Advance AI-motivation research through theory-driven, pedagogically explicit, longitudinal designs covering broader motivational constructs¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The review's forward-looking recommendations urge researchers to move "beyond isolated motivational outcomes and toward a coherent, theory-driven framework that integrates motivational theories, pedagogical design, and AI affordances." Concretely, it recommends diversifying geographic and educational contexts, broadening motivational constructs examined (including potential motivational costs such as reduced intrinsic motivation and erosion of agency), grounding studies in explicit frameworks such as self-determination theory, expectancy–value theory, and achievement goal theory, and explicitly linking AI use to pedagogical design principles.

Design Implications¶

Context¶

Requirements¶

• Future studies should adopt randomized and mixed-method designs and collect data over extended periods to disentangle novelty effects from sustained motivational impact

Constraints¶

• The review states it is beyond the scope of the current study to propose such an integrative framework

Target Learners¶

• researchers and designers of AI-supported learning across primary, secondary, and higher education

Target Learning Goals¶

• understanding and supporting student motivation in AI-mediated learning

Related Strategies¶

• Strengthen the evidence base with comparative and longitudinal designs and include rural, underserved, and low-resource communities before presenting GenAI as democratizing
• Four-part research agenda: longitudinal studies, hybrid frameworks, ethical frameworks, and underrepresented STEM domains
• Diversify AIED: use diverse technologies, address big problems, and reuse existing resources rather than reinventing content
• Integrate autonomy and agency paradigms, standardise instruments, probe non-demographic moderators, and extend evaluations to longitudinal real-world settings
• Research agenda for advancing transformative communication environments: sociocultural theory, conversational analysis, and beyond-face-to-face affordances
• Move beyond explorative expert-novice comparisons to hypothesis-driven designs investigating application of evidence-based pedagogical principles
• Pursue new research avenues on motivation that serve both the field and practitioners

Examples¶

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Key Sources¶

• Jiajing Li, Ronnel B. King, Ching Sing Chai, Vivian WY Lee & Ruyi Li. (2026). AI and student motivation: A scoping review. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2026.2672515