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Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence to create learning environments.[1]

Considerations in the field include data-driven decision-making, AI ethics, data privacy and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias.[2]

History

Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence.

In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois.[3]

In the 1970s and 1980s, intelligent tutoring systems (ITS) were being adapted for classroom instruction.

The International Artificial Intelligence in Education Society was founded in 1993.[4]

Coinciding with the AI boom of the 2020s, the use of large language models in the global north has been promoted and funded by venture capital and big tech.[citation needed] Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies.[5] These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious.[6]

Theory

AIEd applies theory from education studies, machine learning, and related fields.

A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration.[7]

Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems:

  • AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner’s feedback.
  • AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner’s feedback through, for example, natural language processing, wherein AI can support knowledge construction.
  • AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback.[8]

Some scholars place AI in education within a socio-technical framework.[9] This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media.[10]

The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway‘s concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction.[11]

AI agents have been trained on biased datasets, and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced.[12] AI’s data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities.[13][14]

Applications

See also: Intelligent tutoring system

Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading exam question generation and copy editing, or as virtual assistants.[15][16]

Emotional AI in education is the study and development of systems that can detect learners’ emotions or provide emotional support in learning.[17]

Usage

Schools and educators

Following the release of ChatGPT in November 2022, some schools and large school districts blocked access to the site and issued warnings that the use of such tools would be seen as cheating.[18]

Governmental and non-governmental organizations such as UNESCO, Article 4 of the European Union‘s AI Act, and the U.S. Department of Education have published reports advocating for specific AIEd approaches.[19][20][21] National higher-education bodies have also published guidance on generative AI, including Ireland’s Higher Education Authority, which issued a policy framework for higher education teaching and learning in December 2025.[22] In 2024, UNESCO released updated global guidance for generative AI in education, emphasizing ethical use, teacher training, and data protection to ensure responsible integration of AI tools in learning environments.[23] According to Taso (2025), policy implementation in higher education is interpreted and enacted differently by various organizations. These decentralized policies can lead to inconsistent enforcement and confusion among students regarding what constitutes acceptable use, with the burden of ethical navigation falling on individual teachers and students.[24]

AI integration in classrooms has created new forms of invisible labour for educators, who must navigate ambiguous policies, redesign assessments to be AI-resilient, and adjudicate potential academic integrity violations. The use of AI detection tools has also been criticised for creating an adversarial relationship between students and institutions, where students may be falsely accused of misconduct based on probabilistic software.[24]

AIEd advocates say that efforts should be made towards increasing global accessibility and training educators to serve underprivileged areas.[2][25]

According to NPR in a poll conducted with Ipsos in 2026, of 545 K-12 educators in the US, “nearly 3-in-4 believe AI has bigger implications for education than past innovations like the internet or computers.”[26] Around half of educators said they had used AI for work tasks. More than half said that students did not use AI in the classroom at all. Around half said it interfered with students ability to learn critical thinking skills, and that it was a shortcut for students to avoid work. Almost 80% of respondents said that schools should include teaching responsible AI usage as part of their curricula.[26]

Students

Reliance on generative AI has been linked with reduced academic self-esteem and performance, and heightened learned helplessness.[27] Algorithm errors and hallucinations are common flaws in AI agents, making them less trustworthy and reliable.[2][28]

According to a 2025 survey from Inside Higher Ed, 85% of higher education students use generative AI technology in some way, with 25% using AI to complete assignments for them. The most common reason cited for using AI to cheat was pressure to get high grades. 97% of students wanted some form of action from schools on the threat to academic integrity caused by AI, with the most popular options being clearer policies and more education about ethical uses of AI.[29]

In September 2025, The Atlantic published an op-ed from a high school senior arguing that the normalization of AI cheating was eroding critical thinking, academic integrity, creativity, and the shared student experience.[30]

See also

References

  1. ^ Chen, Lijia; Chen, Pingping; Lin, Zhijian (2020). “Artificial Intelligence in Education: A Review”. IEEE Access. 8: 75264–75278. Bibcode:2020IEEEA…875264C. doi:10.1109/ACCESS.2020.2988510.
  2. ^ a b c Nguyen, Andy; Ngo, Ha Ngan; Hong, Yvonne; Dang, Belle; Nguyen, Bich-Phuong Thi (April 2023). “Ethical principles for artificial intelligence in education”. Education and Information Technologies. 28 (4): 4221–4241. doi:10.1007/s10639-022-11316-w. PMC 9558020. PMID 36254344.
  3. ^ Communications, Grainger Engineering Office of Marketing and. “PLATO”. grainger.illinois.edu. Retrieved 2025-05-07.
  4. ^ “International AIED Society”. iaied.org. Retrieved 2025-09-29.
  5. ^ Archie, Ayana (2025-08-06). “So long, study guides? The AI industry is going after students”. NPR. Retrieved 2025-10-10.
  6. ^ Shroff, Lila (2025-08-12). “The AI Takeover of Education Is Just Getting Started”. The Atlantic. Retrieved 2025-10-09.
  7. ^ Zawacki-Richter, Olaf; Marín, Victoria I.; Baecher, Laura (2019). “Systematic review of research on artificial intelligence applications in higher education”. International Journal of Educational Technology in Higher Education. 16 (39): 1–27. doi:10.1186/s41239-019-0171-0. hdl:10459.1/85324.
  8. ^ Ouyang, Fan; Jiao, Pengcheng (2021). “Artificial intelligence in education: The three paradigms”. Computers and Education: Artificial Intelligence. 2 100020. Elsevier. doi:10.1016/j.caeai.2021.100020.
  9. ^ Beck, Silke; Jasanoff, Sheila; Stirling, Andy; Polzin, Christine (2021). “The governance of sociotechnical transformations to sustainability”. Current Opinion in Environmental Sustainability. 49. Elsevier: 143–152. Bibcode:2021COES…49..143B. doi:10.1016/j.cosust.2021.04.010.
  10. ^ Hrastinski, Stefan; Olofsson, Anders D.; Arkenback, Charlotte; Ekström, Sara; Ericsson, Elin; Fransson, Göran; Jaldemark, Jimmy; Ryberg, Thomas; Öberg, Lena-Maria; Fuentes, Ana; Gustafsson, Ulrika; Humble, Niklas; Mozelius, Peter; Sundgren, Marcus; Utterberg, Marie (October 2019). “Critical Imaginaries and Reflections on Artificial Intelligence and Robots in Postdigital K-12 Education”. Postdigital Science and Education. 1 (2). Springer: 427–445. doi:10.1007/s42438-019-00046-x.
  11. ^ Tsao, Jack; Heinrichs, Danielle H.; Camit, Michael (2025-11-02). “Artificial intelligence and epistemic interoperability: towards a sympoietic approach”. Discourse: Studies in the Cultural Politics of Education. Taylor & Francis: 1–13. doi:10.1080/01596306.2025.2579702. ISSN 0159-6306.
  12. ^ Aaron, Lynn; Abbate, Santina; Allain, Nicola Marae; Almas, Bridget; Fallon, Brian; Gavin, Dana; Gordon, C. (Barrett); Jadamec, Margarete; Merlino, Adele (2024), “AI Bias Concerns”, Optimizing AI in Higher Education, SUNY FACT² Guide, Second Edition, State University of New York Press, pp. 5–9, ISBN 979-8-8558-0235-1, JSTOR jj.20522984.11{{citation}}: CS1 maint: work parameter with ISBN (link)
  13. ^ Prinsloo, Paul (18 May 2020). “Data frontiers and frontiers of power in (higher) education: a view of/from the Global South”. Teaching in Higher Education. 25 (4): 366–383. doi:10.1080/13562517.2020.1723537.
  14. ^ Selwyn, Neil (2022). “The future of AI and education: Some cautionary notes”. European Journal of Education. 57 (4): 620–631. doi:10.1111/ejed.12532.
  15. ^ Crompton, Helen; Burke, Diane (24 April 2023). “Artificial intelligence in higher education: the state of the field”. International Journal of Educational Technology in Higher Education. 20 (1) 22. doi:10.1186/s41239-023-00392-8.
  16. ^ Ahmed, Ayla; Kerr, Ellen; O’Malley, Andrew (2025-02-25). “Quality assurance and validity of AI-generated single best answer questions”. BMC Medical Education. 25 (1) 300. doi:10.1186/s12909-025-06881-w. ISSN 1472-6920. PMC 11854382. PMID 40001164.
  17. ^ Zhang, Heng; Liu, Yuhan; Jiang, Meilin; Chen, Juanjuan; Wang, Minhong; Paas, Fred (2025-11-15). “Emotional Artificial Intelligence in Education: A Systematic Review and Meta-Analysis”. Educational Psychology Review. 37 (4): 106. doi:10.1007/s10648-025-10086-4. ISSN 1573-336X.
  18. ^ Heaven, Will Douglas (6 April 2023). “ChatGPT is going to change education, not destroy it”. MIT Technology Review. Retrieved 28 May 2026.
  19. ^ “Guidance for generative AI in education and research”. UNESCO. Archived from the original on 2025-10-03. Retrieved 2025-10-10.
  20. ^ “Artificial Intelligence and the Future of Teaching and Learning” (PDF). Office of Educational Technology. May 2023.
  21. ^ “AI Literacy – Questions & Answers”. European Commission | Shaping Europe’s digital future. Retrieved 2025-10-10.
  22. ^ Generative AI in Higher Education Teaching & Learning: Policy Framework (PDF) (Report). Higher Education Authority. December 2025. Retrieved 10 May 2026.
  23. ^ “Guidance for generative AI in education and research”. Archived from the original on 2025-10-11. Retrieved 2025-10-26.
  24. ^ a b Tsao, Jack (2025). “Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers”. Computers and Education: Artificial Intelligence. 9 100496. Elsevier. doi:10.1016/j.caeai.2025.100496. ISSN 2666-920X.
  25. ^ Miao, Fengchun; Holmes, Wayne; Huang, Ronghuai; Zhang, Hui (2021). AI and education: A guidance for policymakers. UNESCO Publishing. ISBN 978-92-3-100447-6. OCLC 1262785646.[page needed]
  26. ^ a b Gaines, Lee V. (5 June 2026). “Most K-12 teachers say AI’s impact on education will eclipse the internet or computers”. NPR. Retrieved 5 June 2026.
  27. ^ Azeem, Sundas; Abbas, Muhammad (2025). “Personality correlates of academic use of generative artificial intelligence and its outcomes: does fairness matter?”. Education and Information Technologies. 30 (13): 18131–18155. doi:10.1007/s10639-025-13489-6.
  28. ^ Cotton, Debby; Cotton, Peter; Shipway, Reuben (2023). “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT”. Innovations in Education and Teaching International. 61 (2): 228–239. doi:10.1080/14703297.2023.2190148.
  29. ^ Flaherty, Colleen (2025-08-29). “How AI Is Changing—Not ‘Killing’—College”. Inside Higher Ed. Retrieved 2025-10-10.
  30. ^ Rosario, Ashanty (2025-09-03). “I’m a High Schooler. AI Is Demolishing My Education”. The Atlantic. Retrieved 2025-10-09.