Generative AI (GenAI) holds great potential for higher education students (Rudolph et al., 2024), yet studies warn of over‑reliance, shallow engagement, and ethical risks to academic integrity (Stone, 2023; Jensen et al., 2020). In response to UNESCO (2023) and the Hong Kong Digital Policy Office (DPC, 2025), this project develops and evaluates a teacher-facing pedagogicalContinue reading “Co-designing a Pedagogical Framework with Undergraduates to Promote Self-Regulated Learning, Emotional Well-Being, and Academic Integrity in Generative AI Us”
Showing results for keyword “Self-regulated Learning”
Enhancing Self-Regulated Learning Efficacy in Undergraduate Education through the Integration of AI-Enabled Generative Tools and the 6-P Pedagogy: A Learning and Teaching Partnership Approach
AI is rapidly transforming university education globally, presenting both opportunities and challenges. As AI tools become more accessible and cost-effective for students, the risk of academic dishonesty, particularly in writing assignments, escalates. Addressing this concern, the Education University of Hong Kong introduced the 6-P Pedagogy (Kong, Lee & Tsang, 2024) to enhance students’ information literacyContinue reading “Enhancing Self-Regulated Learning Efficacy in Undergraduate Education through the Integration of AI-Enabled Generative Tools and the 6-P Pedagogy: A Learning and Teaching Partnership Approach”
Studying Formative Feedback Strategies to Enhance Student Learning Outcomes
Formative feedback is generated by teachers as strategies to engage learners to constantly reflect on how they can approach, orient, and evaluate learning, which leads to successful learning outcomes. This project adopts a quasi-experiment to evaluate the effectiveness of formative feedback strategies on students’ academic and psychological outcomes. Students from four courses (N=127) were invitedContinue reading “Studying Formative Feedback Strategies to Enhance Student Learning Outcomes”