@inproceedings{agarwal-etal-2026-gamed,
    title = "{GAMED}.{AI}: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation",
    author = "Agarwal, Shiven  and
      Shah, Yash  and
      Shekhar, Ashish Raj  and
      Bordoloi, Priyanuj  and
      Gupta, Vivek",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.84/",
    pages = "851--860",
    ISBN = "979-8-89176-392-0",
    abstract = "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 $73,500 → $19,900 tokens/game) at $0.46 per game. Within this model configuration, these results suggest that phase-bounded architectural structure correlates more strongly with alignment quality than prompting strategy alone.Our demonstration lets attendees generate Bloom's-aligned games from natural language in under 60 seconds, inspect Quality Gate outputs at each pipeline phase, and browse a curated library of 50 games spanning all 15 mechanic types.$"
}