LLMs are widespread across educational and professional environments, often used to tackle tasks beyond users' prior expertise. {However, there is limited work on task-agnostic, turn-level frameworks to characterize human communication styles with LLMs that are linked to better task outcomes.} We introduce TurnStyle, a framework that provides a turn-level taxonomy of human contributions in human–AI conversations, grounded in collaborative learning theory, with a reliability protocol and open-source tooling for public datasets. We apply TurnStyle to two public multi-turn corpora with objective outcomes – StudyChat (college-level assignments) and DevGPT (software engineering issues and pull requests (PRs)) – and to a workplace reskilling randomized trial in which management consultants used ChatGPT for coding, statistics, and machine learning. Across 3,365 conversations with 26,335 human turns spanning the three datasets, mixed-effects and sequence analyses converge on task-agnostic, trainable behaviors that predict task success; this has implications for training, evaluation, and the design of collaborative AI systems.
ACM CHI Conference on Human Factors in Computing Systems