Frontiers in psychology · 2026
Reflective AI use and student engagement in AI-supported programming: technology acceptance, programming self-efficacy, and behavioral trace evidence.
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Wang Sheng, Zhao Jiabi
Аннотация
INTRODUCTION: AI coding assistants are increasingly used in programming education, but willingness to use them does not show whether students evaluate or learn from AI suggestions. Grounded in technology acceptance, self-regulated learning, and self-efficacy theory, this study examined reflective AI use and student engagement.
METHODS: The Reflective AI Acceptance Integration Framework (RAIAF) organized three independent public datasets: a survey structural equation model (N = 131), double human coding of 66 nonblank Critical Engagement responses from a Copilot field study, and complementary programming-behavior prediction using DTA logs (N = 1, 423; 40% calendar-window sample N = 1, 122). The DTA dataset did not measure AI use and was used only to test whether recovery-related programming traces added predictive information.
RESULTS: In the MLR survey model, technology acceptance predicted reflective use (β = 0.694, p < 0.001) and engagement (β = 0.694, p < 0.001); reflective use predicted programming self-efficacy (β = 0.519, p < 0.001) and engagement (β = 0.273, p = 0.034). None of the hypothesized indirect effects was significant. Initial text-coder agreement was limited (linear-weighted κ = 0.421; ordinal α = 0.495), and consensus-coded reflective use did not significantly predict productive engagement (b = 0.149, p = 0.202). In DTA, adding recovery features increased held-out final-success Macro-F1 from 0.725 to 0.743, but average gains in repeated and 30%-50% window analyses were negligible. Activity-defined engagement was already captured by overlapping activity indicators.
DISCUSSION: The findings distinguish acceptance from reflective use but do not establish a general mediation mechanism. Reflective use was associated with self-efficacy and engagement in the survey, whereas its text and behavioral evidence was exploratory, small, and context dependent.
METHODS: The Reflective AI Acceptance Integration Framework (RAIAF) organized three independent public datasets: a survey structural equation model (N = 131), double human coding of 66 nonblank Critical Engagement responses from a Copilot field study, and complementary programming-behavior prediction using DTA logs (N = 1, 423; 40% calendar-window sample N = 1, 122). The DTA dataset did not measure AI use and was used only to test whether recovery-related programming traces added predictive information.
RESULTS: In the MLR survey model, technology acceptance predicted reflective use (β = 0.694, p < 0.001) and engagement (β = 0.694, p < 0.001); reflective use predicted programming self-efficacy (β = 0.519, p < 0.001) and engagement (β = 0.273, p = 0.034). None of the hypothesized indirect effects was significant. Initial text-coder agreement was limited (linear-weighted κ = 0.421; ordinal α = 0.495), and consensus-coded reflective use did not significantly predict productive engagement (b = 0.149, p = 0.202). In DTA, adding recovery features increased held-out final-success Macro-F1 from 0.725 to 0.743, but average gains in repeated and 30%-50% window analyses were negligible. Activity-defined engagement was already captured by overlapping activity indicators.
DISCUSSION: The findings distinguish acceptance from reflective use but do not establish a general mediation mechanism. Reflective use was associated with self-efficacy and engagement in the survey, whereas its text and behavioral evidence was exploratory, small, and context dependent.