Integration of a Personalized Generative AI into University Instructional Design: Contributions, Uses, and Conditions for the Effectiveness of Active Learning Methods

Mulwani Makelele Basile^1, , Sukadi Mangwa Christelle² and Nzuzi Mavungu Gaël³

¹Department of Psychology, University of Lubumbashi, DR Congo

²Department of Information and Communication Sciences, University of Lubumbashi, DR Congo

³Department of Pharmacology, Therapeutics and Toxicology, Faculty of Veterinary Medicine, University of Lubumbashi, DR Congo

Cite this paper:
Mulwani Makelele Basile, Sukadi Mangwa Christelle and Nzuzi Mavungu Gaël. Integration of a Personalized Generative AI into University Instructional Design: Contributions, Uses, and Conditions for the Effectiveness of Active Learning Methods. American Journal of Educational Research. 2026; 14(3):107-113. doi: 10.12691/education-14-3-5

Abstract

The rapid expansion of generative artificial intelligence (AI), particularly large language models (LLMs), is profoundly transforming higher education by enabling the on-demand production of instructional content, learning activities, feedback, and assessment tools. However, recent research indicates that these uses remain largely opportunistic and insufficiently embedded within systematic instructional design processes. This weak integration may compromise constructive alignment between intended learning outcomes, learning activities, and assessment methods, as well as undermine academic integrity. And this article offers a structured synthesis of the contributions, uses, and effectiveness conditions of a personalized generative AI—defined as an AI system configured through stable pedagogical instructions, institutional constraints, and disciplinary frameworks—designed to support active learning approaches in higher education. The study is based on a qualitative documentary analysis of recent international scientific publications (during 2023–2025), complemented by the examination of a university teaching resource used as an empirical case of implementation. The findings highlight three major contributions. First, personalized generative AI can support the entire instructional design cycle, from needs analysis to assessment design, by strengthening pedagogical coherence and constructive alignment. Second, the dominant uses identified primarily concern the design of active learning strategies (flipped classroom, case-based learning, structured debates, collaborative projects) and the enhancement of formative and summative assessment practices, in line with empirical evidence demonstrating the positive impact of active learning on student performance and success. Third, the effectiveness of these uses depends on key conditions: the development of AI literacy and prompt engineering skills among educators and students; the redesign of assessment systems to ensure robustness against automation; and the establishment of ethical and institutional governance grounded in recognized risk management frameworks and international guidelines for AI in education.

Keywords:
generative AI instructional design personalization active learning constructive alignment assessment academic integrity

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	UNESCO, Guidance for generative AI in education and research, UNESCO Publishing, Paris, 2023.
[2]	Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learning and Individual Differences, 103, 102274, 2023.
[3]	Memarian, B., and Doleck, T., “ChatGPT in education: Methods, potentials, and limitations,” Computers & Education: Artificial Intelligence, 4, 100094, 2023.
[4]	Bittle, K., and El-Gayar, O., “Generative AI and academic integrity in higher education: A systematic review and research agenda,” Information, 16(4), 296, 2025.
[5]	Dos, I., “A systematic review of research on ChatGPT in higher education (January 2023–March 2025),” The European Educational Researcher, Advance online publication, 2025.
[6]	Biggs, J., “Constructive alignment in university teaching,” HERDSA Review of Higher Education, 1, 5–22, 2014.
[7]	Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H., and Wenderoth, M.P., “Active learning increases student performance in science, engineering, and mathematics,” Proceedings of the National Academy of Sciences, 111(23), 8410–8415, 2014.
[8]	Chi, M.T.H., and Wylie, R., “The ICAP framework: Linking cognitive engagement to active learning outcomes,” Educational Psychologist, 49(4), 219–243, 2014.
[9]	Knoth, N., Tolzin, A., Janson, A., and Leimeister, J.M., “AI literacy and its implications for prompt engineering strategies in education,” Computers & Education: Artificial Intelligence, 6, 100225, 2024.
[10]	National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework (AI RMF 1.0), U.S. Department of Commerce, 2023.
[11]	European Union, Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonized rules on artificial intelligence (Artificial Intelligence Act), Official Journal of the European Union, 2024.
[12]	Tranfield, D., Denyer, D., and Smart, P., “Towards a methodology for developing evidence-informed management knowledge by means of systematic review,” British Journal of Management, 14(3), 207–222, 2003.
[13]	Creswell, J.W., and Poth, C.N., Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 4th ed., SAGE Publications, Thousand Oaks, 2018.
[14]	Braun, V., and Clarke, V., “Using thematic analysis in psychology,” Qualitative Research in Psychology, 3(2), 77–101, 2006.