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

2026-04-30

AI Summary

This study addresses the low completion rates and poor learning outcomes among students with low prior knowledge or motivation in traditional programming exercises, which often lack personalization. To tackle this issue, the authors propose a learner-profile-driven approach that integrates large language models with the FACET educational framework for the first time. Guided by Bloom’s taxonomy of cognitive processes and Self-Determination Theory, the method dynamically adapts scaffolding structures, instructional explicitness, and linguistic tone to enhance task alignment while preserving desirable difficulty. Experimental results demonstrate that the proposed approach achieves over 99% task completion across all students—compared to 25–30% non-completion among low-knowledge students in the control group—and improves accuracy by 18.2% for learners with low knowledge or motivation, without significantly increasing perceived challenge.

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.

Problem

• personalized learning
• learner profiles
• computer science education
• task completion
• instructional scaffolding

Innovation

• Large Language Models
• Personalized Instruction
• Learner Profiling
• Adaptive Scaffolding
• CS1 Education