Generative AI

DALLE-2 images. Created by Mark Pegrum (2023), using the prompt: ‘A painting celebrating humans and artificial intelligence working together, Picasso style’

This page serves as a clearinghouse of information about generative AI, especially as it relates to education. It is divided into the following sections: strong vs weak AI; conversational vs generative AI; generative AI; ChatGPT and the rise of generative AI; generative AI beyond ChatGPT; ongoing developments in generative AI; prompting generative AI; generative AI in education; generative AI in assessment; and issues with generative AI.

Strong vs weak AI

Artificial intelligence (AI) may be divided into two broad categories. Strong AI, also known as artificial general intelligence (AGI), refers to the ability of machines to display general intelligence, of the kind displayed by humans, which can be applied to many different tasks and situations. While research is ongoing, strong AI remains elusive, and for now is mostly the stuff of utopian or, perhaps more often, dystopian science fiction. In July 2024, OpenAI, the company behind GPT and ChatGPT, proposed a 5-level framework of progress towards AGI, stating that it is currently at Level 1 (conversational) but approaching Level 2 (human-level problem solving). However, some researchers suggest that development of strong AI cannot occur through current large language model (LLM)-based generative AI alone, but may require embodied AI like robots that can interact with and learn from the real world.

Weak AI, by contrast, refers to the ability of machines to apply intelligence to very specific tasks, typically involving very specific pattern-matching; such AI often exceeds the ability of humans in given narrow domains. Examples of weak AI already in everyday use include image recognition, speech recognition, natural language processing, automated translation and learning analytics. Considerable progress has been made in weak AI since the advent of machine learning, and especially deep learning based on artificial neural networks, resulting notably in today’s large language models (LLMs) (see below).

For a more detailed general overview of AI, including weak and strong AI, see IBM’s guide on What is artificial intelligence (AI)?

Conversational vs generative AI

Within the domain of weak AI, it is important to distinguish between conversational AI which is trained on large datasets of human interactions and can provide responses to human users in a limited series of conversational turns (e.g., first-generation digital assistants like Apple’s Siri and Amazon’s Alexa, and many corporate or organisational chatbots), and generative AI which is grounded in LLMs that are trained on vast datasets of texts and other media, and can generate new (or at least remixed) content in response to human users’ queries. The distinction between conversational and generative AI is widely discussed in the technology press; for examples, see:

• • A Complete Guide: Conversational AI vs. Generative AI (Matthew McMullen/Data Science Central, 2023)
  • Conversational AI vs. Generative AI: What’s the Difference? (Amanda Hetler/TechTarget, 2024)
  • Differences between Conversational AI and Generative AI (Sridhar CS/Purple Slate, 2023)
  • What is the Difference between Generative AI and Conversational AI? (Webio, n. d.)

Generative AI

Generative AI is grounded in LLMs such as the following:

• • Claude (Anthropic) [USA]
  • Doubao (ByteDance) [China]
  • Ernie (Baidu) [China]
  • Gemini [formerly LaMDA/Palm) (Google) [USA]
  • GPT (OpenAI) [USA]
  • Hermes (Nous Research) [USA] *open source
  • Hunyuan (Tencent) [China] *open source
  • Inflection (Inflection AI) [USA]
  • Llama (Meta) [USA] *open source
  • Mistral Large/Pixtral Large (Mistral) [France] *open source
  • MM1 & OpenELM (on-device LLM) (Apple) [USA]
  • Qwen/Tongyi Qianwen (Alibaba) [China] *open source
  • SenseNova (SenseTime) [China] *open source
  • Spark/Spark Desk (iFlytek) [China]

Generative AI chatbots combine generative AI with the ethos of conversational AI (in the form of chat interfaces). It is this combination, and this ability to chat with generative AI, that caught the public’s imagination with the release of ChatGPT in late 2022, and has led to the recent explosion of generative AI tools. Generative AI chatbots, some of which have the same names as their underlying LLMs, include:

• • ChatGPT (OpenAI) [USA]
  • Claude (Anthropic) [USA]
  • Copilot [underpinned by GPT/runs alongside Bing] (Microsoft) [USA]
  • Doubao/豆包 (ByteDance) [China]
  • Ernie Bot/文心一言 [China]
  • Gemini [formerly Bard] (Google) [USA]
  • Le Chat (Mistral/Pixtral) [France]
  • Meta AI Assistant (Meta) [USA]
  • Nous Chat (Nous Research) [USA] 
  • Perplexity AI (Perplexity) [USA]
  • Pi (Inflection) [USA]
  • Qwen-Chat/通义千问 (Alibaba) [China]
  • SenseChat/商汤商量 (SenseTime) [China]

For links to these and similar services, see the section on Generative AI beyond ChatGPT below. For a comparison of the performance of different AI models/LLMs, see the Artificial Analysis website, Seal Leaderboards and Tracking AI: IQ.

ChatGPT and the rise of generative AI

As noted above, the first well-known example of generative AI with a chat interface was OpenAI‘s ChatGPT, which essentially grafted a chatbot onto an existing LLM, namely GPT-3.5. More advanced versions of the underpinning LLM have subsequently been released, with  GPT-4 released in March 2023. ChatGPT is now based on GPT-4o, with a higher usage limit for ChatGPT Plus subscribers. Work is reportedly underway on GPT-5. 

ChatGPT can now be controlled not only by text commands but by voice. While ChatGPT did not originally have live web access, it now does. It is also now available in app format for both iOS and Android devices. In November 2023, OpenAI made it possible for users with no programming knowledge to build customised GPTs; for a list of GPTs that have been already been created, see The Rundown’s GPTs directory, or go directly to the GPT Store launched in January 2024. GPT-4 has been incorporated into a number of other platforms and apps, including Microsoft’s Copilot.

Generative AI beyond ChatGPT

Chatbots > Generative AI chatbots with similarities to ChatGPT include Anthropic’s Claude, Google’s Gemini, Inflection’s Pi, Meta’s Meta AI (which is accessible via Facebook Messenger, Instagram chats or WhatsApp), and Perplexity AI; others include X’s Grok, slated as witty and irreverent, and Hume, slated as empathetic AI. Major Chinese developments include Baidu’s Ernie Bot (文心一言), Alibaba’s Qwen-Chat (通义千问) and ByteDance’s Doubao (豆包). Poe is a service which brings together multiple generative AI platforms, allowing users to compare results. DuckDuckGo offers private AI chat based on multiple LLMs, with a promise that chats are not saved or used to train AI.

For those with financial means, largely in the Global North, we are seeing trends away from the use of publicly available generative AI tools and towards more secure enterprise tools or on-device tools (the latter are sometimes referred to as small language models, or SLMs; examples include GPT4All and LM Studio). SLMs with dedicated purposes may also be trained on smaller, more specific datasets. It is notable that educational institutions such as universities are beginning to provide staff with enterprise solutions, including dedicated versions of existing chatbots within protected institutional spaces where data is not shared outside the institution, or generative AI chatbots developed in-house. Examples of the former include the University of Sydney’s Cogniti; examples of the latter include the University of Michigan’s UM GPT.

Search engines > AI for search engines has been rolled out, effectively fusing search functionality and generative AI. The best-known examples are Microsoft’s Bing, linked to Copilot, and Google’s Gemini. Other AI-powered search options include the Opera browser’s Aria, while chatbots like ChatGPT and Perplexity AI can also serve as search engines. Services which search for and summarise scholarly research include Consensus, Elicit (which produces literature reviews), SciSpace, Scite.ai and Scholar AI, while Sourcely finds references to support academic papers, PaperGen promises to create referenced papers from scratch, and Sakana’s AI Scientist promises to conduct entire research projects autonomously. Some of these services may potentially create issues of academic integrity in education and research.

Productivity software > AI is also making its way into productivity software, including Microsoft’s Copilot for Microsoft 365 (covering for example Word, PowerPoint, Outlook and Teams) and Google’s Gemini for Google Workspace (briefly named Duet before changing to its current name; this covers, for example, Gmail, Docs and Slides). Another similar project can be seen in the Notion suite of productivity tools incorporating Notion AI.

Specialised & customised services > See the section on Generative AI in education below.

Ongoing developments in generative AI

Personalised agents > Work is now underway on the upgrading of the old, first-generation digital assistants with generative AI capabilities: examples include Alexa, Bixby, Google Assistant and Siri. This shift is likely to coincide with the rise of semi-autonomous personal generative AI agents that can converse naturally and control apps on smartphones or computers; in other words, we can expect to see that “AIs are breaking out of the chatbox [and] coming into our world” (Ethan Mollick, When You Give Claude a Mouse, Substack). Microsoft is emphasising the rise of ‘autonomous agents‘ – specialised agents will be available in Microsoft 365 Copilot, with users also able to create customised agents; Anthropic is talking about ‘computer use‘ by Claude; and Google has developed its own agent named Jarvis AI.; and OpenAI has announced the 2025 release of its agent called Operator.

New form factors >  In November 2023, Humane officially announced the upcoming release of its AI Pin, designed to allow hands-free interaction with AI via a lapel pin, with no need for a phone or other device. In the same month, Snapchat announced the incorporation of AI into its lenses. In February 2024, OpenAI announced a partnership with robotics company Figure, with a plan to integrate generative AI into robots; a number of other companies are also working on robotics platforms that will incorporate generative AI. Rapid advances are occurring in robotics in terms of perception of smell and touch.

Prompting generative AI

The questions, commands and comments which users enter in conversations with generative AI are known as prompts, and it is vital for these to be as detailed and accurate as possible in order to obtain useful responses. Prompt literacy, indeed, might be seen as an extension of search literacy, especially given the ongoing fusion of generative AI with search services; and both overlap with AI literacy (see below). Prompt literacy is certainly a prerequisite for the emerging professional role which has come to be called prompt engineering, but in fact all users of generative AI need to develop skills in this area. Common prompting techniques include:

• • zero-shot prompting (giving a simple question or command)
  • one-shot prompting (giving a question-response example as a model)
  • few-shot prompting (giving a few question-response examples as models)
  • role prompting (giving the AI a specific role)
  • chain of thought prompting (taking the AI through multiple steps involving reasoning)

Further details of these and other prompting techniques are available in From “Zero-Shot” To “Chain Of Thought” (Tristan Wolff/Medium, 2023), ChatGPT Guide: Use These Prompt Strategies to Maximize Your Results (Jonathan Kemper/The Decoder, 2024), and How to Write Better ChatGPT Prompts in 5 Steps (David Gewirtz/ZDNET, 2024).

A useful starting point is offered by The Rundown’s 6-Step Prompt Checklist, seen below, which helps users ensure their prompts are both specific and well contextualised:

For more information, see Master the 6-Step Prompt Formula (The Rundown, 2023). A copiable template for building good prompts can be seen at LLM Prompting on Github.Examples of effective prompts are available from a number of sites, though some such sites are now more oriented towards the generation of images, and some are moving towards freemium or fully paid models. Options include PromptHero, PromptPal, The Rundown’s 1000+ Copy-Paste ChatGPT Prompts, and Anthropic’s Prompt Library. For specific educational prompting, see the section on Generative AI in education below. Many generative AI chatbots automatically suggest at least a few helpful prompts, and they can also be asked to provide more examples.

Note that prompting is likely to become easier and more intuitive with the rollout of prompt engineering features in some major gen AI chatbots such as Anthropic’s Claude; such a feature will help users to generate appropriate prompts. Of course this does not remove the need for a meta-level awareness of how prompts operate.

Generative AI in education

Students can ask/use generative AI to:

• • explain learning points (e.g., grammar rules, maths problems, or literary themes)
  • provide examples of words, phrases or structures in use
  • find and summarise, or simplify, existing documents
  • suggest ideas for essays, projects or other tasks
  • produce first drafts of titles, task outlines or full texts
  • improve the grammar, vocabulary and style of texts
  • modify the genre and register of texts
  • offer constructive feedback on texts
  • co-generate stories in a choose-your-own-adventure style
  • engage in conversation (including role-plays) in multiple languages
  • create self-study revision questions and games
  • take on the role of a teacher or Socratic tutor
  • take on the role of a student (with students acting as teachers)

Teachers can ask/use generative AI to:

• • design step-by-step lesson plans
  • create teaching materials and handouts
  • devise assessments and rubrics
  • draft model assignments
  • generate responses with specifically planned flaws for students to identify
  • generate multiple responses to a question (which students may critique)
  • provide a first draft of feedback on student work (which students may critique)
  • build customised learning chatbots drawing on specific datasets and/or following
     specific interactional instructions
  • analyse student data to improve teaching and/or learning
  • draft student reports based on the teacher’s notes
  • draft meeting summaries based on notes

Specialised text-based services > There is a growing range of predominantly text-based generative AI software with different specialisations, much of which is relevant to education:

• • composing/editing written documents: Grammarly, Jasper, Jenni, Lex,  Magic Write (Canva), Moonbeam, Otio, Perplexity Pages, Quillbot, Rytr, Trinka, Type, Wordtune
  • summarising/paraphrasing/creating notes: Mindgrasp, Quillbot
  • asking questions of a PDF: Coral AI, ChatPDF, Sharly
  • turning notes into multiple formats (e.g., FAQs, podcasts) NotebookLM
  • translating webpages: Bilingual Reading
  • generating stories (including multimedia): DeepFiction, Depth Tale and Storly 
  • language learning (including audio): Duolingo Max, Fluently, Kippy, LangAI, Language Teacher, Speak, SpeakAI, Talkio, Univerbal, Vocalo
  • generating/interacting with historical & other characters: Character AI, Hello History
  • solving maths problems: MathSolver 
  • generating polls, quizzes & learning games: see lists on the Polling and Quizzes pages of this website

Specialised multimedia services > There is also a growing range of multimedia generative AI software for creating and/or editing different artefact types, much of which is also relevant to education:

• • images: AI Image Generator/Dream Lab (Canva), Art Remix (Google), Copilot Designer (Microsoft), Flux (Black Forest Labs), Ideogram, Imagine (Meta), DALL-E  (OpenAI) [also available through ChatGPT Plus or freely accessible through Microsoft’s Copilot], Firefly (Adobe), Freepik, Generative AI (Getty Images), Imagen AI (Google), Lexica, LogoFast (for logos), LogoMuse (for logos), Midjourney, OpenArt, Photoleap, Scenery AI, Shakker (for remixing), Stable Diffusion, Visual Electric; note that Lummi offers free stock images pre-created by generative AI
  • mind maps: EverLearn Mindmap Generator, Mapify, MyMap
  • multimodal discussion boards: see list on the Discussion Boards page of this website
  • QR codes with embedded images: see list on the QR Codes page of this website
  • voice cloning: Audiobox (Meta), LOVO,  PlayHT, Vall-E (X) (Microsoft) [research phase], Voice Engine (OpenAI) [research phase]
  • voice/video dubbing/translating: Dub AI, ElevenLabs, Translate Video
  • music: AI Jukebox, Hydra, Mix Audio, MusicGen (Meta), MusicLM (Google) [research phase], Musicfy, Stable Audio, Suno, Udio, Voicemod, Wavtool 
  • podcasting: see list on the Podcasting page of this website
  • videos: see list on the Videos page of this website (note that some image software listed above also allows video editing)
  • presentations: AI Menti Builder (Mentimeter), AI PowerPoint Maker (Plus), Decktopus, Gamma, Storydoc
  • digital storytelling/comics: see list on the Digital Storytelling page of this website
  • websites: see list on the Websites page of this website
  • AI characters/avatars/chatbots: Artflow, Character.ai, HeyGen, Synthesia, Voiceflow 
  • multiple design formats: Magic Studio (Canva)

Specialised lesson design services > Specific tools or toolsets designed to support teachers in designing lessons, materials, handouts and assessments include:

• • Brisk Teaching (Google Chrome Extension)
  • Chalkie
  • Curipod
  • Diffit
  • Education Copilot
  • Eduaide
  • EverLearns
  • Kaiden
  • Learneris 
  • Magic Design (Canva)
  • MagicSchool
  • QWiser
  • SLT AI 
  • Teacher’s Buddy 
  • TeachMateAI 
  • Teach Tappy 
  • Twee (English focus)

Socratic tutors > There are increasing numbers of generative AI Socratic tutors available, including the GPT-based Khanmigo (from the Khan Academy) and the AI tutor in Duolingo Max (from the Duolingo language learning app). Free options include Contact North’s ChatGPT-based Socratic tutor called AI Tutor Pro GROW and Google’s Socratic.

Customised tutors & services > As noted earlier, users can now build customised GPTs. More broadly, services like MindPal, Promptly and Zapier, as well as the educationally oriented FlintK12 and Mizou, allow users with little or no coding knowledge to build custom agents using generative AI, including but not limited to GPT.

Other tools > Updates on AI developments are available from The Rundown, which also maintains a list of AI Super Tools. For an extensive, searchable catalogue of AI tools, classified by tool types (e.g., AI detection, generative art, generative video, research), see Future Tools, There’s an AI for That, Toolify or Top AI Tools; for an educational list, see AI List for Educators (Denny Hammond/Google Docs); and for a research list, see AI Research Tools (Susie Macfarlane/Padlet).

For resources on AI at school level, see Common Sense Education’s Classroom Tools that Use AI. Schoolteachers might like to check out Google’s free short course on Generative AI for Educators, Code.org’s AI 101 for Teachers, Educraft’s We Used 121 AI Tools for Teachers (video) or Eric Curts’ AI Resources on his Control Alt Achieve blog. Teachers of English might like to check out the card pack from Cambridge University Press entitled Generative AI Idea Pack for English Language Teachers.

For resources on AI in higher education, see Mike Sharples’ 2023 list in UNESCO’s ChatGPT and Artificial Intelligence in Higher Education: Quick Start Guide (p. 9), the University of Western Australia’s Artificial Intelligence (AI): Overview of AI, Monash University’s AI in Education Learning Circle, and the EDUCAUSE Showcase AI … Friend or Foe? You might also like to subscribe to the Chinese University of Hong Kong’s AI in Education newsletter.

For resources on prompting AI for educational purposes, see 65+ Best ChatGPT Prompts for Teachers (Jill Baker/SplashLearn, 2024), Unlocking Creative Lesson Plans with ChatGPT’s Prompts (Niall McNulty, 2023), and AI for Education’s GenAI Prompt Library for Educators. 

Note that a comprehensive understanding of generative AI requires digital literacies such as code literacy, and its subliteracies technological and AI literacy. AI literacy overlaps with other digital literacies such as text, multimodal and particularly search and prompt literacy, as well as broader literacies such as ethical and critical literacy. Google has recently committed to supporting AI literacy development for young people, and many of the educational resources listed above are relevant to this area. Relevant frameworks can be found in UNESCO’s twin 2024 guides, AI Competency Framework for Teachers and AI Competency Framework for Students.

Generative AI in assessment

With the rise of generative AI, it is more important than ever for educators to be assessing students at the higher levels of Bloom’s Revised Taxonomy, ensuring that students go beyond remembering and understanding to applying, analysing, evaluating, and especially creating. Indeed, given the role of generative AI in today’s workplaces – where it can provide first drafts of standardised emails, weather or sports reports, legal opinions, coding, and multimedia advertising or architectural designs – and given its likely growing role in the future, it is appropriate for educators to assess students on their ability to engage in productive human-AI partnerships, where they improve on suggestions or drafts created by AI, or use AI to give feedback on, and help them hone, their own initial work.

For one approach to the continuum of possible AI uses in assessments, see Leon Furze’s 2023 The AI Assessment Scale: Version 2, and for a consideration of assessments involving humans co-creating with AI, see Danny Liu and Adam Bridgeman’s 2023 ChatGPT is Old News: How Do We Assess in the Age of AI Writing Co-pilots?. See also Monsha AI’s 2024 7 Strategies for Redesigning Assessment in Response to Artificial Intelligence. For guidance on AI and assessment in higher education, see  TEQSA’s 2023 Assessment Reform for the Age of Artificial Intelligence, and  the aforementioned Monash University AI in Education Learning Circle.

Certainly, in all cases where AI is used, it must be acknowledged. Because each user of ChatGPT (or any other generative AI) may receive different answers at different times, the standard APA in-text reference follows this format: ‘ChatGPT, personal communication, [date]’. A similar referencing approach can be adopted with other AI chatbots. Note, however, that this will become rather more complex as AI is increasingly embedded into productivity tools.

Issues with generative AI

Pedagogical issues with generative AI include the need for prompt literacy (see above); the danger of hallucinations, where tools such as ChatGPT invent answers and even references (see Vectara’s regularly updated Hallucination Leaderboard for an overview of the percentage of hallucinations created by different generative AI tools); and issues around student plagiarism and creativity, with a strong implication that educators need to develop learning designs at the higher levels of the SAMR, T3 and PICRAT models and set assessments at the higher levels of Bloom’s Taxonomy. One of the co-creators of the well-known TPACK framework for teacher development, Punya Mishra, has suggested that TPACK can encompass generative AI.

Malevolent uses of AI include the creation of deepfakes to misrepresent people’s views or actions, including fake, sexually explicit materials for the purposes of exploitation or extortion; for more on this, see the Digital Safety & Wellness page of this website.

Broader issues include bias which is due, amongst other things, to the historical datasets on which generative AI is trained (see the work of Emily Bender, Timnit Gebru and colleagues, and for a different perspective see also Maxim Lott’s Tracking AI: Monitoring Bias in Artificial Intelligence Chatbots); the black box nature of the technology, with even its programmers unable to say how it reaches its conclusions and generates its output; the unpaid labour of users whose freely shared online content is monetised by generative AI; and the extractive politics (see the work of Kate Crawford) and environmental racism (see again the work of Emily Bender, Timnit Gebru and colleagues) behind the development of generative AI, which exploits both human labour and planetary resources. An overview of over 700 risks is presented in the MIT AI Risk Repository.

In July 2023, the Frontier Model Forum was launched by Anthropic, Google, Microsoft and Open AI to promote safe and responsible use of AI and to help leverage AI to address societal challenges. In December 2023, the AI Alliance was launched by IBM, Meta and other partners to also promote open, safe and responsible AI. In February 2024, the US National Institute of Standards and Technology announced the creation of the AI Safety Institute Consortium (AISIC), consisting of a large number of key technology companies and institutions. In March 2024, the European Union introduced the world’s first major Artificial Intelligence Act, designed to regulate AI development (it was fully approved in May 2024). Also in March 2024, the United Nations General Assembly adopted a resolution to promote safe development of AI. In June 2024, the Safe Superintelligence lab was co-founded by Ilya Sutskever, former Chief Scientist at OpenAI. In September 2024, Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law, the first internationally binding treaty of its kind, was signed by the EU, UK, US and a number of other countries. 

Last update: November 2024.