Is AI changing learning and assessment as we know it? Evidence from a ChatGPT experiment and a conceptual framework
Open accessAbstract
ChatGPT, a state-of-the-art chatbot built upon Open AI's generative pre-trained transformer, has generated a major public interest and caused quite a stir in the higher education sector, where reactions have ranged from excitement to consternation. This paper therefore examines the potential impact of ChatGPT on learning and assessment, using the example of academic essays, being a major form of assessment with widespread applications of ChatGPT. This provides an opportunity to unpack broader insights on the challenge of generative AI's to the relevance, quality and credibility of higher education learning in a rapidly changing 21st century knowledge economy. We conducted a quasi-experiment in which we deployed ChatGPT to generate academic essays in response to a typical assessment brief, and then subjected the essays to plagiarism checks and independent grading. The results indicate that ChatGPT is able to generate highly original, and high quality, contents from distinct individual accounts in response to the same assessment brief. However, it is unable to generate multiple original contents from the same account, and it struggled with referencing. The discussion highlights the need for higher education providers to rethink their approach to assessment, in response to disruption precipitated by artificial intelligence. Thus, following the discussion of empirical data, we propose a new conceptual framework for AI-assisted assessment for lifelong learning, in which the parameters of assessment extend beyond knowledge (know what) testing, to competence (know how) assessment and performance (show how) evaluation.
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Biased AI tools can lead to unfair learning experiences, discriminatory admissions decisions, or grading systems.
“Biased AI tools can lead to unfair learning experiences, discriminatory admissions decisions, or grading systems that”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Kolade et al., 2024
The Literature review, ‘Use of AI in learning and assessment’ (page unavailable), says algorithmic bias may limit AI tools’ objectivity. That supports a general concern about unfair AI-assisted learning or assessment, but the paper does not connect bias specifically to discriminatory admissions decisions or grading outcomes. (LLM, conf=0.82)
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Aprender e Ensinar em Fisiologia
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Damola, F. M., Dayo, U. O., Sheriff, A., Edem, M. E., Daniel, C. S., Ebite, C., & Paul, M. E. (2025) · Global Journal of Educational Research · doi:10.4314/gjedr.v24i2.6
Application of Artificial Intelligence in the Educational Assessment: A Bibliometric Analysis
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- Authors
- Kolade, O., Owoseni, A., & Egbetokun, A.
- Affiliations
- Sheffield Hallam University (GB)
- De Montfort University (GB)
- Corresponding author
- Kolade, Oluwaseun
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 10
- Issue
- 4
- Pages
- e25953
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC10877295
- OpenAlex
- W4391715216
- Cited by
- 66
- References
- 52
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 14
- FWCI
- 24.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Heliyon (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DMU Open Research Archive (De Montfort University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, generative AI, AI-assisted assessment, academic essay writing, higher education assessment, plagiarism detection, AI-generated text quality, referencing, competency assessment, performance evaluation, lifelong learning, conceptual framework, knowledge economy
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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