CV4Edu: Computer Vision × Education: building a cross-community agenda for multimodal vision in classrooms
In conjunction with CVPR 2026, June 3 or 4, Denver, CO, US.
Website: https://cv4edu.github.io/

Computer vision (CV) plays a central role in human-centered AI, yet most models are trained on web-scale benchmarks that poorly reflect real classrooms. Educational data are noisy, private, small-scale, and multimodal (e.g.,face, gaze, pose). Students’ cognitive/behavioral states (e.g.,engagement, mind-wandering) and learning processes (e.g.,self-regulation, collaboration) can be inferred from subtle cues in the lab. Still, today’s models struggle to generalize to noisy classroom data. CV4Edu brings together computer vision, human-computer interaction, and educational researchers to chart a community agenda for efficient, privacy-aware multimodal data-driven models that work more efficiently and reliably in low-resource, real-world classrooms — potentially launching shared datasets, metrics, and unified practices.

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TOPICS

The workshop topics include (but are not limited to):

Multimodal classroom perception
- Face, gaze, pose, gesture, posture, affect, and prosody
- Video, audio, gaze sensors, and wearables (egocentric and exocentric)
- Multimodal fusion, representation learning, and cross-view / multi-camera setups

Robustness & generalization
- Domain shift beyond the lab, occlusions, noisy data, and missing modalities
- Few-/low-shot learning, continual and on-device adaptation
- Generalization across classroom layouts and populations

Human behavior modeling for learning
- Engagement, attention, affect, confusion, self-regulation, and metacognition
- Collaboration, group dynamics, and teacher–student interactions
- Gaze-informed models, saliency/scanpath prediction, activity recognition

Temporal modeling & intervention
- Sequential/temporal models of learning processes
- Behavioral forecasting, early-warning systems, and interventions
- Real-time inference, feedback, and human-in-the-loop systems

Interpretability, reliability & evaluation
- Interpretable models, uncertainty estimation, and calibration
- OOD detection, fairness, and bias analysis
- Evaluation protocols aligned with learning outcomes

Privacy-aware AI, datasets & deployments
- Privacy-preserving data collection, anonymization, de-identification, and governance
- Annotation strategies, construct-aligned labeling, active learning, synthetic data, and dataset curation
- Classroom-ready systems, scalable multimodal data-collection frameworks, edge/on-device inference, and real-world deployments

We also welcome general computer-vision work (e.g., pose/activity recognition, gaze estimation, multimodal learning, CV “in the wild”) that clearly connects to educational or learning environments (even if primarily in the discussion).

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SUBMISSIONS

The workshop invites submissions presenting original research, emerging ideas, datasets and benchmarks, systems, applications, and position papers advancing computer vision for real-world educational settings. We welcome both archival and non-archival contributions, including early-stage work and previously published research, with the goal of fostering discussion and community building.

All submissions must follow the CVPR 2026 paper template and official style guidelines (https://cvpr.thecvf.com/Conferences/2026/AuthorGuidelines).

Archival Track (Full Papers)
Papers submitted to the Archival Track must present original, unpublished work and will be considered for inclusion in the official CVPR 2026 workshop proceedings. The main text must be 6–8 pages in length and formatted according to the CVPR 2026 submission guidelines. References and appendices are not subject to a page limit.

Non-Archival Track (Extended Abstracts + Short / Position Papers)
We invite non-archival submissions describing ongoing projects, preliminary results, datasets or benchmarks in progress, negative results, lessons learned, position papers, and work previously published elsewhere (including papers on arXiv or at other venues). These submissions will not be included in the official proceedings. Extended abstracts may be up to 2 pages and short/position papers up to 4 pages (excluding references), formatted according to the CVPR 2026 submission guidelines.

Review Process
- All submissions will undergo double-blind peer review.
- Archival submissions will receive at least two reviews, followed by a meta-review.
- Submissions must comply with CVPR policies.
- An ethics/IRB checklist is required where applicable, and an optional ethics and broader impact statement may be included.

Important Dates (AoE)
- Paper Submission Deadline (All Tracks): March 12, 2026
- Notification of Decision: April 3, 2026
- Camera-Ready Deadline (Archival Only): April 10, 2026

Submission Site
Papers can be submitted through the OpenReview Submission Site (https://openreview.net/group?id=thecvf.com/CVPR/2026/Workshop/CV4Edu).

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ORGANIZERS

Ekta Sood: University of Colorado Boulder
Joyce Horn Fonteles: Vanderbilt University
Mariah Bradford: Colorado State University
Paul Gavrikov: Independent researcher
Prajit Dhar: University of Marburg
Janis Pagel: University of Cologne
Trisha Mital: Dolby Laboratories
Gautam Biswas: Vanderbilt University
Sidney D'Mello: University of Colorado Boulder