Research agenda: use process-oriented assessments, study encoding-consolidation-retrieval effects, and examine longitudinal outcomes of educational generative AI¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article proposes a cognitive-psychology-informed research agenda for generative AI in education. Researchers should "differentiate learning from performance" using process-oriented assessments such as retention and transfer tests, testing effects and delayed recall tasks; investigate how generative AI influences encoding, consolidation and retrieval using cognitive load theory paradigms; and study longitudinal effects including interactions with prior knowledge.

Design Implications¶

Context¶

Requirements¶

• Research designs must include delayed or process-oriented measures rather than only immediate task performance

Constraints¶

• The article notes the relationship between cognitive load and deep encoding remains to be clarified, and long-term effects and prior-knowledge interactions remain unexamined

Target Learners¶

• tertiary students
• K-12 students

Target Learning Goals¶

• durable learning
• knowledge retention
• transfer of knowledge

Claims¶

• Evidence on GenAI in chemistry education is concentrated in short-term outcomes, with durable transfer and retention rarely tested [+W]

Related Strategies¶

• Sequence simpler desirable difficulties (retrieval, spacing, interleaving) during initial L2 vocabulary encoding, reserving generation for post-encoding consolidation
• Use five conceptual propositions to guide research and professional development on teachers' diagnostic skills in AI-supported formative assessment
• Research agenda: develop an existential hope scale, then test the framework with SEM and network analysis in longitudinal designs
• Study learning with AI by varying the interactional arrangement and measuring process and delayed reduced-support outcomes

Examples¶

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Key Sources¶

• Yan, L., Greiff, S., Lodge, J. M., & Gašević, D. (2025). Distinguishing performance gains from learning when using generative AI. Nature Reviews Psychology, 4, 435–436. https://doi.org/10.1038/s44159-025-00467-5