Evaluating Contextually Personalized Programming Exercises Created with Generative AI
Evanfiya Logacheva, Arto Hellas, James Prather, Sami Sarsa, Juho, Leinonen

TL;DR
This study evaluates the use of GPT-4 to generate personalized programming exercises tailored to students' interests, finding high-quality, engaging problems that enhance learning in introductory courses.
Contribution
It demonstrates the feasibility and effectiveness of using GPT-4 for automatic creation of personalized programming exercises in educational settings.
Findings
Generated exercises were rated as high quality by students and authors.
Students found personalized exercises engaging and useful.
AI-generated problems can supplement practice material in programming courses.
Abstract
Programming skills are typically developed through completing various hands-on exercises. Such programming problems can be contextualized to students' interests and cultural backgrounds. Prior research in educational psychology has demonstrated that context personalization of exercises stimulates learners' situational interests and positively affects their engagement. However, creating a varied and comprehensive set of programming exercises for students to practice on is a time-consuming and laborious task for computer science educators. Previous studies have shown that large language models can generate conceptually and contextually relevant programming exercises. Thus, they offer a possibility to automatically produce personalized programming problems to fit students' interests and needs. This article reports on a user study conducted in an elective introductory programming course…
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Taxonomy
TopicsEngineering Education and Technology · Teaching and Learning Programming · Distributed and Parallel Computing Systems
