Teaching and Assessment of the Future Today: Higher Education and AI
Open accessAbstract
Artificial intelligence (AI), once a subject of science fiction, is now a tangible, disruptive force in teaching and learning. In an educational setting, generative large language models (LLM), such as OpenAI’s ChatGPT, perform and supplement tasks that usually require human thought, such as data analysis, understanding complex ideas, problem-solving, coding and producing written outputs. AI advances are moving quickly. From the emergence of ChatGPT 3.5 in November 2022, we have witnessed the arrival of other progressive language models, like OpenAI’s GPT-4, Google’s Bard AI and Microsoft’s Bing AI. Most recently, AIs gained the ability to access real-time information, analyse images and are becoming directly embedded in many applications.
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Educators feel the need to keep up to date balanced against the time needed to do this.
“Similar observations were made by Lacey and Smith [59], with educators feeling the need to keep up to date balanced against the time needed to do this”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [59]
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Cites
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · see citing works
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · see citing works
Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Mhlanga, D. (2023) · doi:10.2139/ssrn.4354422 · Cited by 719 · see citing works
A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education
King, M. R., & chatGPT (2023) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-022-00754-8 · Cited by 602 · see citing works
Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023) · European Journal of Engineering Education · doi:10.1080/03043797.2023.2213169 · Cited by 336 · see citing works
How Chat GPT Can Transform Autodidactic Experiences and Open Education?
Firat, M. (2023) · doi:10.31219/osf.io/9ge8m · Cited by 280 · see citing works
A Chat(GPT) about the future of scientific publishing
Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2023.02.022 · Cited by 270 · see citing works
ChatGPT Performance on MCQ Exams in Higher Education. A Pragmatic Scoping ReviewPreprint
Newton, P. M., & Xiromeriti, M. (2023) · doi:10.35542/osf.io/sytu3 · Cited by 51 · see citing works
Naumova, E. N. (2023) · Journal of Public Health Policy · doi:10.1057/s41271-023-00400-1 · Cited by 44 · see citing works
Towards Developing AI Literacy: Three Student Provocations on AI in Higher Education
Brew, M., Taylor, S., Lam, R., Havemann, L., & Nerantzi, C. (2023) · UCL Discovery (University College London) · doi:10.5281/zenodo.8032386 · Cited by 16 · see citing works
Academic dishonesty, essay mills, and Artificial Intelligence: rethinking assessment strategies
Sweeney, S. (2023) · International Conference on Higher Education Advances (HEAd) · doi:10.4995/head23.2023.16181 · Cited by 7 · see citing works
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Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“Similar observations were made by Lacey and Smith [59], with educators feeling the need to keep up to date balanced against the time needed to do this and Walczak and Cellary [60] also highlighted the need for support in this new digital literacy.”
Self-citationChatGPT in Teaching and Learning: A Systematic Review
Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024) · Education Sciences · doi:10.3390/educsci14060643 · Cited by 146 · see citing works
- Authors
- Lacey, M. M., & Smith, D. P.
- Affiliations
- Sheffield Hallam University (GB)
- Corresponding author
- Lacey, Melissa M.
- Venue
- Microbiology Australia
- Publisher
- CSIRO Publishing
- Published
- 2023-07-14
- Volume
- 44
- Issue
- 3
- Pages
- 124-126
- ISSN
- 1324-4272 (ISSN-L)
- 2201-9189
- OpenAlex
- W4384346055
- Cited by
- 34
- References
- 11
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 8
- FWCI
- 7.98
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Microbiology Australia (publishedVersion, cc-by)
- SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in education, generative large language models, ChatGPT, university teaching, assessment, AI disruption, GPT-4
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