Artificial intelligence (AI) is increasingly significant in reshaping how education is designed and delivered, enabling adaptive and personalized learning environments. As AI systems now automate substantial aspects of both learning processes and learning outputs, student produced work can no longer be relied on as the only indicator of learning achievement. This shift requires teachers to prioritize the development of interpretive, ethical, and reflective capacities that allow learners to navigate AI mediated environments with critical awareness and discernment. In response to these emerging challenges, the Chinese Language Enhancement Programme (CLEP) at School of Chinese, the University of Hong Kong, has developed a new learning framework that help to balances AI assisted processes with human engagement. Beginning in the 2024 academic year, some CLEP courses piloted the Human–AI Synergy Learning Model (HASLM) in designing learning activities and course assessments. The model consists of five sequential stages: data collection, human–machine collaboration, interpretation, decision making, and reflection. These stages establish a structured learning cycle that integrates AI tools while encouraging students to contribute human judgment, creativity, and critical thinking. This paper evaluates the effectiveness of HASLM through an analysis of learning cases, teacher feedback, and student survey data. The findings reveal the degree to which this hybrid approach supports deeper learning within AI enhanced educational environments. The results also help us rethink and redesign class assignments and learning activities in educational contexts where AI now plays an increasingly significant and lasting role.

Authors:
Ming Wai Christy Chung, The University of Hong Kong, Hong Kong
Chi Ming Chan, The University of Hong Kong, Hong Kong

About the Presenter(s)
Dr Ming Wai Christy Chung is a University Assistant Professor/Lecturer at The University of Hong Kong in Hong Kong

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