@inproceedings{jin-etal-2026-pedagogybench,
title = "{P}edagogy{B}ench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding",
author = "Jin, Xiaokang and
Zhu, Jia and
Liu, Jingjiang and
Shi, Yabing and
Guan, Jueqi and
Chen, Hao and
De Meo, Pasquale",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.614/",
pages = "12621--12647",
ISBN = "979-8-89176-395-1",
abstract = "Existing video understanding benchmarks mainly emphasize general visual recognition and reasoning, but do not adequately capture the pedagogical logic embedded in instructional videos. To address this gap, we present PedagogyBench, a multimodal benchmark for instructional video understanding grounded in pedagogical cognition. We introduce a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement, enabling the construction of a dataset organized around a cognitive pyramid with four levels and 20 fine-grained tasks. We further propose the Cognitive Fidelity Score (CFS) to measure the balance of model performance across pedagogical cognitive dimensions. Experiments on 12 multimodal large language models reveal a clear generative gap, where models perform relatively well on discriminative tasks but degrade on higher-order pedagogical diagnosis, often relying on parametric memory rather than grounded visual perception. Project resources are available at https://github.com/Shallcom/PedagogyBench."
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<abstract>Existing video understanding benchmarks mainly emphasize general visual recognition and reasoning, but do not adequately capture the pedagogical logic embedded in instructional videos. To address this gap, we present PedagogyBench, a multimodal benchmark for instructional video understanding grounded in pedagogical cognition. We introduce a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement, enabling the construction of a dataset organized around a cognitive pyramid with four levels and 20 fine-grained tasks. We further propose the Cognitive Fidelity Score (CFS) to measure the balance of model performance across pedagogical cognitive dimensions. Experiments on 12 multimodal large language models reveal a clear generative gap, where models perform relatively well on discriminative tasks but degrade on higher-order pedagogical diagnosis, often relying on parametric memory rather than grounded visual perception. Project resources are available at https://github.com/Shallcom/PedagogyBench.</abstract>
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%0 Conference Proceedings
%T PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding
%A Jin, Xiaokang
%A Zhu, Jia
%A Liu, Jingjiang
%A Shi, Yabing
%A Guan, Jueqi
%A Chen, Hao
%A De Meo, Pasquale
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jin-etal-2026-pedagogybench
%X Existing video understanding benchmarks mainly emphasize general visual recognition and reasoning, but do not adequately capture the pedagogical logic embedded in instructional videos. To address this gap, we present PedagogyBench, a multimodal benchmark for instructional video understanding grounded in pedagogical cognition. We introduce a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement, enabling the construction of a dataset organized around a cognitive pyramid with four levels and 20 fine-grained tasks. We further propose the Cognitive Fidelity Score (CFS) to measure the balance of model performance across pedagogical cognitive dimensions. Experiments on 12 multimodal large language models reveal a clear generative gap, where models perform relatively well on discriminative tasks but degrade on higher-order pedagogical diagnosis, often relying on parametric memory rather than grounded visual perception. Project resources are available at https://github.com/Shallcom/PedagogyBench.
%U https://aclanthology.org/2026.findings-acl.614/
%P 12621-12647
Markdown (Informal)
[PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding](https://aclanthology.org/2026.findings-acl.614/) (Jin et al., Findings 2026)
ACL
- Xiaokang Jin, Jia Zhu, Jingjiang Liu, Yabing Shi, Jueqi Guan, Hao Chen, and Pasquale De Meo. 2026. PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12621–12647, San Diego, California, United States. Association for Computational Linguistics.