@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://aclanthology.org/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 $0.46 per game. Within this model configuration, these results suggest that phase-bounded architectural structure correlates more strongly with alignment quality than prompting strategy alone.Our demonstration lets attendees generate Bloom's-aligned games from natural language in under 60 seconds, inspect Quality Gate outputs at each pipeline phase, and browse a curated library of 50 games spanning all 15 mechanic types.$"
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<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.</abstract>
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%0 Conference Proceedings
%T GAMED.AI: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation
%A Agarwal, Shiven
%A Shah, Yash
%A Shekhar, Ashish Raj
%A Bordoloi, Priyanuj
%A Gupta, Vivek
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F agarwal-etal-2026-gamed
%X 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.
%U https://aclanthology.org/2026.acl-demo.84/
%P 851-860
Markdown (Informal)
[GAMED.AI: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation](https://aclanthology.org/2026.acl-demo.84/) (Agarwal et al., ACL 2026)
ACL