Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education

Synthesis: Ahmed and Sultan (2026) investigated how perceived autonomy, competence, relatedness, expectancy, and value influence autonomy support for AI use, autonomous motivation, and ultimately student motivation and engagement in GenAI-supported learning. Integrating Self-Determination Theory, Expectancy-Value Theory, and the Technology Acceptance Model, and analyzing data from 297 undergraduates and postgraduates at King Saud University (Saudi Arabia) with PLS-SEM, they found that autonomy support and autonomous motivation significantly increased student motivation, which emerged as the strongest predictor of student engagement. Perceived expectancy showed no significant influence, and perceived competence did not significantly affect autonomy support.

Key Findings

Perceived autonomy, perceived relatedness, and perceived value significantly enhanced autonomy support for AI use, while perceived autonomy, competence, and relatedness positively influenced autonomous motivation for AI use.

Autonomy support and autonomous motivation significantly increased student motivation, which subsequently emerged as the strongest predictor of student engagement.

Perceived expectancy showed no significant influence on either autonomy support or autonomous motivation, and perceived competence did not significantly affect autonomy support.

The study integrates SDT, EVT, and TAM within a single framework to explain student engagement in generative AI-supported learning environments, addressing the gap of single-theory models.

It provides context-specific evidence from Saudi higher education, extending AI-in-education research beyond Western and technologically advanced contexts.

Study Design & Method

A quantitative research design was used with data from 297 undergraduate and postgraduate students at King Saud University. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Constructs spanned perceived autonomy, competence, relatedness, expectancy, and value (from SDT and EVT); autonomy support for AI use and autonomous motivation for AI use (contextualized mediators); and student motivation and student engagement (outcomes). The integrated framework allowed the authors to trace how psychological need satisfaction and technology acceptance perceptions translate into motivation and engagement in AI-supported learning.

Implications for AI in Education

The findings indicate that the motivational payoff of Generative AI depends on satisfying students' basic psychological needs — autonomy, competence, and relatedness — and on supporting autonomous motivation, rather than on expectancy or simple perceptions of competence. For educators and instructional designers, this argues for GenAI integration that enhances learner autonomy (flexible paths, student choice), competence (feedback and skill development), and relatedness (collaborative, inclusive opportunities), since these psychological supports drive the Motivation that most strongly predicts engagement. The study offers practical guidance for implementing GenAI in ways that promote meaningful, sustainable student engagement in higher education.

Limitations

The cross-sectional design limits causal inference, and the sample is drawn from a single Saudi university, bounding generalizability to other national and institutional contexts. All measures are self-report, and the study's cross-sectional PLS-SEM analysis cannot establish temporal ordering among mediators and outcomes. The integration of three theories, while comprehensive, relies on the specific operationalization of autonomy support and autonomous motivation for AI use developed for this context.

Connected Concepts

Generative AI

Motivation

Self Determination Theory

Engagement Metrics

Higher Ed

Personalized Learning

Student Experience

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Citation

Ahmed, A., & Sultan, A. (2026). Examining the impact of generative AI on student motivation and engagement: The mediating role of autonomy-support and autonomous motivation in education.