From Feedback to Formative Guidance: Leveraging LLMs for Personalized Support in Programming Projects

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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Large Language Models (LLMs) offer scalable opportunities to personalize feedback in education, yet their trustworthiness and effectiveness remain underexplored. We present a study conducted in an introductory programming and data science course with approximately 1,400 first-year university students. A subset of these students received both peer and LLM-generated feedback on their individual programming projects. Our results show that 56% of students preferred the LLM feedback, and 52% could not reliably distinguish it from human-written feedback. Student ratings suggest that LLM feedback is perceived as helpful, constructive, and relevant, though it often lacks personalized depth and motivational nuance. These findings underline the potential of LLMs to support scalable, personalized education, while pointing to key areas for responsible improvement. Based on these insights, we outline the future roadmap for the course in which LLM-generated feedback supports students in their learning journey but also instructors through monitoring student performance and helping to allocate instructional resources more effectively. Given limited human resources this approach enables personalized instructor feedback to be scaled to a large group of students.

OriginalspracheEnglisch
TitelUMAP 2025 - Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
HerausgeberCristina Conati, Fedelucio Narducci, Gaetano Rossiello, Cataldo Musto, Julita Vassileva
Anzahl der Seiten6
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum12.06.2025
Seiten398-403
ISBN (elektronisch)9798400713996
DOIs
PublikationsstatusErschienen - 12.06.2025
Veranstaltung33rd Conference on User Modeling, Adaptation and Personalization - UMAP 2025 - New York, USA / Vereinigte Staaten
Dauer: 16.06.202519.06.2025
Konferenznummer: 33

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