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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: The Effects of AI Programming Assistant on University Students' Algorithmic Thinking and Self-Efficacy
Γλώσσα: English
Συγγραφείς: Wen Xiao (ORCID 0000-0003-1444-908X), XueYing Lu
Πηγή: Journal of Educational Computing Research. 2026 64(5):1121-1157.
Διαθεσιμότητα: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 37
Ημερομηνία έκδοσης: 2026
Τύπος εγγράφου: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Programming, Artificial Intelligence, College Students, Algorithms, Self Efficacy, Thinking Skills, Technology Uses in Education, Sex, Student Attitudes, Foreign Countries, Educational Technology, Cognitive Style
Γεωγραφικοί όροι: China
DOI: 10.1177/07356331261424733
ISSN: 0735-6331
1541-4140
Περίληψη: 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.
Abstractor: As Provided
Entry Date: 2026
Αριθμός Καταχώρησης: EJ1506954
Βάση Δεδομένων: ERIC
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  Data: The Effects of AI Programming Assistant on University Students' Algorithmic Thinking and Self-Efficacy
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 10.1177/07356331261424733
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  Data: 0735-6331<br />1541-4140
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  Data: 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.
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