Learn to Question: Study on the Pattern of Student-GenAI Collaborative Learning in Higher Education

Abstract

The advent of generative artificial intelligence (GenAI), represented by ChatGPT, has challenged traditional learning mode. How students learn through GenAI tends to be an urgent problem to be explored in the current education and teaching reform in China. This study analyzed the in-class dialogues between undergraduates and GenAI by coding discourse types, questioning levels, questioning strategies, and self-reports of students to explore the learning model of human-AI collaboration. The findings showed that in student-GenAI dialogues dominated by students, there were more single-round dialogue units and fewer continuous discussions around a topic. The main types of students’ discourse were initial questioning, extended questioning, and rephrasing questioning, while evaluation and continuing prompt discourses were less prevalent. Moreover, students’ cognitive level of questioning was low, focusing on remembering-level questioning and understanding-level questioning. The use of questioning strategies was unfamiliar, and students seldom used role questioning, material questioning, and solution questioning. In addition, it was discovered that different task stages and different utilization experiences both led to different dialogues between students and GenAI. With the development of task solving, there were more frequent and sustained dialogues, along with a higher cognitive level and more proficient use of questioning strategies. Students with more experience of using GenAI generated more dialogues with a high cognitive level. In human-AI dialogues representing different characteristics, though there were different opinions on using GenAI in classroom teaching, most of the students held a positive attitude. In the students’ perception, GenAI has the advantage in generating responses, furnishing valuable information, handling various types of tasks, and fostering the development of student abilities, thereby assisting students in learning. Meanwhile, GenAI encounters challenges related to technical limitations, raising concerns about student development, learning assessment, and the overall educational ecosystem. On this basis, this study provides effective suggestions for further using GenAI in classroom teaching from three aspects: providing question guidance, enriching question scenarios, and strengthening reflection on GenAI responses.

Keywords

generative artificial intelligence (GenAI) / dialogue / question / project-based learning (PBL)

RIGHTS & PERMISSIONS

Higher Education Press