(1), pp. 75264-75278.

du Boulay, B. (2016) Artificial intelligence as an effective classroom assistant. Institute of Electrical and Electronics Engineers Intelligent Systems , 31(6), pp.76–81.

Fengchun, M. et al. (2021) AI and education: a guide for policymakers . Paris: UNESCO Publishing.

Gulson, K . et al. (2018) Education, work and Australian society in an AI world . Web.

IBM. (2020) Artificial intelligence . Web.

Live Tiles. (2021) 15 pros and 6 cons of artificial intelligence in the classroom . Web.

Mikropoulos, T. A. (2018) Research on e-Learning and ICT in education: technological, pedagogical and instructional perspectives . New York, NY: Springer.

Rexford, J. (2018) The role of education in AI (and vice versa). Web.

Seo, K. et al. (2021) The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education , 18(54), pp. 1-12.

UNESCO. (2021) Artificial intelligence in education . Web.

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AI-assisted writing is quietly booming in academic journals. Here’s why that’s OK

Lecturer in Bioethics, Monash University & Honorary fellow, Melbourne Law School, Monash University

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If you search Google Scholar for the phrase “ as an AI language model ”, you’ll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says:

As an AI language model, I don’t have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …

Obvious gaffes like this aren’t the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as “commendable”, “meticulously” and “intricate”), and found they became far more common after the launch of ChatGPT – so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it’s because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as “ contaminating ” scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between “plagiarising” text authored by humans and text authored by AI. Those who plagiarise humans’ work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone’s autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a “mushroom” for my own purposes.

These kinds of overconfident mistakes have been referred to as “ AI hallucinations ” and “ AI bullshit ”. While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science – one of the world’s most influential academic journals – disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT’s own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words “commendable”, “meticulously” and “intricate”).

The second problem is that banning generative AI outright prevents us from realising these technologies’ benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

• Artificial intelligence (AI)
• Academic journals
• Academic publishing
• Hallucinations
• Scholarly publishing
• Academic writing
• Large language models
• Generative AI

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The Ultimate Guide to AI Essay Writing

Table of contents

Laura Jane Bradbury

There are several aspects of essay writing that many students can struggle with. In particular, seven common problems make it more stressful than it needs to be. From staying within the word limit, to formulating a thesis statement and creating an engaging hook.

As a writer, I'm used to working with article briefs similar to student assignments. We both must follow word counts, avoid plagiarism in our research, and create powerful introductions that grab our readers' attention.

Here are six tips to help make the essay writing process easier.

Write better essays with AI > Write better essays with AI >

1. Create a stronger thesis statement using AI

A thesis statement helps you stay on-track with your essay’s overall aim. It’s a brief statement that summarizes what your essay is about and what readers can expect. For example, are you exploring different ideas, making an argument, explaining something in detail? 

Follow these steps to write a strong thesis statement: 

• Identify and research your essay topic — find a particular angle that interests you.
• Ask a research question that encourages you to explore your topic in more detail. 
• Use your initial thoughts or theories to answer your question. This will be your working thesis. 
• Research your question and working thesis. Look through trustworthy sources to evaluate your thesis’ strengths and weaknesses.
• Refine your thesis. Your research may have altered your opinion or created a new angle.

Wordtune’s AI capabilities can refine your statement through eliminating unnecessary words.

Read the full article: How to Write a Better Thesis Statement Using AI 

2. apply “smart hacks” to boost your word count.

In addition to being too long, essays can also be too short. I know how frustrating it is when you have explained everything you wanted to, but are still 200 words under the word count.

Before you add filler words or rewrite the same content in different sections of your essay, apply these smart hacks to your copy:

• Add examples: Examples can illustrate a point and make it easier to understand. They’re particularly useful for explaining complex academic and technical information.
• Use quotes and references: Find valuable quotes and references that can support your essay’s argument.
• Extend your introduction and conclusion: This is where you can add free flowing paragraphs without needing to validate sentences with references, sources, and quotes. Consider adding detail on what inspired you to explore your topic, or reiterate the key takeaway from your essay. You can also share personal experiences and research findings to introduce or close your topic where appropriate.
• Use an AI writing tool: AI tools such as Wordtune can generate text. While reading through your essay, you may find that some paragraphs or points could be expanded. Paste your words into Wordtune Editor, and click “Continue Writing” under “Spices'' for ideas on additional text you can include.

Read the full article: 10 Ways to Increase Your Essay Word Count (AI Included)

3. hook your readers in your opening sentence.

The pressure to deliver a unique and attention-grabbing hook can make your opening sentence the hardest to craft.

Before brainstorming, ensure you have performed in-depth research and that you understand your essay’s tone and audience. 

Your research may uncover facts and arguments that can guide your hook, such as a shocking statistic or discovery. In addition, your intended audience will impact the tone you should use and how you should introduce readers to your essay. For instance, sharing a joke or a light-hearted anecdote will likely be too casual for a serious argumentative essay.

Some of the most-effective ways to create an enticing hook include:

• Shocking statistics : These are facts that will surprise your readers.
• Bold claim : This is a claim that your readers don’t necessarily already accept as fact, or that they may not even have heard of. 
• Storytelling/anecdote: Stories can help your readers connect with your topic and you as the author.
• Questions: Asking a question can spark curiosity. An interesting question can encourage people to read your essay to find out the answer.
• Description: This is a unique and specific description of something (a person, event, time period, etc.) that relates to the argument you’re making in your essay.

Read the full article: Essay Hook Examples That Grab Attention (Formula For Better Grades)  

4. look at examples of explanatory essays.

When in doubt, examples can be your best friend. Especially if you are writing an explanatory essay, as there are many different subtypes. 

Explanatory essays examine a topic or situation in detail. You as the author provide evidence and facts to explain why something happened, why something works, or what something is. The essay must have an objective perspective, and the facts must speak for themselves.

The different types of explanatory essays include:

• How-tos: Step-by-step instructions on how to do something.
• Problem and solution: Explaining a problem and providing a solution.
• Chronology: Detailing something’s history or backstory in chronological order.
• Cause-and-effect analysis: Examining a phenomenon to explain what caused it and what it influenced.

Because of the various explanatory sub-types, it helps to look at different examples to decide which style works best for your subject. Looking at examples also gives you an idea of how to structure your essay and present your arguments effectively.

Read the full article: 7+ Explanatory Essay Examples That Get the Best Grades  

5. use ai to avoid plagiarism.

While there are several ways to avoid plagiarism, including expressing your research and findings in your own words and citing your sources, Wordtune can also help you. The AI technology produces text not written anywhere else on the internet — see for yourself by copying Wordtune’s generated text into search engines such as Google. 

Accidental plagiarism can make your work look less credible and unprofessional. In addition to making your research look underdeveloped, not referencing your sources correctly can suggest you’re trying to pass off other people’s ideas as your own. With the right tools, however, you can avoid this critical essay mistake.

Read the full article: 9 Steps to Avoid Plagiarism As a Student (Including Using AI)

6. break persuasive essays into manageable chunks.

Persuasive essays use research and logic to persuade the reader of your opinion on a particular subject. In some ways, this article is persuasive because I use research and examples to persuade (or at least encourage) you to practice these essay writing methods. 

If writing an argument intimidates you, this five-step list will break your essay into manageable chunks, making it easier to write.

• Identify a topic or issue that is arguable from more than one position . You must form an opinion and not argue a simple fact. Find a topic that has multiple theories and no conclusive evidence, such as “Is animal testing ethical?” or “Should students still study Shakespeare?”
• Use research to create your thesis (follow the steps mentioned earlier).
• Find evidence to back up your thesis . Double-check sources for credibility and try to spot missing information that could impact your argument.
• Address opposing ideas others may hold . Use search engines such as Google to research opposite arguments. For instance, if I believe animal testing is not ethical, I can research “Why animal testing is ethical” to find other theories. To respecfully show different opinions, first acknowledge the opposing view and evidence. Then, share your argument and provide logical evidence as to why your argument is the correct one.
• Create a convincing conclusion . Rather than repeating what you’ve already said, draw from the arguments you’ve made and point out how they logically prove your thesis.

Read the full article: How to Write a Persuasive Essay (This Convinced My Professor!)  

A successful essay formula.

Essays contain a lot of ingredients. From deciding on the right hook, to staying in line with the word count. But with the right tools — such as AI — and the use of smart hacks, you can create an effective formula that will make the essay writing process much easier.

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What is ChatGPT? Here's everything you need to know about ChatGPT, the chatbot everyone's still talking about

• ChatGPT is getting a futuristic human update. 
• ChatGPT has drawn users at a feverish pace and spurred Big Tech to release other AI chatbots.
• Here's how ChatGPT works — and what's coming next.

OpenAI's blockbuster chatbot ChatGPT is getting a new update. 

On Monday, OpenAI unveiled GPT-4o for ChatGPT, a new version of the bot that can hold conversations with users in a very human tone. The new version of the chatbot will also have vision abilities.

The futuristic reveal quickly prompted jokes about parallels to the movie "Her," with some calling the chatbot's new voice " cringe ."

The move is a big step for the future of AI-powered virtual assistants, which tech companies have been racing to develop.

Since its release in 2022, hundreds of millions of people have experimented with the tool, which is already changing how the internet looks and feels to users.

Users have flocked to ChatGPT to improve their personal lives and boost productivity . Some workers have used the AI chatbot to develop code , write real estate listings , and create lesson plans, while others have made teaching the best ways to use ChatGPT a career all to itself.

ChatGPT offers dozens of plug-ins to those who subscribe to ChatGPT Plus subscription. An Expedia one can help you book a trip, while an OpenTable one will get nab you a dinner reservation. And last month, OpenAI launched Code Interpreter, a version of ChatGPT that can code and analyze data .

While the personal tone of conversations with an AI bot like ChatGPT can evoke the experience of chatting with a human, the technology, which runs on " large language model tools, " doesn't speak with sentience and doesn't "think" the way people do. 

That means that even though ChatGPT can explain quantum physics or write a poem on command, a full AI takeover isn't exactly imminent , according to experts.

"There's a saying that an infinite number of monkeys will eventually give you Shakespeare," said Matthew Sag, a law professor at Emory University who studies copyright implications for training and using large language models like ChatGPT.

"There's a large number of monkeys here, giving you things that are impressive — but there is intrinsically a difference between the way that humans produce language, and the way that large language models do it," he said. 

Chatbots like ChatGPT are powered by large amounts of data and computing techniques to make predictions to string words together in a meaningful way. They not only tap into a vast amount of vocabulary and information, but also understand words in context. This helps them mimic speech patterns while dispatching an encyclopedic knowledge. 

Other tech companies like Google and Meta have developed their own large language model tools, which use programs that take in human prompts and devise sophisticated responses.

Despite the AI's impressive capabilities, some have called out OpenAI's chatbot for spewing misinformation , stealing personal data for training purposes , and even encouraging students to cheat and plagiarize on their assignments. 

Some recent efforts to use chatbots for real-world services have proved troubling. In 2023, the mental health company Koko came under fire after its founder wrote about how the company used GPT-3 in an experiment to reply to users. 

Koko cofounder Rob Morris hastened to clarify on Twitter that users weren't speaking directly to a chatbot, but that AI was used to "help craft" responses. 

Read Insider's coverage on ChatGPT and some of the strange new ways that both people and companies are using chat bots: 

The tech world's reception to ChatGPT:

Microsoft is chill with employees using ChatGPT — just don't share 'sensitive data' with it.

Microsoft's investment into ChatGPT's creator may be the smartest $1 billion ever spent

ChatGPT and generative AI look like tech's next boom. They could be the next bubble.

The ChatGPT and generative-AI 'gold rush' has founders flocking to San Francisco's 'Cerebral Valley'

Insider's experiments: 

I asked ChatGPT to do my work and write an Insider article for me. It quickly generated an alarmingly convincing article filled with misinformation.

I asked ChatGPT and a human matchmaker to redo my Hinge and Bumble profiles. They helped show me what works.

I asked ChatGPT to reply to my Hinge matches. No one responded.

I used ChatGPT to write a resignation letter. A lawyer said it made one crucial error that could have invalidated the whole thing .

Read ChatGPT's 'insulting' and 'garbage' 'Succession' finale script

An Iowa school district asked ChatGPT if a list of books contains sex scenes, and banned them if it said yes. We put the system to the test and found a bunch of problems.

Developments in detecting ChatGPT: 

Teachers rejoice! ChatGPT creators have released a tool to help detect AI-generated writing

A Princeton student built an app which can detect if ChatGPT wrote an essay to combat AI-based plagiarism

Professors want to 'ChatGPT-proof' assignments, and are returning to paper exams and requesting editing history to curb AI cheating

ChatGPT in society: 

BuzzFeed writers react with a mix of disappointment and excitement at news that AI-generated content is coming to the website

ChatGPT is testing a paid version — here's what that means for free users

A top UK private school is changing its approach to homework amid the rise of ChatGPT, as educators around the world adapt to AI

Princeton computer science professor says don't panic over 'bullshit generator' ChatGPT

DoNotPay's CEO says threat of 'jail for 6 months' means plan to debut AI 'robot lawyer' in courtroom is on ice

It might be possible to fight a traffic ticket with an AI 'robot lawyer' secretly feeding you lines to your AirPods, but it could go off the rails

Online mental health company uses ChatGPT to help respond to users in experiment — raising ethical concerns around healthcare and AI technology

What public figures think about ChatGPT and other AI tools:

What Elon Musk, Bill Gates, and 12 other business leaders think about AI tools like ChatGPT

Elon Musk was reportedly 'furious' at ChatGPT's popularity after he left the company behind it, OpenAI, years ago

CEO of ChatGPT maker responds to schools' plagiarism concerns: 'We adapted to calculators and changed what we tested in math class'

A theoretical physicist says AI is just a 'glorified tape recorder' and people's fears about it are overblown

'The most stunning demo I've ever seen in my life': ChatGPT impressed Bill Gates

Ashton Kutcher says your company will probably be 'out of business' if you're 'sleeping' on AI

ChatGPT's impact on jobs: 

AI systems like ChatGPT could impact 300 million full-time jobs worldwide, with administrative and legal roles some of the most at risk, Goldman Sachs report says

Jobs are now requiring experience with ChatGPT — and they'll pay as much as $800,000 a year for the skill

Related stories

ChatGPT may be coming for our jobs. Here are the 10 roles that AI is most likely to replace.

AI is going to eliminate way more jobs than anyone realizes

It's not AI that is going to take your job, but someone who knows how to use AI might, economist says

4 careers where workers will have to change jobs by 2030 due to AI and shifts in how we shop, a McKinsey study says

Companies like Amazon, Netflix, and Meta are paying salaries as high as $900,000 to attract generative AI talent

How AI tools like ChatGPT are changing the workforce:

10 ways artificial intelligence is changing the workplace, from writing performance reviews to making the 4-day workweek possible

Managers who use AI will replace managers who don't, says an IBM exec

How ChatGPT is shaping industries: 

ChatGPT is coming for classrooms, hospitals, marketing departments, and everything else as the next great startup boom emerges

Marketing teams are using AI to generate content, boost SEO, and develop branding to help save time and money, study finds

AI is coming for Hollywood. 'It's amazing to see the sophistication of the images,' one of Christopher Nolan's VFX guy says.

AI is going to offer every student a personalized tutor, founder of Khan Academy says

A law firm was fined $5,000 after one of its lawyers used ChatGPT to write a court brief riddled with fake case references

How workers are using ChatGPT to boost productivity:  

CheatGPT: The hidden wave of employees using AI on the sly

I used ChatGPT to talk to my boss for a week and she didn't notice. Here are the other ways I use it daily to get work done.

I'm a high school math and science teacher who uses ChatGPT, and it's made my job much easier

Amazon employees are already using ChatGPT for software coding. They also found the AI chatbot can answer tricky AWS customer questions and write cloud training materials.

How 6 workers are using ChatGPT to make their jobs easier

I'm a freelance editor who's embraced working with AI content. Here's how I do it and what I charge.

How people are using ChatGPT to make money:

How ChatGPT and other AI tools are helping workers make more money

Here are 5 ways ChatGPT helps me make money and complete time-consuming tasks for my business

ChatGPT course instruction is the newest side hustle on the market. Meet the teachers making thousands from the lucrative gig.

People are using ChatGPT and other AI bots to work side hustles and earn thousands of dollars — check out these 8 freelancing gigs

A guy tried using ChatGPT to turn $100 into a business making 'as much money as possible.' Here are the first 4 steps the AI chatbot gave him

We used ChatGPT to build a 7-figure newsletter. Here's how it makes our jobs easier.

I use ChatGPT and it's like having a 24/7 personal assistant for $20 a month. Here are 5 ways it's helping me make more money.

A worker who uses AI for a $670 monthly side hustle says ChatGPT has 'cut her research time in half'

How companies are navigating ChatGPT: 

From Salesforce to Air India, here are the companies that are using ChatGPT

Amazon, Apple, and 12 other major companies that have restricted employees from using ChatGPT

A consultant used ChatGPT to free up time so she could focus on pitching clients. She landed $128,000 worth of new contracts in just 3 months.

Luminary, an AI-generated pop-up restaurant, just opened in Australia. Here's what's on the menu, from bioluminescent calamari to chocolate mousse.

A CEO is spending more than $2,000 a month on ChatGPT Plus accounts for all of his employees, and he says it's saving 'hours' of time

How people are using ChatGPT in their personal lives:

ChatGPT planned a family vacation to Costa Rica. A travel adviser found 3 glaring reasons why AI won't replace experts anytime soon.

A man who hated cardio asked ChatGPT to get him into running. Now, he's hooked — and he's lost 26 pounds.

A computer engineering student is using ChatGPT to overcome learning challenges linked to her dyslexia

How a coder used ChatGPT to find an apartment in Berlin in 2 weeks after struggling for months

Food blogger Nisha Vora tried ChatGPT to create a curry recipe. She says it's clear the instructions lacked a human touch — here's how.

Men are using AI to land more dates with better profiles and personalized messages, study finds

Lawsuits against OpenAI:

OpenAI could face a plagiarism lawsuit from The New York Times as tense negotiations threaten to boil over, report says

This is why comedian Sarah Silverman is suing OpenAI, the company behind ChatGPT

2 authors say OpenAI 'ingested' their books to train ChatGPT. Now they're suing, and a 'wave' of similar court cases may follow.

A lawsuit claims OpenAI stole 'massive amounts of personal data,' including medical records and information about children, to train ChatGPT

A radio host is suing OpenAI for defamation, alleging that ChatGPT created a false legal document that accused him of 'defrauding and embezzling funds'

Tips on how to write better ChatGPT prompts:

7 ways to use ChatGPT at work to boost your productivity, make your job easier, and save a ton of time

I'm an AI prompt engineer. Here are 3 ways I use ChatGPT to get the best results.

12 ways to get better at using ChatGPT: Comprehensive prompt guide

Here's 9 ways to turn ChatGPT Plus into your personal data analyst with the new Code Interpreter plug-in

OpenAI's ChatGPT can write impressive code. Here are the prompts you should use for the best results, experts say.

Axel Springer, Business Insider's parent company, has a global deal to allow OpenAI to train its models on its media brands' reporting.

Watch: What is ChatGPT, and should we be afraid of AI chatbots?

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10 Best AI Essay Writer Platforms to Help You Get Better Grades

Essays are some of the most common homework assignments for students from middle school all the way through to college. And it’s not always easy to find time to get every essay done on time or write about subjects you struggle with. That’s where AI essay writing platforms can help. Most AI essay writers use powerful natural language technology to generate essays, add citations, use AI to auto-complete essay paragraphs, research subjects, and much more. It’s a powerful learning tool, and this list will look at 10 of the best options to use.

EssayGPT – Best AI Essay Writer Overall

When it comes to the best AI essay writers, EssayGPT by HIX.AI tops the list. It stands out for its highly impressive technology, capable of tackling almost any subject imaginable, from complicated science essays to in-depth literature analysis.

So much more than a simple AI essay writer, EssayGPT also offers editing and auto-complete features, helping students fine-tune and improve their work. It can automatically fill out phrases and sentences with a click, as well as add citations to your essays in various styles to suit the tone and target audience.

But EssayGPT’s feature list doesn’t end there. It also comes with built-in grammar and plagiarism checkers to ensure that your work is free of mistakes. And it has a powerful search tool that students can use to look up relevant and useful information to draw from when completing their essays.

• Industry-leading technology
• A major time-saver to help you hit any deadline
• Much cheaper than a tutor or human-based essay writing services
• Prices can add up if you use it often

Enjoy academic success with EssayGPT.

EssayWriter – Best AI Essay Writer for Researching

EssayWriter is another AI essay generator that can appeal to students at any level of academia. With its advanced technology and deep database of academic resources, it makes it easy for students to look up relevant references and sources connected to the topics they’re writing about, making it faster and easier to create informative and detailed text.

That’s not all. EssayWriter also comes with a simple citation generation, letting you make citations that are formatted to suit common standards, like APA and Chicago. Plus, it has a built-in plagiarism detector to ensure your work is original, coupled with real-time AI content suggestions to help you complete essays more quickly.

• Customizable writing styles and tones
• Sources accurate content from academic sources
• Great value for money
• Requires an internet connection to access

EssayFlow – Best AI Essay Writer for Any Form of Essays

EssayAI is a leading undetectable AI essay generator that stands out on the market. It produces high-quality, undetectable essays in great detail, with expert citations and sources listed throughout, showing you exactly how each point was made and supported.

It offers a range of customization options, allowing you to tailor your essays to specific target audiences, adjust tones of voice, select preferred languages, and more. This level of customization ensures that your essays are precisely crafted to meet your unique requirements and effectively engage your intended audience.

• An excellent undetectable AI essay writer
• Adds lots of quality citations
• Many customization features
• Needs to upgrade to unlock more features

EssayAI – Best AI Essay Writer for Undetectable Academic Writing

ToolBaz – Best AI Essay Writer for Adjustable Creativity

Next on our list is ToolBaz. Launched in 2022, the ToolBaz AI essay writer is part of an extensive suite of AI writing solutions on the ToolBaz platform. It’s fast, reliable, and ready to use at any time, whenever you need it.

This AI essay writer also has a unique adjustable creativity feature. It lets users change the creativity of their essays via a simple slider, giving the AI bot more or less freedom to get imaginative with the content.

• Completely free to use
• Useful for high school, middle school, and more
• Simple, beginner-friendly interface
• Sometimes requires manual editing

Caktus AI – Best AI Essay Writer for Improving Your Knowledge

Caktus AI is an AI essay writer that is trusted by students and teachers alike as a reliable learning aid. Instead of simply doing work for students, this AI platform, founded by Harrison Leonard and Tao Zhang, is designed to teach them and help them improve.

Caktus AI fills its essays with precise citations and the most accurate information, sourced from published essays and academic textbooks. This results in high-quality, easy-to-read output text that can help you improve in even the trickiest subjects, with prices starting at $14.99 per month.

• A vast database of academic resources
• Designed by students, with students in mind
• Flexible pricing to suit your budget
• More expensive than other AI essay generators

Essaybot – Best AI Essay Writer for High School Students

Next up, we have Essaybot. This company was founded in 2023 and has staff both in China and the U.S. It’s quickly become one of the go-to essay generator tools for many students, thanks to its ease-of-use.

With EssayBot, all you have to do is type the subject or concept of your essay into the box provided and then wait for it to create content for you. It’ll generate precise, relevant text for any subject you need help with, adding citations automatically and running a plagiarism check too.

• Very easy for beginner users
• Built-in grammar and spell check
• Offers unlimited essay downloads
• May struggle with college-level texts

StudyCrumb – Best AI Essay Generator to Use for Free

While many AI essay writing platforms charge high fees or recurring subscriptions, StudyCrumb is 100% free. Launched by the Crumb4Life company, which is based in Estonia, this trusty AI essay generator is completely risk-free and perfect to add to your academic arsenal.

Students can easily and quickly get help with any essay through StudyCrumb. It boasts fast processing times and is much more cost-effective than spending money on other tools or paying a human tutor.

• Produces relevant, good quality content
• Fast essay generation
• Intuitive user interface
• Text regularly needs manual editing

EssayService – Best AI Essay Writer for Easy Essay Generation

EssayService is an AI-powered essay generator that was launched in 2023 by a company that had previously specialized in human essay writing services. They decided to branch out into AI, resulting in the development of this clever and easy-to-use AI tool.

With the EssayService AI essay writer, users can paste their questions or subjects into a box and get instant essay generation. It also supports essay outline generation, giving you headers and talking points that you can then flesh out on your own.

• Excellent for custom essay requests
• Draws from a vast database of academic sources 
• Can save you hours on essay writing
• May struggle with complex science essays

PaperTyper – Best AI Essay Writer for Improving Your Essays

PaperTyper isn’t quite the same as the other essay writers on this list. Developed by a one-woman team, Juli Sheller, this tool is part of an entire suite of academic AI aids, including a plagiarism checker, grammar scanner, and more.

In short, PaperType has all you need to write essays, check them, proofread them, and fine-tune them. It’s ideal for students who want to do most of the work themselves, but still want to use AI to make improvements to the clarity and flow of their texts.

• Completely free to start
• Checks grammar and spelling for you
• Works at all levels of education
• Interface may be a little awkward for first-time users

Who Should Use an AI Essay Writer?

So, who are AI essay generators actually aimed at? Well, almost any kind of student can benefit from these tools. It’s important to note that many AI essay writers are capable of writing a range of different kinds of content, from persuasive essays to descriptive papers, analytical texts, and more.

Therefore, it doesn’t matter what level of education you’re at or what kind of work you’ve got to do. Every student can use and benefit from an AI essay generator. What’s more, they’re ideal for students who have very busy lives. As well as those who feel like they don’t have enough time to keep up with their assignments.

You may also enjoy big benefits from AI essay writing technology if you’re the kind of user who tends to struggle with certain subjects. Let’s say that you excel in geography but struggle with history, for instance. In that case, you can use AI to help out with your history essays and ease your stress in that subject.

What Should I Look for in an AI Essay Generator?

With all of those different AI essay writers on the market, you might not know which one you should actually use. Well, here are some key factors that you can focus on when trying to find the right one:

• Reliable Citation Generation : The best AI essay writers make it easy to add citations to your work. Citations help to make essays feel more professional and can aid in getting better grades. Look for writers with built-in citation generators, and favor those that can add citations in different styles, like Chicago and MLA.
• Grammar and Spelling Checks : Leading AI essay writers are also capable of scanning essay text to spot and fix errors. They can get rid of any little typos that could make your essay look rushed, for instance. They can also improve punctuation and grammar to produce a more professional and high quality piece of text.

Research Feature : Top quality AI essay writers should also make researching your essays a lot easier. Many of the best ones come with their own research features to help you look up relevant content. Focus on writers that allow you to find relevant content quickly to save time while writing essays.

Overall, AI essay writers are incredible tools to consider. They can help students in so many ways, easing their essay-related stresses, making their academic lives easier, saving them time, and even saving them money, too. Try one of the top 10 tools listed above and see how an AI essay generator can elevate your education.

This is a   guest post,   created for informational purposes only, and should not be considered as professional advice. Readers are encouraged to conduct their   own research   and consult with relevant experts before making any financial or investment decisions.

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May 13, 2024

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AI-assisted writing is quietly booming in academic journals—here's why that's OK

by Julian Koplin, The Conversation

If you search Google Scholar for the phrase " as an AI language model ," you'll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says,

"As an AI language model, I don't have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …"

Obvious gaffes like this aren't the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as "commendable," "meticulously" and "intricate"), and found they became far more common after the launch of ChatGPT—so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it's because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as " contaminating " scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between "plagiarizing" text authored by humans and text authored by AI. Those who plagiarize humans' work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone's autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a "mushroom" for my own purposes.

These kinds of overconfident mistakes have been referred to as "AI hallucinations" and " AI bullshit ." While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science—one of the world's most influential academic journals—disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT's own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words "commendable," "meticulously" and "intricate").

The second problem is that banning generative AI outright prevents us from realizing these technologies' benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

Provided by The Conversation

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