About

Bertrand Schneider is an Associate Professor of Education at Harvard Graduate School of Education. He holds a Ph.D. from Stanford University, earned in 2015. His research focuses on how emerging technologies and multimodal data can transform the understanding and support of learning. Schneider combines sensors, artificial intelligence, and innovative interfaces to capture the rich dynamics of collaboration, including how students talk, move, focus, and feel as they work through complex problems in STEM and beyond. His work aims to make invisible processes visible, designing learning environments that foster deeper understanding, belonging, and engagement, while reimagining how to measure and nurture human learning in its collaborative, embodied, and emotional dimensions.

Research topics

• Computer science
• Human–computer interaction
• Multimedia
• Data science
• Artificial intelligence

Selected publications

• Connecting joint visual attention, community belonging, and self-efficacy in makerspace environments using high frequency data

  Learning and Instruction · 2026-02-03 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Corrigendum to “Connecting joint visual attention, community belonging, and self-efficacy in makerspace environments using high frequency data” [Learning and Instruction 103 (2026) 102315]

  Learning and Instruction · 2026-03-01

  article1st authorCorresponding
  PublisherDOI

• Implementing multimodal learning analytics in authentic settings: A roadmap for ecological impact

  Learning and Instruction · 2026-03-02

  article1st authorCorresponding
  PublisherDOI

• A quarter century of advances in collaboration sensing: a comprehensive review of high-frequency metrics and their connection to collaborative constructs from 2000 to 2025

  Universität Zürich, ZORA · 2026-01-08

  articleOpen access1st authorCorresponding
  PublisherDOI

• Capturing Activities and Interactions in Makerspaces Using Monocular Computer Vision

  IEEE Transactions on Learning Technologies · 2025-01-01 · 1 citations

  articleOpen access

  This study presents a monocular approach for capturing students' prototyping activities and interactions in digital fabrication-based makerspaces. The proposed method uses images from a single camera and applies object re-identification, tracking, and depth estimation algorithms to track and uniquely label participants in the space, extracting both spatial and temporal information. A case study was conducted by recording a lab session in a digital fabrication-based makerspace where students from a university undergraduate program turned their product ideas into tangible prototypes using digital fabrication. Moreover, a creativity test was conducted to assess individual creative competence. The findings reveal that the monocular approach effectively captures interactions among team members and instructors. It also identifies prototyping activities at individual and team levels. Furthermore, results demonstrate that the students with high and low creativity scores exhibit distinct patterns of interaction with instructors and teammates. Those with high creativity scores worked more independently and less collaboratively. Students with low creativity scores worked more collaboratively and less independently. The proposed monocular approach can be used in formal educational settings for student evaluation and prototyping activities. Additionally, instructors can use this approach to assess and tailor teaching methods by promptly intervening and providing structures and scaffolding support to assist struggling students.

  PublisherDOI

• Using AI to Care: Lessons Learned from Leveraging Generative AI for Personalized Affective-Motivational Feedback

  International Journal of Artificial Intelligence in Education · 2025-01-15 · 10 citations

  articleSenior author
  PublisherDOI

• A quarter century of advances in collaboration sensing: a comprehensive review of high-frequency metrics and their connection to collaborative constructs from 2000 to 2025

  International Journal of Computer-Supported Collaborative Learning · 2025-11-22 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Can Money Buy Better Judgment? High Incentives Backfire on Misinformation Discernment, But AI May Fix It

  Proceedings. · 2025-06-10

  articleOpen accessSenior author

  Misinformation susceptibility is often attributed to motivational lapses (failure to engage in analytical reasoning) or information gaps (lack of necessary knowledge).While prior research has explored these factors separately, little is known about their interaction.This study examines how monetary incentives (to increase motivation) and AI-generated credibility analyses (to provide informational support) influence misinformation discernment.In a 23 factorial experiment (N=300), we find that high monetary incentives reduce discernment by increasing skepticism and false negatives.However, when paired with AI analyses, this effect is reversed, improving performance beyond baseline.AI access alone had no significant impact.Qualitative findings suggest trust in AI depends on self-reliance, skepticism, and alignment with personal judgment.These results highlight the need for interventions that balance motivation and cognitive support to enhance misinformation discernment and calls for more research to understand the cognitive and metacognitive processes during misinformation discernment.

  PublisherOA PDFDOI

• Exploring Human-AI Interactions Across Various Learning Tasks: Diverse Perspectives on Augmenting Learner

  Computer-supported collaborative learning/˜The œComputer-Supported Collaborative Learning Conference · 2025-06-10

  articleOpen access

  This symposium focuses on understanding learners' interactions with artificial intelligence (AI), particularly chatbots and agents, to advance human-AI collaboration research from the perspective of computer-supported collaborative learning (CSCL).The four papers discuss human-AI interactions in various learning tasks and domains, especially from an augmentation perspective.The studies examine how these interactions can support learning processes and how learners perceive AI as an interaction partner (or not).By exploring these augmentation dynamics across tasks and domains, the symposium contributes to the evolving understanding of human-AI collaboration in teaching and learning.The symposium's discussant will synthesize insights from these studies to shed light on how these studies contribute knowledge and how these approaches need to be further developed to contribute to future hybrid intelligence (HI) systems in education.discussant Mutlu Cukurova (10-min).This will allow for a 15-minute general discussion between the symposium participants and the session attendees.At the beginning of their presentation, each presenter will define how their current research advances human-AI collaboration research from the perspective of computer-supported collaborative learning (CSCL) and contributes to the development of hybrid intelligence systems in education.

  PublisherOA PDFDOI

• Exploring how shared gaze visualizations support remote one-on-one teaching: A mixed method study

  Journal of the Learning Sciences · 2025-03-17 · 2 citations

  article1st authorCorresponding
  PublisherDOI

Recent grants

• Making With Understanding: Using Augmented Reality to Support Peer Teaching in Makerspaces

  NSF · $750k · 2019–2023

• EAGER: Making with Understanding

  NSF · $300k · 2017–2020

Frequent coauthors

• Iulian Radu

  Harvard University

  20 shared

• Paulo Blikstein

  Columbia University

  17 shared

• Roy D. Pea

  14 shared

• Edwin Chng

  10 shared

• Gahyun Sung

  University of Iowa

  9 shared

• A. F. Villon

  7 shared

• Pierre Dillenbourg

  École Polytechnique Fédérale de Lausanne

  7 shared

• Joseph Reilly

  6 shared

Labs

• LIT LabPI

Awards & honors

• Bertrand Schneider Named Associate Professor