REDESIGNING LEARNER ASSESSMENT  IN THE CONTEXT OF AI-INTEGRATED HIGHER EDUCATION

Huynh Van Tung; Nguyen Le Hoa Tuyet; Huynh Huu Tho

Huynh Van Tung	hvtung@ctuet.edu.vn	Can Tho University of Technology 256 Nguyen Van Cu street, Cai Khe ward, Can Tho City, Vietnam
Huynh Huu Tho	hhtho@ctu.edu.vn	School of Economics, Can Tho University 3/2 road, Ninh Kieu ward, Can Tho City, Vietnam
Nguyen Le Hoa Tuyet*	nlhtuyet@ctuet.edu.vn	Can Tho University of Technology 256 Nguyen Van Cu street, Cai Khe ward, Can Tho City, Vietnam

Summary:

The rapid rise of AI has severely compromised the validity of product-based assessment, creating an urgent need for a new theoretical framework to monitor learners’ knowledge construction. Applying the integrative literature review method, this study proposes the Research - Operation - Evaluation (R-O-E) framework, shifting the assessment paradigm from product-based to process-based evaluation. The core contribution lies in establishing a comprehensive assessment ecosystem through the synchronous integration of the R-O-E framework with the ETHICAL principles and the SARPS reconstruction strategies. This system mandates transparency in students’ knowledge construction by leveraging digital footprints, reflective logs, oral defense, and adversarial validation of AI outputs. Consequently, the study provides a scientific and feasible roadmap to help higher education institutions safeguard academic integrity and enhance substantive educational quality in the AI era.

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Keywords:

product-based assessment

process-oriented assessment

AI

R-O-E

students.

Refers:

[1] Adamakis, M. & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(4), pp.180-205.

[2] AlBlooshi, S. (2026). Artificial intelligence in higher education, opportunities, and challenges: a review. Frontiers in Education, 10: 1683968. https:// doi.org/10.3389/feduc.2025.1683968

[3] Anderson, L. W. & Krathwohl, D. R. (2001). A Taxonomy for Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives: Complete Edition. New York: Longman.

[4] Chan, C. K. Y. & Colloton, T. (2024). Generative AI in higher education: The ChatGPT effect. Routledge. https://doi.org/10.4324/9781003459026

[5] Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), pp.228 239. https://doi.org/10.1080/14703297.2023.219014 8

[6] Digital Education Council - DEC. (2025). Global AI faculty survey 2025. DEC.

[7] Earl, L. (2003). Assessment as Learning: Using Classroom Assessment to Maximize Student Learning. Thousand Oaks, CA: Corwin Press.

[8] Freeman, J. (2025). Student generative AI survey 2025. Higher Education Policy Institute: London, UK. https://www.hepi.ac.uk/wp-content/uploads/ 2025/02/HEPI-Policy-Note-61-2.pdf

[9] Hodges, C. B. & Kirschner, P. A. (2024). Innovation of instructional design and assessment in the age of generative artificial intelligence. TechTrends, 68(1), pp.195-199. https://doi.org/10.1007/s11528-023-00 926-x.

[10] Jacques, P. H., Moss, H. K. & Garger, J. (2024). A synthesis of AI in higher education: Shaping the future. Journal of Behavioral and Applied Management, 24(2), pp.103-111. https://doi.org/10.21818/001c.122146

[11] Khlaif, Z. N., Alkouk, W. A., Salama, N. & Abu Eideh, B. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), pp.174 201. https://doi.org/10.3390/educsci15020174.

[12] Kofinas, A. K., Tsay, C. H. H. & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), pp.2522-2549. https://doi.org/10.1111/bjet. 13585

[13] Koh, E. & Doroudi, S. (2023). Learning, teaching, and assessment with generative artificial intelligence: towards a plateau of productivity. Learning: Research and Practice, 9(2), pp.109-116. https://doi. org/10.1080/23735082.2023.226408

[14] Liang, W., Yuksekgonul, M., Mao, Y., Wu, E. & Zou, J. (2023). GPT detectors are biased against non native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779

[15] Lodge, J., Howard, S., Bearman, M. L., Dawson, P. & Associates (2023). Assessment Reform for The Age of Artificial Intelligence. Tertiary Education Quality and Standards Agency. https://www.teqsa.gov. au/sites/default/files/2023-09/assessment-reform age-artificial-intelligence-discussion-paper.pdf

[16] Perkins, M., Furze, L., Roe, J. & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6), pp.49-66. https://doi.org/10.53761/q3azde36

[17] Roe, J., Perkins, M. & Giray, L. (2026). Assessment twins: An approach for strengthening assessment validity in the age of generative AI. Journal of Applied Learning & Teaching, 9(2), pp.1-15. https:// doi.org/10.37074/jalt.2026.9.2.3

[18] Shaw, C., Dorn, H., Martin, S., Hay, L., Janson, N. & Bryant, G. (2025, June). Time for Class - 2025. Tyton Partners.

[19] Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human resource development review, 4(3), pp.356-367. https://doi. org/10.1177/1534484305278283

[20] United Nations Educational, Scientific and Cultural Organization - UNESCO. (2025). AI and the future of education: Disruptions, dilemmas and directions. https://doi.org/10.54675/KECK1261

[21] Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., ... & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), pp.26-65. https://doi. org/10.1007/s40979-023-00146-z

[22] Whittemore, R.& Knafl, K. (2005). The integrative review: updated methodology. Journal of advanced nursing, 52(5), pp.546-553. https://doi.org/10.1111/ j.1365-2648.2005.03621.x

Journal:

No. 10, October 2026

VIETNAM JOURNAL OF EDUCATIONAL SCIENCES

THE VIETNAM NATIONAL INSTITUTE OF EDUCATIONAL SCIENCES

REDESIGNING LEARNER ASSESSMENT IN THE CONTEXT OF AI-INTEGRATED HIGHER EDUCATION