Computing Students' Attitudes Toward ChatGPT Across Learning Modalities in Programming Courses

Abstract

This study aims to explore computing students' attitudes toward ChatGPT across online, face-to-face (F2F), and hybrid learning modalities in programming courses. It also examines how key factors such as task complexity, self-efficacy, AI literacy, outcome expectations, and dependency vary across these learning environments using descriptive cross-sectional research design. Students generally perceive ChatGPT positively in online programming tasks, valuing its usefulness and ability to enhance skills, though they acknowledge challenges related to task complexity, usability, and over-reliance, with high expectations for its role in complementing learning while maintaining strong selfefficacy and AI literacy. Significant differences among learning modality groups in attitudes toward ChatGPT and perceptions of task complexity was observed, while no significant differences were found for self-efficacy, AI literacy, outcome expectations, or dependency, indicating that these factors remain consistent across groups. Recommendations are discussed.

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