Exploring Teachers’ Perspectives and Strategies on Student Assessment Using Generative AI in Chinese Universities

Generative artificial intelligence (AI) provides changes to higher education, which has attracted widespread attention from researchers and educators. However, the existing research has not comprehensively examined how university teachers perceive the use of generative AI for student assessment and what strategies should be implemented when applying this technology in China. This study employed mixed-method research to explore teachers’ perceptions of student assessment using generative AI. We collected data through interviews and surveys from Chinese universities. Our findings demonstrate that a majority of teachers agreed with the power of AI tools to enhance the overall quality of the evaluation and their work efficiency, since they can alleviate the burden associated with manual grading and offer prompt feedback. University teachers expect to prepare their knowledge and skills for the usage of new technologies through training programs and professional development opportunities. Besides, this study notes some concerns like data security and privacy fairness, biases inherent in AI algorithms, overreliance on automation, and limited critical human thinking. These challenges lead to the need for further explorations to provide holistic feedback and balance the AI assessment approach with human judgment to meet reasonable standards. This study provides a general introduction to AI-assisted assessment transformation in the university setting, and more empirical inquiries on different types, phases, objectives, and strategies of assessment in practices are suggested.