Evaluating Contextually Personalized Programming Exercises Created with Generative AI
<|reference_start|>Evaluating Contextually Personalized Programming Exercises Created with Generative AI: 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 that included contextually personalized programming exercises created with GPT-4. The quality of the exercises was evaluated by both the students and the authors. Additionally, this work investigated student attitudes towards the created exercises and their engagement with the system. The results demonstrate that the quality of exercises generated with GPT-4 was generally high. What is more, the course participants found them engaging and useful. This suggests that AI-generated programming problems can be a worthwhile addition to introductory programming courses, as they provide students with a practically unlimited pool of practice material tailored to their personal interests and educational needs.<|reference_end|>
@article{logacheva2024evaluating,
    title={Evaluating Contextually Personalized Programming Exercises Created with
  Generative AI},
    author={Evanfiya Logacheva, Arto Hellas, James Prather, Sami Sarsa, Juho
  Leinonen},
    journal={arXiv preprint arXiv:2407.11994},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11994},
    primaryClass={cs.HC cs.AI cs.CY}
}