# ConfusionBench: An Expert-Validated Benchmark for Confusion Recognition and Localization in Educational Videos

arXiv: https://arxiv.org/abs/2603.17267 · HTML: https://mlsanity.com/paper/2603.17267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
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- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [CLIP-KD: An Empirical Study of CLIP Model Distillation](https://mlsanity.com/paper/2307.12732/) (2023)
- [PromptKD: Unsupervised Prompt Distillation for Vision-Language Models](https://mlsanity.com/paper/2403.02781/) (2024)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021)
- [Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data](https://mlsanity.com/paper/2406.10502/) (2024)
- [Language-guided Human Motion Synthesis with Atomic Actions](https://mlsanity.com/paper/2308.09611/) (2023)
- [SignAvatar: Sign Language 3D Motion Reconstruction and Generation](https://mlsanity.com/paper/2405.07974/) (2024)
- [Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning](https://mlsanity.com/paper/2310.15985/) (2023)
- [Ig3D: Integrating 3D Face Representations in Facial Expression Inference](https://mlsanity.com/paper/2408.16907/) (2024)
- [AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot](https://mlsanity.com/paper/2503.06791/) (2025)
- [A General Model for Detecting Learner Engagement: Implementation and Evaluation](https://mlsanity.com/paper/2405.04251/) (2024)
- [MistyPilot: An Agentic Fast-Slow Thinking LLM Framework for Misty Social Robots](https://mlsanity.com/paper/2603.03640/) (2026)

## Abstract (arXiv)

Recognizing and localizing student confusion from video is an important yet challenging problem in educational AI. Existing confusion datasets suffer from noisy labels, coarse temporal annotations, and limited expert validation, which hinder reliable fine-grained recognition and temporally grounded analysis. To address these limitations, we propose a practical multi-stage filtering pipeline that integrates two stages of model-assisted screening, researcher curation, and expert validation to build a higher-quality benchmark for confusion understanding. Based on this pipeline, we introduce ConfusionBench, a new benchmark for educational videos consisting of a balanced confusion recognition dataset and a video localization dataset. We further provide zero-shot baseline evaluations of a representative open-source model and a proprietary model on clip-level confusion recognition, long-video confusion localization tasks. Experimental results show that the proprietary model performs better overall but tends to over-predict transitional segments, while the open-source model is more conservative and more prone to missed detections. In addition, the proposed student confusion report visualization can support educational experts in making intervention decisions and adapting learning plans accordingly. All datasets and related materials will be made publicly available on our project page.