Exploring University Students' Preferences for AI-Assisted Learning Environment: A Drawing Analysis with Activity Theory Framework

This study employed drawing and co-word analysis techniques to explore students' preferences for AI-assisted learning environments. A total of 64 teacher education students from a university in Taiwan participated in the study. The participants were asked to describe their perceptions of AI-assisted learning in the form of drawings and text descriptions. In order to analyze the content of the students' drawings, a coding scheme was developed based on the activity theory framework. Based on the results of the analysis, it was found that students placed more importance on personalized guidance and appropriate learning content provision. In addition, students acknowledged that AI technology can be used flexibly in different fields and situations. Interestingly, more than half of the students agreed that robots play important roles in AI-assisted learning. This indicates that the students expected a social AI learning companion. However, it was found that students' expectations of an AI learning environment were less connected to the real environment and did not reveal learning activities with higher order thinking. In addition to the need for accurate and fast AI computing, this result indicated that professional instructional guidance is also an expectation that students have of AI education.