Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models

Motivation: Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptation of the Framework for Adaptive Content using Educational Technology (FACET) system to generate personalized instructional materials for an Introduction to Computer Programming (CS1) course. Method: We conducted a mixed-methods study with 409 first-year computer science (CS) students, focusing on regular expressions (RegEx). Students were assessed on their knowledge and motivation, classified into one of four learner profiles, and assigned either LLM-personalized (treatment) or standard non-adaptive (control) exercises. Personalized materials varied in scaffolding, instructional explicitness, and tone based on learner profiles grounded in Bloom's Taxonomy and Self-Determination Theory. Results: Quantitative analysis reveals that standard exercises resulted in task incompletion among low-knowledge learners, with approximately 25–30% incompletion, whereas personalized materials sustained near-universal completion (>99%) across all profiles. While high-performing students experienced ceiling effects, Low Knowledge/Low Motivation students achieved significantly higher correctness (+18.2%) with personalized support. Survey data indicate that students prioritize structural scaffolding (logical sequence, difficulty pacing) over motivational tone and perceive the adaptive tasks as equally challenging as standard exercises. Implications: These findings suggest that learner–profile–driven LLM personalization primarily serves as a retention scaffold, preventing task abandonment among at-risk students without diminishing the task's ''desirable difficulty''. The results demonstrate that instructor-facing LLM systems can effectively close engagement gaps in CS1 by tailoring instructional explicitness to student needs.