← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-demo.77 · 记录 #1
SlideGuard: AI-Driven Evaluation of Graduate Student Presentation Materials
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
摘要
Preparing graduate students for effective professional communication remains a central goal of higher education, yet consistently assessing the quality of presentation slide decks - particularly in fast-growing AI/ML programs - poses significant challenges.We introduce SlideGuard, an evaluation agent that assesses slide decks against a comprehensive framework of expert-defined criteria using a visual language model.The criteria, developed in collaboration with domain experts, span visual design, narrative coherence, and argumentative structure.SlideGuard delivers explicit, interpretable justifications for its scoring decisions, and its content-hash-based caching enables efficient re-evaluation after incremental edits, reducing the time educators spend on slide deck evaluation and accelerating feedback delivery to students.We evaluate the approach on a dataset of 150 annotated slide decks and show that it detects the majority of expert-identified issues, with stronger results on structural and visual criteria and known limitations on subjective dimensions such as research quality.SlideGuard is released under the Apache 2.0 license and is available on GitHub,[https://github.com/Industrial-AI-Research-Lab/SlideGuard] including all criterion prompts, configuration files, and evaluation scripts to facilitate replication.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
SlideGuard: AI-Driven Evaluation of Graduate Student Presentation Materials
Preparing graduate students for effective professional communication remains a central goal of higher education, yet consistently assessing the quality of presentation slide decks - particularly in fast-growing AI/ML programs - poses significant challenges.We introduce SlideGuard, an evaluation agent that assesses slide decks against a comprehensive framework of expert-defined criteria using a visual language model.The criteria, developed in collaboration with domain experts, span visual design, narrative coherence, and argumentative structure.SlideGuard delivers explicit, interpretable justifications for its scoring decisions, and its content-hash-based caching enables efficient re-evaluation after incremental edits, reducing the time educators spend on slide deck evaluation and accelerating feedback delivery to students.We evaluate the approach on a dataset of 150 annotated slide decks and show that it detects the majority of expert-identified issues, with stronger results on structural and visual criteria and known limitations on subjective dimensions such as research quality.SlideGuard is released under the Apache 2.0 license and is available on GitHub,[https://github.com/Industrial-AI-Research-Lab/SlideGuard] including all criterion prompts, configuration files, and evaluation scripts to facilitate replication.
查看此版本解读 →查看此版本原文 ↗