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Case Study | Pedagogy | Volume 15 Issue 5, May 2026 | Pages: 605 - 609 | Uzbekistan
AI-Driven Personalized Learning in Higher Education: Balancing Adaptivity and Academic Rigor
Abstract: Artificial intelligence has significantly expanded personalized learning opportunities in higher education through adaptive instruction, automated feedback, and individualized pacing. At the same time, the widespread use of generative AI has challenged the validity of traditional assessment practices. This paper examines both the educational benefits and the pedagogical risks of AI-driven personalization. It discusses major adaptation mechanisms used in higher education and analyzes risks associated with over-adaptivity, including dependency, illusion of competence, reduced originality, and weakened assessment authenticity. The study also presents a practice-based case study from mathematics education in which graded homework became unreliable as evidence of independent student understanding because of increasing AI-assisted solution generation. In response, assessment practices were redesigned to emphasize non-graded formative homework, in-class board work, and controlled written examinations. Based on this experience, the paper proposes a hybrid assessment framework that balances AI-supported learning with AI-resistant evaluation. The central argument is that reliable assessment in AI-rich educational environments requires triangulation across multiple evaluation contexts rather than dependence on a single assessment format. The proposed framework offers a practical conceptual model for preserving academic rigor while retaining the educational advantages of AI-supported personalization.
Keywords: artificial intelligence, personalized learning, higher education, assessment, academic rigor, generative AI, mathematics education, adaptive learning, formative assessment, assessment redesign, educational technology, AI-assisted learning
How to Cite?: Andrey Dragunov, Anna Tomskova, "AI-Driven Personalized Learning in Higher Education: Balancing Adaptivity and Academic Rigor", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 605-609, https://www.ijsr.net/getabstract.php?paperid=SR26503141339, DOI: https://dx.dx.doi.org/10.21275/SR26503141339