Artificial Intelligence Supported Personalized Learning: Examining Its Impact on Students' Critical Thinking, Learning Engagement, and Academic Achievement in Higher Education

Abstract

Artificial intelligence (AI)-supported personalized learning provides adaptive instructional content, individualized feedback, and differentiated learning pathways, although its educational outcomes remain inconsistently established. This study examines the relationships between AI-supported personalized learning and students' critical thinking, learning engagement, and academic achievement in higher education. A structured integrative literature review was conducted using 24 scholarly publications from 2022–2026. Qualitative thematic synthesis examined technological mechanisms, reported educational outcomes, and pedagogical conditions while distinguishing empirical findings from conceptual interpretations. The findings identify adaptive questioning, reflective feedback, and individualized cognitive challenges as potential mechanisms supporting critical thinking, although direct evidence of measurable improvement remains limited. Personalized instructional support is associated with learning engagement through relevance, motivation, and sustained participation, while adaptive assessment, individualized feedback, and self-regulated learning are linked to reported academic achievement. These relationships vary according to instructional design, learner autonomy, digital competence, and educational context. The reviewed evidence does not establish causal relationships among the three outcomes. The bounded literature corpus, heterogeneous research designs, and absence of a standardized quality appraisal limit generalizability. The review contributes an integrated interpretation distinguishing reported educational associations from proposed mechanisms and identifies priorities for longitudinal, comparative, and outcome-specific empirical investigation.