Wikipedia

AI in education

Artificial intelligence is defined as “systems which display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals”[1]. Artificial intelligence in education (aied) is a generic term[2], applied to a disperate collection of fields, bundled together.[3] Including anthropomorphism, generative artificial intelligence, data-driven decision-making, ai ethics, classroom surveillance, data-privacy and Ai Literacy.[4]

Overview

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There is not a single lens with which to understand Ai in education. At least three dominant paradigms have been suggested. Firstly the transmission paradigm, where Ai systems represent a conduit for personalizing information. Secondly, the coordination paradigm, where Ai is the supporter of student's knowledge construction. Alternately there is the leadership model, where students take agency and make choices about their learning (with or without ai)[5][6]

Emerging Perspectives

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Those who see Ai as a conduit for knowledge are comfortable with the idea of machine’s reasoning or having hallucinations. While those who are sceptics, recognize the cultivated “closed-off imaginative spaces” that big tech has captured, see how big tech’s discourse limits critical thought and discussions about these computational systems.[7] Resistors often take a principled response to Ai. A refusal to accept the marketing metaphors of the “artificial intelligence” package is designed to create trust while hiding corporate business practices that are exploitative and extractive.[8]

This complex social, cultural, and material assemblage should be seen in its geo-political context. It is likely that Ai systems will be shaped by different policy or economic imperatives which will influence the construction, legitimation and use of this assemblage in an education setting.[9]

The AI in education community

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The Ai in education community has grown rapidly in the global north.[10] Currently, there is much hype from venture capital, big tech and convinced open educationalists. Ai in education is a contested terrain. Some educationalists believe that Ai will remove the obstacle of "access to expertise”.[11] Others claim that education will be revolutionised with machines and their ability to understand natural language.[12] While others are exploring how LLM’s “reasoning” might be improved.[13] While in the global south, others see the Ai's data processing and monitoring as a misguided attempt to address colonialism and apartheid, that that has inadvertently re-enforced a neo-liberal approach to education.[14]

Algorithms effects on education

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Ai companies that focus on education, are currently preoccupied with Generative artificial intelligence (GAI), although data science and data analytics is another popular educational theme. At present, there is little scientific consensus on what Ai is or how to classify and sub-categorize Ai[15][16] This has not hampered the growth of Ai in education systems, which are gathering data and then optimising models.

Ai offers scholars and students automatic assessment and feedback, predictions, instant machine translations, on-demand proof-reading and copy editing, intelligent tutoring or virtual assistants.[10] The "generative-AI supply chain",[17] brings conversational coherence to the classroom, and automates the production of content.[18] Using categorisation, summaries and dialogue, Ai "intelligence" or "authority" is reinforced through anthropomorphism and the Eliza effect.

Framing education

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Educational technology can be a powerful and effective assistant in a suitable setting. Computer companies are constantly updating their technology products. Some educationalists have suggested that Ai might automate procedural knowledge and expertise[19] or even match or surpass human capacities on cognitive tasks. They advocate for the integration of AI across the curriculum and the development of AI Literacy.[20] With higher education facilities finding themselves with an opportunity to create a path for themselves and their students by creating guidelines so that AI can incorporated into their curriculum.[21] Others are more skeptical as AI faces an ethical challenge,[22] where "fabricated responses" or "inaccurate information", politely referred to as “hallucinations” [19] are generated and presented as fact. Some remain curious about societies tendency to put their faith in engineering achievements, and the systems of power and privilege[23] that leads towards determinist thinking.[24] While others see copyright infringement[25][26][17] or the introduction of harm, division and other social impacts, and advocate resistance to Ai.[27] Evidence is mounting that Ai written assessments are undetectable, which poses serious questions about the academic integrity of university assessments.[28]

Tokens, text and hallucinations

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Large language models (LLMs) take text as input data and then generate output text.[29] LLMs are generated from billions of words and code that has been web-scraped by Ai companies or researchers. LLM are often dependent on a huge text corpus that is extracted, sometimes without permission. LLMS are feats of engineering, that see text as tokens. The relationships between the tokens, allows LLM to predict the next word, and then the next, thus generating a meaningful sentence that has an appearance of thought and interactivity. This massive dataset creates a statistical reasoning machine,[30] that does pattern recognition.[31] The LLM examines the relationships between tokens, generates probable outputs in response to a prompt, and completes a defined task, such as translating, editing, or writing. The output that is presented is a smoothed collection of words,[32] that is normalized and predictable. However, the text corpora that LLMs draw on can be problematic, as outputs will reflect their stereotypes or biases of the people or culture whose content has been digitized.[33] The confident, but incorrect outputs are termed “hallucinations”.[34] These plausible errors are not malfunctions but a consequence of the engineering decisions that inform the large language model.[35] "Guardrails" offer to act as validators of the LLM output, prevent these errors, and safeguard accuracy[36] There are no fixes[37][38] for so-called "hallucinations", the "factually incorrect or nonsensical information that seems plausible[39] Translation, summarization, information retrieval, conversational interactions are some of the complex language tasks that machines are expected to handle.[40]

Socio-technical imaginaries

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The benefits of multilingualism, grammatically correct sentences or statistically probable texts written about any topic or domain are clear to those who can afford software as a service (SaaS). In edtech, there is a recurrent theme, that “emerging technologies”[41] will transform education.[42] Whether it be radio, TV, PC computers, the internet, interactive whiteboards, social media, mobile phones or tablets. New technologies generate a socio technical imaginary (STI) that offer's society, a shared narrative[43] and a collective vision for the future.[44] Improvements in natural language processing and computational linguistics have re-enforced assumptions that underlie this “emerging technology” STI. Ai is not an emerging technology, but an “arrival technology”[45] Ai appears to understand instructions and can generate human-like responses.[46] Behaving as a companion for many in a lonely and alienated world.[47] While also creating a “jagged technology frontier”,[48] where Ai is both very good and terribly bad at very similar tasks.[45]

Public goods vs venture capital

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At first glance, artificial intelligence in education offers pertinent technical solutions to address future education needs.[10] Ai champions envision a future where machine learning and artificial intelligence might be applied in writing, personalization, feedback or course development. The growing popularity of AI, is especially apparent to many who have invested in higher education in the past decade.[10] Critical skeptics on the other hand, are wary of rhetoric that presents technology as solution. They point out that in public services, like education, human and algorithmic decision systems should be approached with caution.[49] Post digital scholars and sociologists are more cautious about any techno-solutions, and have warned about the dangers of building public systems around alchemy [49] or stochastic parrots. They argue that there are multiple costs that accompany LLMs, including dangerous biases the potential for deception, and environmental costs[50] The AI curious are aware of how cognitive activity has become commodified. They see how education has been transformed into a “knowledge business” where items are traded, bought, or sold.[51] African hyper scalers, venture capital and vice chancellors[52] are punting the Fourth Industrial Revolution. with the prospect of billions earmarked for South African. Data Centers,[53] such as Teraco Data Environments, Vantage Data Centre,[15] Africa Data Centres[18] NTT /Dimension_Data,[12] carefully avoiding being accused of monopoly practices[54]

AI resilient graduates

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AI has co-existed comfortably between academia and industry for years.[55] The terrain is shifting and currently Ai research in the global north has computing power, large datasets, and highly skilled researchers. Power is shifting away from students and academics toward corporations and venture capitalists.[56] Graduates from universities in dominant cultures, where there are high levels of digitisation, need to become AI-resilient. Graduates from the majority world also need to value their own process of knowledge construction, resist the lure of normalisation and see Ai for what it is, another form of enclosure, and start blogging. [opinion] Graduates from both the global north and the majority of the world need to be able to critique AI output, become familiar with ithe processes of technical change,[57] and let their own studies and intellectual life guide their working futures.[18]

Prominent digital education researchers and works on artificial intelligence in education

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Critical Sceptics Curious Practitioners Convinced Experts Committed Champions
Ben Williamson[58] Lance Eton[59] David Wiley [60]
Helen Beetham[61] Anna Mills[62] Stephen Downs
Bryan Alexander

It is human to categorize and then gravitate towards the published works of authors with whom we share similar thoughts and familiar ideas.[63] It is easy for champions or critics within the artificial intelligence in education community to ignore those in the opposite camps, instead of discussing differences of opinion and discovering matters of agreement.[64] The above spectrum is another "skewer", intended to introduce unfamiliar voices and their research to a wider readership, and work with the backlash. [opinion]

References

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  1. ^ Sheikh, Haroon; Prins, Corien; Schrijvers, Erik (2023), Sheikh, Haroon; Prins, Corien; Schrijvers, Erik (eds.), "Artificial Intelligence: Definition and Background", Mission AI: The New System Technology, Cham: Springer International Publishing, pp. 15–41, doi:10.1007/978-3-031-21448-6_2, ISBN 978-3-031-21448-6, retrieved 2024-11-18
  2. ^ Ifenthaler, Dirk; Majumdar, Rwitajit; Gorissen, Pierre; Judge, Miriam; Mishra, Shitanshu; Raffaghelli, Juliana; Shimada, Atsushi (2024-06-04). "Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners". Technology, Knowledge and Learning. doi:10.1007/s10758-024-09747-0. ISSN 2211-1670.
  3. ^ Holmes, Wayne (2023). "AIED—Coming of Age?". International Journal of Artificial Intelligence in Education. 34: 1–11. doi:10.1007/s40593-023-00352-3.
  4. ^ Knaus, Thomas (2024). "Künstliche Intelligenz und Pädagogik – ein Plädoyer für eine Perspektiverweiterung". LBZM. 24: 4–8. doi:10.21240/lbzm/24/11.
  5. ^ Ouyang, Fan; Jiao, Pengcheng (2021-01-01). "Artificial intelligence in education: The three paradigms". Computers and Education: Artificial Intelligence. 2: 100020. doi:10.1016/j.caeai.2021.100020. ISSN 2666-920X.
  6. ^ Griffiths, Dai; Frías-Martínez, Enrique; Tlili, Ahmed; Burgos, Daniel (2024). "A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination". International Journal of Interactive Multimedia and Artificial Intelligence. 8 (5): 15. doi:10.9781/ijimai.2024.02.008. ISSN 1989-1660.
  7. ^ Jr, Edward Ongweso (2024-11-04). "AI, slavery, surveillance, and capitalism". The Tech Bubble. Retrieved 2024-11-06.
  8. ^ Sano-Franchini, Jennifer, Megan McIntyre, and Maggie Fernandes. “Refusing GenAI in Writing Studies: A Quickstart Guide.” Refusing Generative AI in Writing Studies. Nov. 2024. refusinggenai.wordpress.com
  9. ^ Eynon, Rebecca; Young, Erin (January 2021). "Methodology, Legend, and Rhetoric: The Constructions of AI by Academia, Industry, and Policy Groups for Lifelong Learning". Science, Technology, & Human Values. 46 (1): 166–191. doi:10.1177/0162243920906475. ISSN 0162-2439.
  10. ^ a b c d Crompton, Helen; Burke, Diane (2023-04-24). "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. ISSN 2365-9440.
  11. ^ Wiley, David (2023). "The Near-term Impact of Generative AI on Education, in One Sentence – improving learning". opencontent.org. Archived from the original on 2024-08-29. Retrieved 2024-08-29.
  12. ^ a b Wang, Alex; Pruksachatkun, Yada; Nangia, Nikita; Singh, Amanpreet; Michael, Julian; Hill, Felix; Levy, Omer; Bowman, Samuel R. (2019-12-08), "SuperGLUE: a stickier benchmark for general-purpose language understanding systems", Proceedings of the 33rd International Conference on Neural Information Processing Systems, Red Hook, NY, USA: Curran Associates Inc., pp. 3266–3280, arXiv:1905.00537, retrieved 2024-08-22
  13. ^ Havrilla, Alex; Raparthy, Sharath; Nalmpantis, Christoforus; Dwivedi-Yu, Jane; Zhuravinskyi, Maksym; Hambro, Eric; Raileanu, Roberta (2024). "GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements". arXiv:2402.10963 [cs.CL].
  14. ^ Prinsloo, Paul (2020-05-18). "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. ISSN 1356-2517.
  15. ^ a b Knoedler, Leonard; Knoedler, Samuel; Allam, Omar; Remy, Katya; Miragall, Maximilian; Safi, Ali-Farid; Alfertshofer, Michael; Pomahac, Bohdan; Kauke-Navarro, Martin (2023-10-30). "Application possibilities of artificial intelligence in facial vascularized composite allotransplantation—a narrative review". Frontiers in Surgery. 10. doi:10.3389/fsurg.2023.1266399. ISSN 2296-875X. PMC 10646214. PMID 38026484.
  16. ^ Grobelnik, Marko; Perset, Karine; Russell, Stuart (March 6, 2024). "What is AI? Can you make a clear distinction between AI and non-AI systems?". oecd.ai. Retrieved 28 June 2024.
  17. ^ a b Lee, Katherine; Cooper, A. Feder; Grimmelmann, James (2023). "Talkin' 'Bout AI Generation: Copyright and the Generative AI Supply Chain". SSRN Electronic Journal. arXiv:2309.08133. doi:10.2139/ssrn.4523551. ISSN 1556-5068.
  18. ^ a b c "What price your 'AI-ready' graduates?". 7 August 2024.
  19. ^ a b Collins, Harry (2024-05-18). "Why artificial intelligence needs sociology of knowledge: parts I and II". AI & Society. doi:10.1007/s00146-024-01954-8. ISSN 1435-5655.
  20. ^ Southworth, Jane; Migliaccio, Kati; Glover, Joe; Glover, Ja'Net; Reed, David; McCarty, Christopher; Brendemuhl, Joel; Thomas, Aaron (2023). "Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy". Computers and Education: Artificial Intelligence. 4. doi:10.1016/j.caeai.2023.100127.
  21. ^ "Revolutionizing Education: The Impact of AI on Learning and Teaching". www.pearson.com. 2023-11-08. Retrieved 2024-09-30.
  22. ^ Knaus, Thomas (2024). "Künstliche Intelligenz und Pädagogik – ein Plädoyer für eine Perspektiverweiterung". LBZM. 24: 15–26. doi:10.21240/lbzm/24/11.
  23. ^ Birhane, Abeba; McGann, Marek (2024-11-01). "Large models of what? Mistaking engineering achievements for human linguistic agency". Language Sciences. 106: 101672. arXiv:2407.08790. doi:10.1016/j.langsci.2024.101672. ISSN 0388-0001.
  24. ^ "Resisting Deterministic Thinking". 5 April 2023.
  25. ^ "Generative AI is a marvel. Is it also built on theft?". The Economist. ISSN 0013-0613. Retrieved 2024-08-20.
  26. ^ "Researchers tested leading AI models for copyright infringement using popular books, and GPT-4 performed worst". CNBC. 6 March 2024.
  27. ^ McQuillan, Dan (2022). Resisting AI: An Anti-fascist Approach to Artificial Intelligence. Bristol University Press
  28. ^ Scarfe, Peter; Watcham, Kelly; Clarke, Alasdair; Roesch, Etienne (2024-06-26). "A real-world test of artificial intelligence infiltration of a university examinations system: A "Turing Test" case study". PLOS ONE. 19 (6): e0305354. Bibcode:2024PLoSO..1905354S. doi:10.1371/journal.pone.0305354. ISSN 1932-6203. PMC 11206930. PMID 38923941.
  29. ^ McCoy, R. Thomas; Yao, Shunyu; Friedman, Dan; Hardy, Matthew; Griffiths, Thomas L. (2023-09-24). "Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve". arXiv:2309.13638 [cs.CL].
  30. ^ "Artificial Intelligence (AI) vs. Difference". Law Office of Lainey Feingold. 2024-08-20. Retrieved 2024-08-21.
  31. ^ Singh, Chetanpal (2023-04-11). "Machine Learning in Pattern Recognition". European Journal of Engineering and Technology Research. 8 (2): 63–68. doi:10.24018/ejeng.2023.8.2.3025. ISSN 2736-576X.
  32. ^ "When Should We Trust AI? Magic-8-Ball Thinking and AI Hallucinations". Nielsen Norman Group. Retrieved 2024-08-21.
  33. ^ Knaus, Thomas (2023). "Künstliche Intelligenz und Bildung - Was sollen wir wissen? Was können wir tun? Was dürfen wir hoffen? Und was ist diese KI? Ein kollaborativer Aufklärungsversuch". LBZM. 23: 16–19. doi:10.25656/01:27904.
  34. ^ Knaus, Thomas (2023). "Künstliche Intelligenz und Bildung - Was sollen wir wissen? Was können wir tun? Was dürfen wir hoffen? Und was ist diese KI? Ein kollaborativer Aufklärungsversuch". LBZM. 23: 16–19. doi:10.25656/01:27904.
  35. ^ "ChatGPT Isn't 'Hallucinating.' It's Bullshitting". 6 April 2023.
  36. ^ "Inside Guardrails AI: A New Framework for Safety, Control and Validation of LLM Applications | by Jesus Rodriguez | Medium".
  37. ^ "Tech experts are starting to doubt that ChatGPT and A.I. 'hallucinations' will ever go away: 'This isn't fixable'".
  38. ^ "Google CEO Sundar Pichai says 'hallucination problems' still plague A.I. Tech and he doesn't know why".
  39. ^ Xu, Ziwei; Jain, Sanjay; Kankanhalli, Mohan (2024). "Hallucination is Inevitable: An Innate Limitation of Large Language Models". arXiv:2401.11817 [cs.CL].
  40. ^ Naveed, Humza; Khan, Asad Ullah; Qiu, Shi; Saqib, Muhammad; Anwar, Saeed; Usman, Muhammad; Akhtar, Naveed; Barnes, Nick; Mian, Ajmal (2024-04-09), A Comprehensive Overview of Large Language Models, arXiv:2307.06435
  41. ^ "A definition of emerging technologies for education". 18 November 2008.
  42. ^ 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 (2019-10-01). "Critical Imaginaries and Reflections on Artificial Intelligence and Robots in Postdigital K-12 Education". Postdigital Science and Education. 1 (2): 427–445. doi:10.1007/s42438-019-00046-x. ISSN 2524-4868.
  43. ^ Tidwell, Jacqueline Hettel; Tidwell, Abraham S. D. (2018-05-01). "Energy ideals, visions, narratives, and rhetoric: Examining sociotechnical imaginaries theory and methodology in energy research". Energy Social Science. 39: 103–107. Bibcode:2018ERSS...39..103T. doi:10.1016/j.erss.2017.11.005. ISSN 2214-6296. OSTI 1894586.
  44. ^ Jasanoff, Sheila; Kim, Sang-Hyun (2015). Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power. University of Chicago Press. doi:10.7208/chicago/9780226276663.001.0001. ISBN 978-0-226-27652-6.
  45. ^ a b Klopfer, Eric; Reich, Justin; Abelson, Hal; Breazeal, Cynthia (2024-03-27). "Generative AI and K-12 Education: An MIT Perspective". An MIT Exploration of Generative AI. doi:10.21428/e4baedd9.81164b06.
  46. ^ Menon, Devadas; Shilpa, K (2023-11-01). ""Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model". Heliyon. 9 (11): e20962. Bibcode:2023Heliy...920962M. doi:10.1016/j.heliyon.2023.e20962. ISSN 2405-8440. PMC 10623159. PMID 37928033.
  47. ^ Jacobs, Kerrin Artemis (2024-03-05). "Digital loneliness—changes of social recognition through AI companions". Frontiers in Digital Health. 6. doi:10.3389/fdgth.2024.1281037. ISSN 2673-253X. PMC 10949182. PMID 38504806.
  48. ^ Dell'Acqua, Fabrizio; McFowland, Edward; Mollick, Ethan R.; Lifshitz-Assaf, Hila; Kellogg, Katherine; Rajendran, Saran; Krayer, Lisa; Candelon, François; Lakhani, Karim R. (2023). "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality". SSRN Electronic Journal. doi:10.2139/ssrn.4573321. ISSN 1556-5068.
  49. ^ a b "Don't Believe Every AI You See". New America. Retrieved 2024-08-20.
  50. ^ Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-01). "On the Dangers of Stochastic Parrots: Can Language Models be Too Big? 🦜". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7.
  51. ^ "The commodification of education and the (generative) AI-induced scam-like culture".
  52. ^ "Five new universities coming to South Africa – BusinessTech".
  53. ^ Riley, B., & Bruno, P. (2024). Education hazards of generative AI. https://www.cognitiveresonance.net/resources.html
  54. ^ "Competition Commission slapping Microsoft with monopoly complaint in South Africa – MyBroadband".
  55. ^ Ahmed, Nur; Wahed, Muntasir; Thompson, Neil C. (2023). "The growing influence of industry in AI research". Science. 379 (6635): 884–886. Bibcode:2023Sci...379..884A. doi:10.1126/science.ade2420. PMID 36862769.
  56. ^ Stilgoe, Jack (2024). "AI has a democracy problem. Citizens' assemblies can help". Science. 385 (6711): eadr6713. Bibcode:2024Sci...385r6713S. doi:10.1126/science.adr6713. PMID 39172823.
  57. ^ Postman, N. (1998, March). Five things we need to know about technological change [Conference presentation]. Denver, CO, United States. https://www.cs.ucdavis.edu/~rogaway/classes/188/materials/postman.pdf
  58. ^ "artificial intelligence – Page 2". code acts in education. Retrieved 2024-09-09.
  59. ^ Eaton, Lance. "AI + Education = Simplified | Lance Eaton | Substack". aiedusimplified.substack.com. Retrieved 2024-09-09.
  60. ^ "artificial intelligence – improving learning". opencontent.org. Retrieved 2024-09-09.
  61. ^ Beetham, Helen. "imperfect offerings | Helen Beetham | Substack". helenbeetham.substack.com. Retrieved 2024-09-09.
  62. ^ "AI Text Generators: Sources to Stimulate Discussion Among Teachers".
  63. ^ Holmes, Wayne (2024-03-01). "AIED—Coming of Age?". International Journal of Artificial Intelligence in Education. 34 (1): 1–11. doi:10.1007/s40593-023-00352-3. ISSN 1560-4306.
  64. ^ Selwyn, Neil (2024-03-01). "Constructive Criticism? Working with (Rather than Against) the AIED Back-Lash". International Journal of Artificial Intelligence in Education. 34 (1): 84–91. doi:10.1007/s40593-023-00344-3. ISSN 1560-4306.

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