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Creators/Authors contains: "Sonmez_Unal, Deniz"

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  1. ; ; ; ; (, Proceedings of 26th International Conference on Artificial Intelligence in Education, AIED 2025, Part 4, Springer Nature Switzerland)
    Many educational recommender systems (EdRecSys) rely on commercial recommendation strategies that emphasize content relevance while neglecting learners’ views on recommendation effectiveness. To address this, we conducted a co-design study with computer science students in an introductory programming course to explore their vision of an ideal EdRecSys. The subjects shared preferences and concerns related to three areas: recommendation approaches, transparency, and control. We used Zimmerman’s model of self-regulated learning to contextualize their expectations within a broader educational framework. Findings offer actionable insights for designing learner-centered AIED systems that foster engagement, agency, and self-regulation. 
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    Free, publicly-accessible full text available July 16, 2026