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Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research
Open Univ Shantou, Shantou, Peoples R China..
KTH, Skolan för industriell teknik och management (ITM), Lärande, Digitalt lärande.ORCID-id: 0000-0002-9984-6561
NTNU, Trondheim, Norway..
2025 (engelsk)Inngår i: TechTrends, ISSN 8756-3894Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its potential use and future impact on education. Adopting the methodology of collective writing as an inquiry, this study reports on the participating educators' perceived grey areas (i.e. issues that are unclear and/or controversial) and recommendations on future research. The grey areas reported cover decision-making on the use of GenAI, AI ethics, appropriate levels of use of GenAI in education, impact on learning and teaching, policy, data, GenAI outputs, humans in the loop and public-private partnerships. Recommended directions for future research include learning and teaching, ethical and legal implications, ownership/authorship, funding, technology, research support, AI metaphor and types of research. Each theme or subtheme is presented in the form of a statement, followed by a justification. These findings serve as a call to action to encourage a continuing debate around GenAI and to engage more educators in research. The paper concludes that unless we can ask the right questions now, we may find that, in the pursuit of greater efficiency, we have lost the very essence of what it means to educate and learn.

sted, utgiver, år, opplag, sider
Springer Nature , 2025.
Emneord [en]
Artificial intelligence in education, Future research directions, Generative artificial intelligence (GenAI), Grey areas, Higher education
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-361046DOI: 10.1007/s11528-025-01060-6ISI: 001425226500001Scopus ID: 2-s2.0-85218173332OAI: oai:DiVA.org:kth-361046DiVA, id: diva2:1943598
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QC 20250311

Tilgjengelig fra: 2025-03-11 Laget: 2025-03-11 Sist oppdatert: 2025-03-11bibliografisk kontrollert

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Totalt: 160 treff
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