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).