The Role of Artificial Intelligence in Shaping Assessment Practices in Higher Education

Background/purpose. Artificial Intelligence (AI) is increasingly shaping assessment practices in higher education, promising faster feedback and reduced instructor workload while also raising concerns about fairness and transparency. This study examines how AI technologies are transforming assessment processes and the experiences of stakeholders.

Materials/methods. We conducted a mixed-methods study across five universities. A total of 300 undergraduate students and 100 instructors completed parallel surveys (totaling 30 items each) about their use of and perceptions toward AI tools. We also interviewed 15 faculty and conducted four student focus groups to explore experiences with AI-based assessment. Quantitative data were analyzed using descriptive statistics and independent t-tests, while interview transcripts were analyzed thematically.

Results. The majority of participants reported efficiency gains from AI. For example, 73% of students received faster feedback when AI was used, and instructors reported an average 40% reduction in grading time. However, 52% of students did not fully understand how AI-derived scores were calculated. Qualitative themes included improved formative feedback, broad assessment redesign by instructors, and concerns about algorithmic bias and equity for low-resource students.

Conclusion. AI-enabled assessment offers clear pedagogical benefits when implemented thoughtfully. Key recommendations include ensuring transparent AI processes, training educators and students, and enacting equity-focused policies. Institutional readiness—through policies and support—is crucial for harnessing AI's potential for assessment while ensuring fairness and maintaining learning quality.

Keywords: Artificial intelligence; assessment; higher education; formative feedback; ethics

References

Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 31(1), 1–27. https://doi.org/10.1007/s40593-021-00285-9

Biggs, J., & Tang, C. (2011). Teaching for Quality Learning at University (4th ed.). Maidenhead: McGraw-Hill Education.

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102

Carless, D. (2007). Learning‐oriented assessment: conceptual bases and practical implications. Innovations in Education and Teaching International, 44(1), 57–66. https://doi.org/10.1080/14703290601081332

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. Advance online publication. https://doi.org/10.1080/14703297.2023.2190148

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1

Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 0028. https://doi.org/10.1038/s41562-016-0028

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson.

Memarian, B., & Doleck, T. (2023). Fairness, accountability, transparency, and ethics (FATE) in AI and higher education: A systematic review. Computers and Education: Artificial Intelligence, 5, 100152. https://doi.org/10.1016/j.caeai.2023.100152

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Nicol, D. (2021). The power of internal feedback: Exploiting natural comparison processes. Assessment & Evaluation in Higher Education, 46(5), 756–778. https://doi.org/10.1080/02602938.2020.1823314

Nicol, D., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8

Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Cambridge: Polity Press.

Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), 635–649. https://doi.org/10.1111/ejed.12532

Tsai, Y. S., & Gašević, D. (2017). Learning analytics in higher education – challenges and policies: a review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 233–242). ACM. https://doi.org/10.1145/3027385.3027400

Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: critical issues and perspectives. Teaching in Higher Education, 25(4), 351–365. https://doi.org/10.1080/13562517.2020.1748811

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

UNESCO. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. Paris: UNESCO.

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