Higher education assessment practice in the era of generative AI tools
Open accessAbstract
The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.
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Over-reliance on AI can lead to a loss of fundamental skills such as memory formation, concentration, attention span, and can prevent students from developing critical thinking, autonomous problem-solving, and creativity.
“AI can lead to a loss skills such as memory attention span, and can also prevent developing critical thinking, and creativity.”
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 Ogunleye et al., 2024
The Results and discussion section says that unethical GenAI use can hinder students’ development of critical-thinking skills. It does not report a loss of memory or attention span, or establish that AI use prevents students from developing creativity. The paper also limits its findings to the assessed disciplines and conditions of use. (LLM, conf=0.87)
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Pramjeeth, S. (2026) · Springer eBooks · doi:10.1007/978-981-95-8240-2_6
Mwakalinga, S. E., & Tweve, C. (2026) · Advances in Artificial Intelligence and Robotics Research · doi:10.4236/airr.2026.21001
From Theory to Practice: ADMIT’s Framework for Ethical GenAI in Higher Education
Triantafyllopoulos, L., Takouli, V., Kalles, D., Kameas, A. D., & Antonaci, A. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-30809-2_21
Supurwoko, S., Sunarno, W., Sarwanto, Sukarmin, Gunawan, K., Hudha, M. N., & Ridhani, J. (2026) · Physics Education · doi:10.1088/1361-6552/ae7871
Elepaño, M. A. D., Manimtim, L., Buendia, B. D., Pasahol, I. S., Abel, J. V., & Sargento, J. U. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631602
Pulhin, J. C., Borbon, N. M. D., Pesigan, N. R. I., Buendia, B. D., Bautista, A., Magpili, D. C., & Manimtim, L. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631416
ENHANCING ASSESSMENT LITERACY: A SYSTEMATIC REVIEW OF AI TOOLS IN EDUCATION
Pongkendek, J. J., Rochintaniawati, D., Nahadi, N., Nugroho, E. P., Suana, W., Suparman, A., Saefullah, A., Dewi, W. S., Uliyandari, M., & Lestari, A. K. (2026) · F1000Research · doi:10.12688/f1000research.188623.1
Teachers’ use of generative artificial intelligence in K–12 education: a systematic review
Cincioğlu, O., Bayraktar, D. M., & Altan, T. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1935733 · Cited by 1 · see citing works
AI-enabled assessment: framework, perspective and educational outcomes
Ogunleye, B., Olayinka, O., Zakariyyah, K. I., Ajao, O., & Sharma, H. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2747507
Self-citationLipow, G., & Rowe, N. C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28454-9_19
van Rensburg, J. J. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2744231
Higher education assessments in the age of GenAI: a systematic review
Crompton, H., & Burke, D. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00627-4
- Authors
- Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H.
- Affiliations
- University of Brighton (GB)
- Manchester Metropolitan University (GB)
- Sheffield Hallam University (GB)
- Venue
- Journal of Applied Learning & Teaching
- Publisher
- Open Access Publishing Association
- Published
- 2024-04-01
- Volume
- 7
- Issue
- 1
- ISSN
- 2591-801X (ISSN-L)
- OpenAlex
- W4393358204
- Cited by
- 37
- References
- 68
- Citations by year
- 2026: 15 · 2025: 16 · 2024: 6
- FWCI
- 13.91
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Applied Learning & Teaching (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- e-space (Manchester Metropolitan University) (submittedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion, cc-by)
- University of Brighton Repository (University of Brighton) (submittedVersion, cc-by)
- SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- 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
- generative AI, higher education assessment, AI in teaching and learning, assessment design, ethical use of AI, critical thinking, problem-solving skills, data science education, data analytics education, construction management education, pedagogic practice
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