On this page

GenAI as a learning partner — a longitudinal study of 75 first-year university students across a full semester showing that SRL is both a stable aptitude and a dynamically fluctuating state. Individual baselines were consistent, but metacognitive knowledge and wellbeing declined systemically over the term, driven by curriculum demands (e.g., major assessment deadlines). A proof-of-concept demonstrated that giving an LLM personal, temporal, and contextual information enables it to identify tailored SRL support directions.

Key Findings

  • SRL as both stable and dynamic. Pre-/post-semester surveys showed high individual consistency (R² up to 0.73), but paired t-tests revealed systemic declines in metacognitive knowledge (MAI_K) and wellbeing (WEMWBS) over the term — while metacognitive Regulation (MAI_R) held steady.
  • Curriculum drives fluctuations. Week-by-week daily-survey analysis showed a major Week 8 project deadline lowered interest, Self Efficacy, and emotional wellbeing, with most affective metrics recovering after the assessment. Students shifted from planning/reviewing toward reading new material after the deadline.
  • Context-aware GenAI support. By separating formal, psychometrically-validated measurement of SRL from the LLM's interpretation, the authors gave Gemini 3 each student's baseline, week-by-week fluctuations, and academic context — and it generated appropriately tailored support (e.g., foundational strategy-building for a low-metacognition student vs. post-assessment recovery pacing for a high-metacognition student whose confidence had dipped). Researchers judged the output pedagogically sound.

Implications

  • Design GenAI support as temporally mapped and context-aware. "One-time-fits-all" personalization based only on baseline aptitude risks over-assisting during productive struggle or under-assisting during deadline-induced downturns. Support should track the academic calendar.
  • Hybrid intelligence over black-box. Measuring SRL with validated instruments and using GenAI only to interpret and translate those metrics into support directions preserves theory-based reliability and learner effort.

Connected Concepts

Connected Articles

Citation

Song, Y., de Barba, P., & Oliveira, E. (2026). GenAI as a learning partner: Supporting self-regulated learning over time without replacing effort. Learning Letters, 8, Article 73. https://doi.org/10.20851/ll.v8.73

Embed this page

Copy the code below to embed a chromeless version of this page in a learning management system or other website. The embedded view hides the site header, navigation, and footer.