From search to work tasks: exploring students’ AI chatbot information practices in higher education
DOI:
https://doi.org/10.47989/ir31iConf64180Keywords:
AI chatbots, Task-based information practices, Information behaviour, Higher education, Activity theoryAbstract
Introduction. Research on AI chatbot use in higher education has largely focused on undergraduates, often overlooking graduate students or treating both groups as a single population. Drawing on activity theory, this study examines how task type shapes differences in AI chatbot use among undergraduate and graduate students. Distinguishing task types is theoretically important because shifts in the object of activity shape how students perceive agency, organise information practices, and engage with AI tools.
Method. Eighteen students participated in semi-structured interviews. Transcripts were coded in NVivo using activity theory components: Subject, Object, Tools, Rules, Community, Division of Labor, and Outcome.
Analysis. A deductive coding approach identified how students engaged with AI tools in relation to different learning tasks. Data triangulation enhanced credibility by combining self-reported reflections with interaction records.
Results. Undergraduates primarily used AI for course-related search tasks, often accepting responses with limited verification. Graduate students engaged in more complex work tasks and were more likely to verify AI outputs with secondary sources.
Conclusion. Task type influenced students’ information interaction, particularly in credibility judgments and ethical considerations. These findings highlight the importance of task-based distinctions in AI literacy instruction and suggest tailoring guidance to both education level and task context.
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