Agentic AI for Personalized Learning in Higher Education: A Thematic Cross-Analysis of Effectiveness, Engagement, and Barriers
Year: 2026
Type: conference-paper
Abstract: Background: Agentic AI, systems that have the potential to support autonomous goal-directed behavior, multi-step planning, and adaptive feedback are quickly transforming the instructional design, academic decision-making, and student support across institutions of higher learning. Purpose: The research is a synthesis of the existing literature that will measure the effectiveness of agentic AI in personalized learning, its effects on student engagement and self-regulation, and the challenges of implementation that limit equitable adoption. Procedure: 42 peer-reviewed publications (2023-2025) were analyzed with the help of a thematic content analysis (qualitative). Coding of studies was done in three dimensions of analysis namely learning effectiveness, engagement and self-regulation, and implementation challenges. The ranking of the challenges was done with the assistance of an impact scoring formula (Frequency × Severity / Research Attention). Significant Findings: Agentic AI improves personalization and efficiency of processes but has dissimilar impacts on populations of learners. The advantages of engagement are mostly short-term; the advantages of autonomy development in the long term are yet to be determined. The cross-analysis reveals that the most influential and least-studied issues are gaps in governance and risks of cognitive dependency. Conclusion: To achieve the transformative potential of agentic AI, enforceable governance structures, equity-audited deployment, and pedagogical designs to counteract cognitive passivity are needed.
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7214729186