Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education

Introduction: Rapid adoption of Artificial Intelligence (AI) in learning has revolutionized learners' engagement but comprehension of psychological and technological drivers of successful AI-enabled learning remains scarce. This research investigates how students' perceived agency of AI, usefulness, ease of use, trust, autonomy supporting, and self-efficacy collectively impact students' self-learning behavior and motivation. Based on Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Self-Determination Theory (SDT) theories, our research model predicts an integrated model of motivational and behavioral processes underlying AI adoption in learning settings. Methods: We adopted and followed a quantitative research design with a structured questionnaire administered among 280 higher education students in Saudi Arabia. We applied Structural Equation Modeling (SEM) using SmartPLS 4 to analyze data. Results: Findings indicate that students' perceived agency of AI significantly predicts usefulness, ease of use, and autonomy supporting, while ease of use significantly enhances AI-enabled self-efficacy. Self-efficacy and autonomy supporting significantly impact self-learning motivation, driving self-learning behavior positively. But usefulness and trust in AI failed to influence self-efficacy directly, which reveals cultural and contextual settings. Discussion: This research adds richness to the fusion of TAM, SCT, and SDT theories in illustrating how AI's perceived autonomy and usability collectively promote self-directed learning motivation. This research also provides guidelines to educators and system designers to design AI tools that promote learner autonomous settings, usability, and confidence. Future research ought to perform longitudinal and cross-cultural validations to fine-tune theoretically.