Flipping the Classroom with AI: Student Perceptions and Learning Outcomes of Collaborative Learning Environment in Undergraduate Professional Courses

Abstract

In an era where artificial intelligence (AI) is trans- forming education, particularly in India’s professional under- graduate programs, reimagining teaching and learning strategies is imperative. This study investigates the effectiveness of AI- integrated flipped classrooms in enhancing student engagement, collaboration, and academic performance. Grounded in the Technology Acceptance Model (TAM) and Social Constructivist Theory, a quasi-experimental design was used involving two student groups from engineering and management courses. The experimental group used AI tools such as ChatGPT, Curipod, and Synthesia, whereas the control group followed conventional methods. Quantitative data from pre-test and post-tests and perception surveys were analysed using paired t-tests, ANCOVA, and Mann–Whitney U tests, while qualitative feedback was interpreted using thematic analysis. The results revealed statis- tically significant improvements in academic performance and technology acceptance indicators, perceived usefulness, ease of use, and behavioral intention among the experimental group. Thematic insights highlighted benefits like personalised support and improved peer collaboration. These findings support the adoption of AI tools in flipped pedagogies to promote active, student-centered learning in line with the goals of India’s NEP 2020. This study also offers theoretical implications for extending TAM to collaborative learning environments and practical guid- ance for instructors and institutions aiming to scale AI-enhanced teaching models. The limitations and future research directions are discussed to inform the development of scalable and inclusive AI-driven teaching ecosystems. Future research could explore the longitudinal impacts and broader institutional adoption across disciplines and regions.