Generative AI as a Pedagogical Co-Pilot in Higher Education: A Review of Adaptive Learning and Teacher Support Systems

Purwono Purwono, Alfian Ma’arif, Hamzah M. Marhoon, Ahmed J. Abougarair, Abdel-Nasser Sharkawy

Abstract


Generative Artificial Intelligence (GenAI) has emerged as a transformative force in higher education, offering dynamic support for adaptive learning and instructional practices. This review explores the integration of GenAI in pedagogical environments, focusing on its dual function as a facilitator of personalized learning and a support system for educators. As its adoption expands, concerns arise regarding algorithmic bias, data privacy, overdependence, and the erosion of critical thinking. This paper synthesizes findings from a range of recent studies to assess GenAI's capabilities, limitations, and ethical implications. Employing a thematic literature analysis, the review categorizes GenAI applications into adaptive learning systems, teacher support tools, and student engagement platforms. The results indicate that GenAI enhances individualized instruction, reduces administrative burden, and fosters student interaction through co-creation and chatbot-assisted learning. However, challenges such as content hallucination, contextual insensitivity, academic integrity concerns, and equity gaps persist. Comparative analysis of general-purpose and education-specific GenAI tools further highlights trade-offs between adaptability, accessibility, and user control. The study concludes with recommendations for developing teacher-AI collaboration frameworks, improving AI literacy, and establishing inclusive and ethical governance models to ensure responsible GenAI integration in education. This review contributes to the field by providing a structured taxonomy of GenAI tools, a comparative analysis of their pedagogical functions, and policy-oriented recommendations for ethical and inclusive integration in higher education.

Keywords


Generative AI; Adaptive Learning; Teacher Support; Pedagogical Technology; AI Ethics

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DOI: https://doi.org/10.59247/jtped.v2i1.8

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