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

TL;DR

This study demonstrates that using Large Language Models to personalize computer science exercises significantly improves task completion and engagement among diverse student profiles in an introductory programming course.

Contribution

It presents a practical LLM-based adaptation of the FACET system for personalized instructional content, addressing engagement gaps in CS1 education.

Findings

Personalized exercises increased task completion from 70-75% to over 99%.

Low Knowledge/Low Motivation students improved correctness by 18.2%.

Students valued scaffolding over motivational tone in personalized tasks.

Abstract

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. 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. 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. 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.