A Validated Scale Measuring Student Self-Efficacy for Programming with Generative AI

Abstract

The rise of generative artificial intelligence (GenAI) has sparked a rapid change in computing curricula and teaching approaches. GenAI coding tools can accurately complete assignments, answer test questions, and perform other tasks traditionally associated with learning programming, especially at the introductory level. Because GenAI is still so new, researchers investigating student usage of GenAI have used informal rubrics and questionnaires. To advance, the field needs validated instruments that measure student perception and use of GenAI. This paper presents the development and initial validation of an instrument to measure self-efficacy while using GenAI to learn programming. Self-efficacy is an important construct in education research because it robustly correlates with student success, across disciplines and ages, including undergraduate computing education. Computing education researchers have presented several validated self-efficacy instruments, most recently by Steinhorst et al. in 2020. Critically, this instrument was created before the rise of GenAI’s popularity in 2022. To complement this instrument, we created a GenAI scale similar in style to the Steinhorst self-efficacy instrument, consisting originally of 11 items and revised to 5 items. We report two important findings in this paper. First, we found strong support for the validity of the existing Steinhorst instrument in a new context, specifically an introductory programming course that fully integrates GenAI. Second, the new GenAI scale shows strong internal reliability, discriminant validity with items in the Steinhorst subscales, and criterion validity with students’ GenAI usage patterns. Based on statistical analysis and cognitive probing interviews, we argue for the validity of the five-item scale to measure students’ GenAI self-efficacy in the context of programming.