collaborators

12 papers

cs.CL2026

Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification

Haocheng Zhang, Zhuqian Zhou, Kirk Vanacore +2

Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII)…

cs.HC2026

A Causal Framework for Estimating Heterogeneous Effects of On-Demand Tutoring

Kirk Vanacore, Danielle R Thomas, Digory Smith +3

This paper introduces a scalable causal inference framework for estimating the immediate, session-level effects of on-demand human tutoring embedded within adaptive learning system…

cs.CY2026

Who Decides in AI-Mediated Learning? The Agency Allocation Framework

Conrad Borchers, Olga Viberg, René F. Kizilcec

As AI-mediated learning systems increasingly shape how learners plan, make decisions, and progress through education, learner agency is becoming both more consequential and harder…

cs.CY2026

Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability

Yan Tao, Olga Viberg, Deepak Varuvel Dennison +2

Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely…

cs.HC2026

Does the TalkMoves Codebook Generalize to One-on-One Tutoring and Multimodal Interaction?

Corina Luca Focsan, Marie Cynthia Abijuru Kamikazi, Tamisha Thompson +5

Accountable Talk theory has been widely adopted to analyze classroom discourse and is increasingly used to annotate tutoring interactions. In particular, the TalkMoves codebook, gr…

cs.CY2026

Million Tutoring Moves (MTM): An Open Multimodal Dataset for the Science of Tutoring

René Kizilcec, Kirk Vanacore, Zhuqian Zhou +8

We introduce the Million Tutoring Moves (MTM) project, an open dataset initiative aimed at advancing the science of tutoring through large-scale, reusable, and multimodal interacti…