Chat as learning: Student–AI conversations as discipline-associated cognitive engagement patterns

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Generative AI teaching assistants are increasingly adopted in higher education, yet debates persist over whether student–AI conversations engage students in genuine cognitive processes or function primarily as information retrieval. We propose “Chat as Learning,” a measurement paradigm that treats student prompts as observable externalisations of in-progress learning cognition, complementing outcome-based assessment with a process-level signal aggregable at population, course, and student scales. We adopt the term learning to denote the paradigm’s theoretical positioning; the present study measures cognitive demand reflected in students’ prompts, classified via Bloom’s Taxonomy, rather than learning outcomes per se. Building on prior work establishing that approximately 62% of student–AI messages on this platform reflect higher-order cognitive demand at the aggregate level Chang and Li, 2026, we ask whether such engagement is stable across contexts or varies with disciplinary demands within the same individual. Drawing on data from the Uedu AI teaching assistant platform ( https://uedu.tw ), a multi-institutional deployment spanning four universities, we analysed over 60,000 student messages from 116 courses across two academic semesters using an automated Bloom’s Taxonomy classification pipeline. The classifier was validated against two trained human raters on a 300-message stratified sample, giving binary higher-order vs. lower-order LLM–human Cohen’s – at the individual level and under best-pair consensus (full 6 6 confusion matrix in Table 1). Our within-person, cross-discipline design—in which the same students were tracked across courses in different disciplines—revealed discipline-associated Bloom-level prompt profiles: STEM courses elicited Apply-prevalent prompts (20.8%), Language courses showed Understand-prevalent patterns (31.7%), and Social Science courses were Create-prevalent (33.8%); patterns in Humanities courses are reported descriptively but should be interpreted with caution given the small course base (3 courses across two semesters). Paired within-person comparisons indicated that the same students produced significantly higher proportions of higher-order prompts in Social Science courses than in STEM courses (pooled , ), with the same direction of effect observed in two independent semester samples. A crossed random-effects mixed-effects logistic regression confirmed these contrasts and showed that course-level variation in higher-order engagement substantially exceeds student-level variation. These results are consistent with the view that student–AI conversations reflect discipline-associated cognitive engagement rather than fixed individual interaction styles, and they suggest that AI teaching assistants may benefit from being designed and evaluated with disciplinary context in mind.