#human-AI collaboration

human-AI collaboration

2 papers

A diverse group of educators reviews an instructional video storyboard, narration timeline, and multimedia-learning checks at a university media lab

Conference Paper2026

Dual gatekeeping improved ratings of AI-generated instructional video without testing student learning

Yearim Kim, Njun Baek, Nojun Kwak

CHI 2026 Workshop on Understanding and Engaging Critical Resistance to AI in Education

Kim, Baek and Kwak present PedaCo, an AI-video workflow that lets educators revise scripts against multimedia-learning principles before synthesis and then checks the rendered video with automated pedagogical metrics. Twenty-three educators rated reviewed videos higher than baselines, while a 14-video comparison improved coherence and temporal alignment. The workshop study did not measure student learning or long-term teacher workload.

AI-generated videomultimedia learningeducator review

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A diverse group of university students and a lecturer examine clustered dialogue cards, a ten-trait matrix and an exam-progress chart in a bright learning analytics studio

Tool / Dataset2026

Tool / Dataset 94

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.

Principal Trait AnalysisAI tutoring dialoguehuman-AI collaboration

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