CV for Education Call for Papers

CALL FOR PAPERS

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


 Date: June 3 or 4, 2026
 Title: Computer Vision × Education: building a cross-community agenda for multimodal vision in classrooms (CV4Edu)
 URL: https://cv4edu.github.io/
 Paper deadline: March 12, 2026 
 CfP: see below

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 We are excited to invite submissions to CV4Edu, an interdisciplinary workshop at CVPR 2026 in Denver, bringing together researchers in AI in education, computer vision, and human-centered AI.

 The workshop focuses on multimodal perception in classrooms and the challenges of building interpretable, reliable, and privacy-aware AI systems for modeling engagement, self-regulation, and collaboration in real learning environments. 

 We welcome work on multimodal modeling, behavioral forecasting,
cognitive state inference, privacy-aware benchmarks, real-world
deployments, multimodal learning, CV "in the wild", etc. - as
long as the paper makes a clear link to education or learning
environments (even if that's primarily in the discussion), e.g., by
indicating applicability beyond benchmark datasets/tasks and
explaining potential relevance in noisy educational settings.

 Formats: Full, short, or position papers (archival/non-archival in CVPR Style)

 Submission deadline: March 12, 2026
 Website: https://cv4edu.github.io/

 We hope you'll join us.