arXiv — NLP / Computation & Language · · 3 min read

Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation

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Computer Science > Computation and Language

arXiv:2605.26918 (cs)
[Submitted on 26 May 2026]

Title:Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation

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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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2605.26918 [cs.CL]
  (or arXiv:2605.26918v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.26918
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Unggi Lee [view email]
[v1] Tue, 26 May 2026 12:16:41 UTC (3,467 KB)
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