arXiv AI July 22, 2026 By Jia-Kai Dong, Yi-Cheng Lin, Hung-yi Lee

EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration

arXiv:2607. 18529v1 Announce Type: cross Abstract: Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 11

Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents

arXiv:2608. 08852v1 Announce Type: new Abstract: AI agents can now solve problems, answer like subject experts, and generate long-form multimodal content.

By Yi-Cheng Lin, Yu-Kai Guo, Szu-Chi Chen, Bo-Han Feng, Yun-Man Hsu, Hsiang Hsieh, Yu-Jung Lin, Yue-Ling Wu, Jia-Kai Dong, An-Yu Cheng, Yu-Han Huang, Lok-Lam Ieong, Kuan-Yu Chen, Ming-Douo Tchouang, Shao-Hua Sun, Che Lin, Jian-Jiun Ding, Hung-yi Lee

llms agents diffusion multimodal benchmarks

arXiv Machine Learning

Jun 8

Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.

By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao

llms agents benchmarks safety

arXiv AI

Sep 3

PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment

PhoenixNest-Video is an evidence‑grounded multimodal agent designed for automated video interview assessment. It constructs a semantic video graph as working memory, retrieves information conditioned on rubrics across visual, audio, and textual streams, and outputs per‑criterion scores tied to the candidate’s materials. Trained with rubric‑based reinforcement learning, the system achieves 91.50% grade‑level accuracy on VInterview‑2025, outperforming larger proprietary models while providing traceable evidence for each score.

By Fan Yuxuan, Huang Miaojun, Zhang Haimei, Wu Jingshen, Liu Hao

llms agents reinforcement-learning multimodal safety

Hugging Face Trending Papers

Aug 10

RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement

AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation models~(AIVGMs) have become increasingly difficult to discern using conventional evaluation criteria, such as visual fidelity and semantic instruction following.

diffusion benchmarks

arXiv Computation and Language

Sep 15

When Consistency Does Not Mean Reliability: Evaluating Local LLM Judges Against Human Ratings

arXiv:2609.13824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to evaluate the responses of other language models. This approach, known as LLM-as-a-Judge, is faste...

By Aakash Kumar Tiwari

llms safety

arXiv Computation and Language

Sep 22

From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning

arXiv:2609.24083v1 Announce Type: new Abstract: Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than s...

By Xinchen Ma, Shuimu Wang, Gaole He, Yanbin Zhang, Chunyang Wang, Yunshi Lan, Weining Qian

multimodal