CV4Edu 2026

CV4Edu - Computer Vision for Education

Computer vision (CV) plays a central role in multimodal 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., video, audio, text). Students’ cognitive/behavioral states (e.g., engagement, mind-wandering) and learning processes (e.g., self-regulation, collaboration) can be inferred from subtle multimodal cues (e.g., gaze, pose, facial features). Still, today’s models struggle to generalize to classroom data, limiting reliability in deployed human-centered applications (e.g., assistive technology, collaborative AI). CV4Edu brings together computer vision, natural language processing, human-computer interaction, and educational researchers to chart a community agenda for efficient, privacy-aware multimodal data-driven models that are more reliable in low-resource, real-world classroom settings — potentially launching shared datasets, metrics, and unified practices.

Our goal is to support research that bridges CV, NLP, HCI, cognitive science, and the learning sciences/education communities. We welcome submissions both within and beyond education contexts—such as multimodal modeling, sensing, behavior forecasting, cognitive state inference, robotics, and embodied AI—provided they discuss transferability to classroom settings (e.g., what may break or carry over under noise, occlusions, viewpoints, multi-person dynamics, privacy constraints, limited annotations, distribution shift, hardware variability).

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

Language-centered multimodal learning analytics

Linking speech/text to video events, gaze/attention, and instructional context
Classroom NLP: ASR robustness, diarization, evaluating and mitigating bias, discourse modeling, dialogue/tutoring interactions, simplification, misconception detection
Retrieval-augmented classroom analytics, model adaptation, evaluation for learning-aligned outcomes

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 encourage general computer-vision, visually grounded NLP, and human-centered, collaborative AI submissions (e.g., behavioral modeling, pose/activity recognition, gaze estimation, attention modeling, multimodal learning, methods “in the wild”, cognitive state inference and forecasting) that make a clear connection to educational/learning environments (even if primarily in the discussion).

Call for Papers

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.

Submission Tracks

All submissions must follow the CVPR 2026 paper template and official style guidelines.

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/early-stage work (e.g., 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). Our goal is to foster discussion and community building. 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

Each submission will undergo a rigorous double-blind review by at least three reviewers. Conflicts of interest will be managed through the OpenReview platform.
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)

Archival Paper Submission Deadline: ~~March 12, 2026~~ March 16, 2026
Archival Notification of Decision: ~~April 3, 2026~~ March 24, 2026
Archival Camera-Ready Deadline: April 10, 2026
Non-Archival Submission Deadline: May 10, 2026
Non-Archival Notification: May 15, 2026

Note: The archival notification date is earlier this year due to updated IEEE metadata submission requirements.

Submission Site

Papers can be submitted through the OpenReview Submission Site.

Note: Submission for non-archival papers will open soon.

Submit paper

Invited Speakers

Tentative Schedule

1:00PM	Opening & Goals
1:10PM	Keynotes
2:20PM	Poster Session 1 + Coffee Break
2:25PM	Keynotes
3:55PM	Poster Session 2 + Coffee Break
4:20PM	Panel: From Lab to In‑The‑Wild
5:00PM	Community Discussion
5:20PM	Closing & Next Steps