Enhancing Flipped Learning With Convergent Feedback: The Impact of <scp>GPTutor</scp> on Knowledge Construction, Motivation and Engagement in Higher Education

Abstract

ABSTRACT While flipped learning promotes active learning in higher education, students often struggle with self‐regulation and motivation when facing complex content. Traditional search engines provide divergent feedback that can increase cognitive load and impede learning effectiveness. This study examined the impact of GPTutor, an AI‐powered tool integrating ChatGPT and Apple's Shortcuts to provide convergent feedback, on knowledge construction, motivation and engagement. Using a quasi‐experimental design, 72 Taiwanese educational technology students were divided into control (Google search) and experimental (GPTutor) groups. ANCOVA and t ‐tests revealed that GPTutor significantly improved post‐test scores ( F = 7.99, p &lt; 0.01), intrinsic motivation ( t = 2.993, p &lt; 0.01) and reduced amotivation ( t = −2.307, p &lt; 0.05). The experimental group also demonstrated higher cognitive, behavioural and emotional engagement (all p &lt; 0.01). The findings suggest that AI‐powered convergent feedback can enhance flipped learning effectiveness in higher education, particularly for complex interdisciplinary content.

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