human-AI collaboration

Principal Trait Analysis linked AI-tutor dialogue patterns to outcomes, but not yet to transferable skills

Hunter McNichols, Kai Du, Andrew Lan

arXiv preprint

McNichols, Du and Lan introduce Principal Trait Analysis, an LLM-assisted pipeline that turns human-AI conversation traces into interpretable behavioral traits. On 1,540 university AI-tutor sessions and 2,774 professional coding-agent sessions, selected traits added explanatory or predictive signal beyond prior performance. Conceptual questioning aligned positively with some exam outcomes, but cross-semester inconsistency, contradictory coefficients and mostly flat temporal patterns mean the traits cannot yet be treated as transferable AI collaboration skills.