Seeking Knowledge or Efficiency: Profiling students’ AI-use through survey-based Latent Class Analysis

The spread of easily accessible generative AI in the form of chatbots has impacted secondary education, but the effects of this are largely unknown. Previous studies have shown that using chatbots in a learning context can be both harmful or helpful depending on how they are used. While students are undoubtedly using this technology, there is scarce data about the extent, intention or approach in their usage, or what drives this. Additionally, there is virtually no data on what differences there are in usage between different groups of students. The present study builds upon the findings of a previous qualitative study, aiming to investigate and quantify students’ use of generative AI for schoolwork. Through a survey sent to multiple upper secondary schools, we collected 1266 responses to analyse upper secondary students' attitudes toward, usage of, support for, and knowledge about generative AI. One thousand of these students had used generative AI at least once. Based on their answers, we present an overview of their usage and knowledge of AI through descriptive statistics. For further analysis, a Latent Class Analysis was conducted and four distinct response patterns among students identified: AI-positive knowledge-seekers, Cautious AI-adopters, AI-skeptics and Efficiency-seekers. These four classes were then used to see differences relating to gender, grade, choice of study programme, attitude to knowledge, neuropsychiatric diagnoses and non-native Swedish speaking students. We find that students use generative AI for schoolwork primarily as support for the process of doing their schoolwork but also as a shortcut for tasks perceived as meaningless. We find that the identified patterns of attitudes, knowledge and usage exhibit behaviours that are in different ways both promising and worrisome, and that warrants different courses of action in education. Further analysis reveals significant differences between high-performing and low-performing students and different programmes which indicate risk of a widening divide between student groups. No significant differences were found in relation to gender or diagnosis, but tendencies in the latter are discussed as needing more research. This research contributes by identifying differences in student behaviour and attitudes towards AI, and points to needs for further research in the diversity of behaviour and the consequences of different use patterns, as well as the need to tailor educational support for different student groups.