Sun Jun 28 · 9:00 AM · COEX Room 305
Advancing the Science of Human and AI Tutoring through Shared Infrastructure: A Collaborative Workshop
← All speakers
R
René Kizilcec
★★Cornell
14 sessions speaking
Known for
Learning at scale; algorithmic fairness and equity in digital education
Recognition
NSF CAREER Award; multiple Best Paper awards; General Chair ACM L@S 2022
★ Speaking (14)
Mon Jun 29 · 10:45 AM · Room 104
Does the TalkMoves Codebook Generalize to One-on-One Tutoring and Multimodal Interaction?
Mon Jun 29 · 11:10 AM · North 205
From Tutor Moves to Tutoring States: Modeling the Timing and Sequencing of Pedagogical Strategies for Student Engagement
Mon Jun 29 · 3:45 PM · Auditorium
Rethinking Assessment in the Age of AI: From Snapshots to Continuous Evidence Infrastructure
Mon Jun 29 · 4:35 PM · North 205
Comparing Teacher and AI-Generated Feedback in the Writing Classroom: Experimental Results from Secondary School Classrooms
Mon Jun 29 · 4:35 PM · North 210
Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
Tue Jun 30 · 10:45 AM · North 203
Teachers’ Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability
Tue Jun 30 · 10:45 AM · North 209
LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse
Tue Jun 30 · 11:10 AM · North 203
The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essays
Tue Jun 30 · 2:50 PM · North 205
Who Decides in AI-Mediated Learning? The Agency Allocation Framework (AAF)
Fri Jul 3 · 9:00 AM · North 205
How Well Do Large Language Models Recognize Instructional Moves? Establishing Baselines for Foundation Models in Educational Discourse
Fri Jul 3 · 10:45 AM · North 202
A Causal Framework for Estimating Local Effects of On-Demand Tutoring
Fri Jul 3 · 11:10 AM · North 203
A Large-Scale Analysis of Student Behavior with Pedagogically Constrained LLM Tutors
Fri Jul 3 · 2:00 PM · North 203
Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education