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Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape
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While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.
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“Quality-of-teaching gains include using AI to check accessibility of materials, identify early indicators of learning difficulty (Johnson, Smart, & Mahar, 2023), and produce personalised teaching strategies (Bozkurt et al., 2023).”
From The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students (2025) · cites this paper as Bozkurt et al., 2023
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Metadata
- Authors
- Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L., Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G., Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., & Jandrić, P.
- ORCID
- Bozkurt, A.
- Xiao, J.
- Lambert, S.
- Pazurek, A.
- Crompton, H.
- Koseoglu, S.
- Farrow, R.
- Bond, M.
- Nerantzi, C.
- Honeychurch, S.
- Bali, M.
- Dron, J.
- Mir, K.
- Stewart, B.
- Costello, E.
- Mason, J.
- Stracke, C. M.
- Romero-Hall, E.
- Koutropoulos, A.
- Toquero, C. M.
- Singh, L.
- Tlili, A.
- Lee, K.
- Nichols, M.
- Ossiannilsson, E.
- Brown, M.
- Irvine, V.
- Raffaghelli, J. E.
- Santos-Hermosa, G.
- Farrell, O.
- Adam, T.
- Thong, Y. L.
- Sani-Bozkurt, S.
- Sharma, R. C.
- Hrastinski, S.
- Jandrić, P.
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2023-02-13
- OpenAlex
- W6894330473
- Cited by
- 350
- References
- 0
- Citations by year
- 2026: 24 · 2025: 111 · 2024: 146 · 2023: 68
- FWCI
- 82.35
- Type
- journal article
- Language
- en
- Open access
- green
- orcid.org
- Licence
- public-domain
- creativecommons.org/licenses/by/4.0/legalcode
- Locations
- Dublin City University Open Access Institutional Repository (Dublin City University) (acceptedVersion, public-domain)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, public-domain)
- Open Research Online - ORO (The Open University) (acceptedVersion, public-domain)
- ODU Digital Commons (Old Dominion University) (submittedVersion, cc-by)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Funders
- Università degli Studi di Padova
- Ministerio de Ciencia e Innovación (PID2021-125828OB-I00)
- Anadolu Üniversitesi (2207E099)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Digital Education and Society
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, generative AI, speculative futures, artificial intelligence in education, future of education, speculative narrative, educational technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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