Enhancing student engagement through Ai-assisted analysis of past-year questions using canva whiteboard

Student participation is vital for effective learning, especially in technical fields like Construction Technology. Current revision techniques, which often rely on old exam papers, tend to make students passive. This study investigated how incorporating AI tools and digital collaboration platforms would boost student engagement. Using a mono-method approach, the study involved undergraduate Construction Technology students. AI tools analyzed past exam questions to pinpoint key concepts and helped structure answers. These insights were then used in interactive learning sessions on Canva Whiteboard, where lecturers guided students through brainstorming, discussions, and Questions and Answer (Q&A). Data were gathered via subjective answers, classroom observations, and academic performance reviews. The expectation was that this approach would lead to increased student engagement, higher participation, and a deeper grasp of concepts and question’s verbs towards students’ readiness facing the Final Examination. Ultimately, the study suggests that AI-supported collaborative learning effectively transforms traditional revision into a more active, student-focused experience