The Effects of AI Programming Assistant on University Students’ Algorithmic Thinking and Self-Efficacy

Abstract

Algorithmic thinking refers to students’ abilities and skills in understanding problems, formulating strategies, and creating algorithms. This study explores the integration of large language model-driven AI programming assistants into the cultivation of university students’ algorithmic thinking, and examines the effect of AI assistants on university students’ algorithmic thinking and self-efficacy. The results indicate that AI assistants, equipped with functions such as timely feedback, personalized support, emotional companionship, and cognitive scaffolding, exert a significantly positive effect on students’ algorithmic thinking and self-efficacy. This effect shows no differences across genders or learning styles. While AI assistants demonstrated clear advantages in areas such as real-time feedback, problem diagnosis, and generating diverse solutions, students perceived that teachers retained a crucial and complementary role in guiding goals and values, constructing knowledge frameworks, providing emotional support, and offering holistic learning guidance. Based on these perceptions, we propose that teachers and AI assistants can collaborate to form a collaborative model for cultivating algorithmic thinking. This study provides practical guidance for applying AI assistants in programming or algorithm courses to enhance students’ algorithmic thinking, and offers valuable insights for designing human-machine collaborative teaching methods and optimizing AI assistants.