Currently, generative AI has undergone rapid development. Numerous studies have attested to the benefits of Gen AI in programming, mathematics and other disciplines. However, since Gen AI mostly uses English as the intrinsic training parameter, it is more effective in facilitating the teaching of courses that use international common notation, but few scholars have researched the fitness of Gen AI-assisted teaching of humanities courses in Chinese-language environments. To address these gaps, this study examined the learning behaviours of 30 students using Gen AI to help them answer questions on economic law tests using the Lag Sequential Analysis. The results show that the following: (1) The use of Gen AI to aid learning in an economic law course did not significantly improve the cognitive level of academics from the perspective of knowledge construction. (2) According to the characteristics of students' behavioural paths via Gen AI-assisted learning, their behavioural patterns can be classified into "autonomous and innovative," "moderate," and "lacking innovation." (3) Different learning modes when Gen AI-assisted teaching was used affected the final results, which were as follows: High-performing students favoured the "autonomous and innovative pattern," medium-performing students favoured the "moderate" pattern, and low-performing students favoured the "lacking innovation" pattern.