From Flipped Classroom to AI-Enhanced Assessment

Source abstract

In response to the challenges posed by generative artificial intelligence and increasingly diverse student populations, this paper examines a decade-long programme of pedagogical innovation in undergraduate mathematics education. At a research-intensive university in Hong Kong, a coherent set of evidence-informed practices has been developed and scaled across courses of different sizes and natures. These include a fully flipped classroom model supported by animated lecture videos and an exercise generator, a deliberate shift towards open-book and oral assessments, and the adoption of an AI-assisted grading platform to provide timely and individualised feedback. Drawing on multi-year institutional student surveys, mid-term feedback and peer observations, the evaluation reveals consistently positive outcomes, as reflected in teacher and course effectiveness scores, while students also reported enhanced conceptual understanding, greater engagement, improved inclusivity and stronger communication skills. The integration of AI-assisted grading significantly improved the timeliness and quality of feedback, particularly in large-enrolment classes. Supported by competitive teaching development grants and recognised through institutional teaching awards, this sustained body of work contributes to the Scholarship of Teaching and Learning by demonstrating how practitioner-led innovation can be systematically developed, rigorously evaluated, and progressively institutionalised. Rather than offering a prescriptive model, the paper provides an adaptable example for STEM educators seeking to strengthen student learning and assessment practices in an era of rapid technological change.