We introduce GamEDAI, a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. Built on phase-based LangGraph sub-graphs, deterministic Quality Gates, and structured Pydantic schemas, GamEDAI supports two template families encompassing 15 interaction mechanics across spatial reasoning, procedural execution, and higher-order Bloom's Taxonomy objectives. Evaluated on 200 questions spanning five subject domains, the system achieves a 90% validation pass rate, 98.3% schema compliance, and 73% token reduction over ReAct agents (${\sim}$73,500 $\rightarrow$ ${\sim}$19,900 tokens/game) at $0.46 per game. Within this model configuration, these results suggest that phase-bounded architectural structure correlates more strongly with alignment quality than prompting strategy alone. Our demonstration lets attendees generate Bloom's-aligned games from natural language in under 60 seconds, inspect Quality Gate outputs at each pipeline phase, and browse a curated library of 50 games spanning all 15 mechanic types.
Automated Domain Question Mapping (DQM) with Educational Learning Materials
Cognition and Learning Design Lab, Department of Computer Science, Kennesaw State University（认知与学习设计实验室，计算机科学系，肯纳邦大学）；Department of Computer Science, Kennesaw State University（计算机科学系，肯纳邦大学）；School of Instructional Technology and Innovation, Kennesaw State University（教学技术与创新学院，肯纳邦大学）
Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction
Slide2Text: Leveraging LLMs for Personalized Textbook Generation from PowerPoint Presentations
East China Normal University（华东师范大学）；Shanghai Institute of Artificial Intelligence for Education（上海人工智能教育研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
EduResearchBench: A Hierarchical Atomic Task Decomposition Benchmark for Full-Lifecycle Educational Research
Shanghai Institute of AI for Education, East China Normal University（上海人工智能教育研究院，东华大学）；Shanghai Innovation Institute（上海创新研究院）；School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Future Brain Education Technology Co., Ltd.（北京未来脑教育科技有限公司）；The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Guangxi Zhuang Autonomous Region Big Data Research Institute（广西壮族自治区大数据研究院）
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Rensselaer Polytechnic Institute（伦斯勒理工学院）；IBM Research（IBM研究院）；University of Washington（华盛顿大学）；University of Notre Dame（圣母大学）；WeChat AI, Tencent（腾讯微信人工智能）；University of California Irvine（加州大学欧文分校）
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Xtracta（埃克斯特拉克塔公司）；School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；School of Life and Environmental Sciences, University of Sydney（悉尼大学生命与环境科学学院）
