mlsanityData to 7 Oct 2026

GamED.AI: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation

arXiv 2604.23947 ↗First posted 27 Apr 2026cs.AIcited by 0

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Abstract (arXiv)

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 rightarrow ~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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 40 references that are in the map. First posted on arXiv 27 Apr 2026.

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6 of 40 references.

References · 6

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  5. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  6. 2023GameGPT: Multi-agent Collaborative Framework for Game Developmentcited by 41

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