About Me
👋 Hi! I’m Xiaomeng Huang, a PhD candidate in the Educational Communication and Technology program at New York University, where I am fortunate to be advised by Xavier Ochoa. My research bridges the learning sciences, AI in education, and learning analytics to address an enduring educational question: how can learners develop shared epistemic agency—the capacity to take responsibility for co-constructing knowledge with others—in human-human and AI-mediated collaboration?
I have also been an Ida Lawrence Research Intern at ETS Research Institute, where I was mentored by Jiangang Hao in the Assessment of Complex Skills group. Prior to NYU, I earned an Ed.M. in Technology, Innovation, and Education from the Harvard Graduate School of Education, advised by Bertrand Schneider, and worked with research teams across Harvard and MIT.
Research Program
Education is not only about what learners know, but who they become as knowledge co-constructors.
I study how learners develop shared epistemic agency with others: the capacity to take shared responsibility for creating and advancing knowledge together. My research integrates the learning sciences, educational measurement, and pedagogical design to understand how knowledge co-construction unfolds, develop theory-grounded and context-sensitive measures of the socio-cognitive capacities that sustain it, and design AI-supported environments that help those capacities grow.
Research Area 01
Knowledge Co-Construction in Human and AI Collaboration
How do ideas develop through human–human and human–AI interaction?
I study how ideas are introduced, taken up, revised, and transformed through interaction. My work develops analytic approaches that make the temporal and structural development of collective knowledge—and the distribution of knowledge-building work across human and AI participants—visible.
Selected work
Idea Tracing Analysis (ITA): A Temporal-Structural Methodology for Modeling Collaborative Discourse and Contextualizing Multimodal Interactions
Introduces a temporal-structural methodology that connects individual interaction to the evolution of collective knowledge.
Exploring the Potential of Generative AI to Support Non-experts in Learning Analytics Practice
Shows that responsible AI-supported analysis depends not only on prior expertise but on how users inspect, evaluate, and coordinate with AI-generated work.
Research Area 02
Measuring Socio-Cognitive Capacities that Sustain Shared Epistemic Agency
How can we make theory-grounded and context-sensitive inferences about learners’ capacities for knowledge co-construction?
I develop automated, theory-grounded methods for measuring socially situated capacities, such as active listening, by connecting multimodal behavioral evidence to the interactional contexts that give it meaning.
Selected work
Charting the Development of Collaboration Skills Through Collaborative Learning Analytics Systems
Introduces the TAP framework and identifies validity and learner-modeling requirements for systems designed to develop collaboration skills over time.
The TME Framework: Multimodal Learner Modeling for Active Listening Skills in Collaborative Problem Solving
Develops a theory-grounded framework that connects active-listening constructs to contextualized multimodal evidence and interpretable learner models.
Towards Automated Measurement of Active Listening Skills in Collaborative Problem Solving Using Multimodal Learning Analytics and Large Language Models
Develops an automated multimodal pipeline for detecting active-listening evidence in small-group collaborative problem solving.
Research Area 03
Pedagogical AI for Responsible Knowledge Co-Construction
How can AI help learners develop as knowledge co-constructors without taking over their epistemic work?
I design and study pedagogical AI that translates interpretable evidence about collaboration into actionable feedback while preserving learners’ responsibility for sensemaking and change.
Selected work
Unpacking the Complexity: Why Current Feedback Systems Fail to Improve Learner Self-Regulation of Participation in Collaborative Activities
Shows why participation visualizations alone produce uneven change and identifies interpretation, motivation, and context as conditions for actionable feedback.
Bridging Explainable Modeling and Actionable Feedback: Multimodal AI-Augmented Pedagogical Interventions for Developing Active Listening Skills in Collaborative Problem Solving
Connects interpretable learner evidence to context-grounded feedback designed to support reflection and future active-listening development.
Teaching & Mentoring
My teaching focuses on helping students become thoughtful and responsible users of emerging technologies. This philosophy parallels my research on shared epistemic agency: learning to work productively with AI requires judgment about what to delegate, what to verify, and what intellectual responsibility the learner must ultimately retain.
Selected course
Artificial Intelligence for Data Analysis in Education
Instructor, NYU · Fall 2026
A course on using AI responsibly throughout the educational data-analysis process, with an emphasis on critically evaluating AI outputs, making defensible analytical decisions, and retaining responsibility for the conclusions produced.
Mentoring
I mentor graduate and undergraduate researchers across education, learning design, computer science, and applied psychology. My mentoring emphasizes theory-driven inquiry, methodological rigor, and meaningful participation in collaborative scholarship.
Current and former mentees include student coauthors on CSCL and LAK publications.