Implement GenAI in higher education in ways that support autonomy, competence, relatedness, and value to sustain motivation and engagement¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's practical implication is that educators, instructional designers, and policymakers should implement Generative AI technologies so as to enhance meaningful learning experiences and sustainable engagement. It states the study "provides valuable guidance for educators, instructional designers, and policymakers seeking to implement Generative AI technologies in ways that enhance meaningful learning experiences and sustainable student engagement in higher education." The theoretical background suggests designing AI features such as adaptive feedback, personalized learning paths, and collaborative opportunities that satisfy autonomy, competence, and relatedness needs.

Design Implications¶

Context¶

Requirements¶

• Attention to students' psychological needs and perceived value rather than technology availability alone

Constraints¶

• Findings derive from a cross-sectional convenience sample at one Saudi university; the article also flags concerns such as over-reliance on AI, academic integrity issues, and unequal access

Target Learners¶

• higher education students, including postgraduate and linguistically diverse learners

Target Learning Goals¶

• sustainable student engagement and meaningful learning with GenAI tools

Related Strategies¶

• Prioritize autonomous motivation and scaffold concept-focused AI support tailored to learner trajectories rather than providing uniform access alone

Examples¶

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Key Sources¶

• Ahmed A and 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. Front. Psychol. 17:1852265. https://doi.org/10.3389/fpsyg.2026.1852265