  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.
