Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation
- URL: http://arxiv.org/abs/2605.26918v1
- Date: Tue, 26 May 2026 12:16:41 GMT
- Title: Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation
- Abstract summary: We present EduVideoBench, the first balanced benchmark in the education domain.<n>Our results show substantial room for improvement across knowledge, skills, and attitude before they are classroom-ready.<n>We hope EduVideoBench will guide the development of VGMs that are pedagogically grounded and safe for the classroom.
- Score: 11.425144935556789
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Video generation models (VGMs) are rapidly entering classrooms, yet existing benchmarks evaluate only perceptual quality, intrinsic faithfulness, generic safety, or video as a reasoning medium, and none assesses whether the outputs are educationally valid. In this work, we present EduVideoBench, the first balanced benchmark in the education domain, grounded in the Knowledge-Skills-Attitude (KSA) framework so that pedagogical adequacy and educational safety are evaluated jointly rather than as ad-hoc quality dimensions. Across five frontier VGMs, our results show substantial room for improvement across knowledge, skills, and attitude before they are classroom-ready. We complement this with a qualitative analysis of expert comments, finding that educational validity is multi-component, where a single misaligned element such as pacing, legibility, or notation can invalidate an otherwise correct video. We hope EduVideoBench will guide the development of VGMs that are pedagogically grounded and safe for the classroom.
Related papers
- Video-OPSD: Exploiting Privileged Visual Evidence for On-Policy Self-Distillation in Video Large Language Models [58.45264308304815]
On-policy self-distillation (OPSD) has recently emerged as an effective post-training paradigm that improves policy optimization through dense token-level supervision from a privileged self-teacher.<n>We present $textbfVideo-OPSD$, an OPSD framework that exploits privileged visual evidence for both self-teacher construction and knowledge transfer.
arXiv Detail & Related papers (2026-08-27T12:51:32Z) - Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Framework [18.322787201155048]
We study child-oriented AIGC video reviewing, making three contributions.<n>First, we construct CAVSR, a benchmark of 605 real-world videos collected from multiple platforms.<n>Second, we propose QVRS-E, a knowledge- and experience-augmented video reviewing framework.
arXiv Detail & Related papers (2026-07-21T15:29:39Z) - EduVQA: Benchmarking AI-Generated Video Quality Assessment for Education [21.304581210099045]
We present EduAIGV-1k, the first benchmark dataset and evaluation framework dedicated to assessing the quality of AI-generated videos (AIGVs)<n>EduAIGV-1k contains 1,130 short videos produced by ten state-of-the-art text-to-video (T2V) models using 113 pedagogy-oriented prompts.
arXiv Detail & Related papers (2026-03-03T15:05:27Z) - Structured Over Scale: Learning Spatial Reasoning from Educational Video [6.358055069130984]
We introduce DoraVQA, a dataset of 5,344 question-answer pairs automatically extracted from 8 seasons of Dora the Explorer.<n>We fine-tune both Qwen2 and Qwen3, leveraging the clear correctness signals and structured reasoning traces inherent in educational content.<n>Our approach achieves improvements of 8-14 points on DoraVQA and state-of-the-art 86.16% on CVBench.
arXiv Detail & Related papers (2026-01-30T18:20:23Z) - VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics [83.61875204972465]
We introduce Video Connecting, a task that aims to generate smooth intermediate video content between given start and end clips.<n>To bridge this gap, we proposed VC-Bench, a novel benchmark specifically designed for video connecting.<n> VC-Bench focuses on three core aspects: Video Quality Score VQS, Start-End Consistency Score SECS, and Transition Smoothness Score TSS.
arXiv Detail & Related papers (2026-01-27T06:15:12Z) - EduGuardBench: A Holistic Benchmark for Evaluating the Pedagogical Fidelity and Adversarial Safety of LLMs as Simulated Teachers [8.123835490773095]
Large Language Models for Simulating Professions (SP-LLMs) are pivotal for personalized education.<n>EduGuardBench assesses professional fidelity using a Role-playing Fidelity Score (RFS)<n>It also probes safety vulnerabilities using persona-based adversarial prompts targeting both general harms and, particularly, academic misconduct.
arXiv Detail & Related papers (2025-11-10T09:42:24Z) - Video-SafetyBench: A Benchmark for Safety Evaluation of Video LVLMs [45.265397990158846]
Video-SafetyBench is the first benchmark designed to evaluate the safety of LVLMs under video-text attacks.<n>It comprises 2,264 video-text pairs spanning 48 fine-grained unsafe categories.<n>To generate semantically accurate videos for safety evaluation, we design a controllable pipeline that decomposes video semantics into subject images and motion text.
arXiv Detail & Related papers (2025-05-17T05:06:38Z) - MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [82.91830877219822]
We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
arXiv Detail & Related papers (2025-02-26T08:43:47Z) - Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions [7.706941074799756]
Saliency detection in videos addresses the automatic recognition of attention-drawing regions in single frames.
There is currently no study that evaluates saliency detection approaches in educational videos.
We reproduce the original studies and explore the replication capabilities for general-purpose (non-educational) datasets.
arXiv Detail & Related papers (2024-08-08T15:15:48Z) - T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models [39.15695612766001]
We introduce T2VSafetyBench, a new benchmark for safety-critical assessments of text-to-video models.
We define 12 critical aspects of video generation safety and construct a malicious prompt dataset.
No single model excels in all aspects, with different models showing various strengths.
There is a trade-off between the usability and safety of text-to-video generative models.
arXiv Detail & Related papers (2024-07-08T14:04:58Z) - MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding [67.56182262082729]
We introduce MMBench-Video, a quantitative benchmark to rigorously evaluate large vision-language models (LVLMs) in video understanding.
MMBench-Video incorporates lengthy videos from YouTube and employs free-form questions, mirroring practical use cases.
The benchmark is meticulously crafted to probe the models' temporal reasoning skills, with all questions human-annotated according to a carefully constructed ability taxonomy.
arXiv Detail & Related papers (2024-06-20T17:26:01Z) - Paxion: Patching Action Knowledge in Video-Language Foundation Models [112.92853632161604]
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Recent video-language models' impressive performance on various benchmark tasks reveal their surprising deficiency (near-random performance) in action knowledge.
We propose a novel framework, Paxion, along with a new Discriminative Video Dynamics Modeling (DVDM) objective.
arXiv Detail & Related papers (2023-05-18T03:53:59Z) - Privileged Knowledge Distillation for Online Action Detection [114.5213840651675]
Online Action Detection (OAD) in videos is proposed as a per-frame labeling task to address the real-time prediction tasks.
This paper presents a novel learning-with-privileged based framework for online action detection where the future frames only observable at the training stages are considered as a form of privileged information.
arXiv Detail & Related papers (2020-11-18T08:52:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.