WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts

• URL: http://arxiv.org/abs/2608.16924v1
• Date: Tue, 04 Aug 2026 21:16:18 GMT
• Title: WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts
• Abstract summary: This work-in-progress (WIP) innovative practice category paper presents LLM Odyssey, an open source, browser-based serious gaming platform for teaching Large Language Model (LLM) engineering concepts.<n> Topics such as tokenization, transformer architecture, prompt engineering, retrieval augmented generation (RAG), and production deployment are underrepresented in computer science curricula.<n>Each game incorporates five pedagogical strategies drawn from the literature: immediate formative feedback, scaffolded hints grounded in the Zone of Proximal Development, progressive difficulty informed by flow theory, worked examples to manage cognitive load, and authentic scenarios drawn from production practice.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work-in-progress (WIP) innovative practice category paper presents LLM Odyssey, an open source, browser-based serious gaming platform comprising 13 interactive games for teaching Large Language Model (LLM) engineering concepts. Topics such as tokenization, transformer architecture, prompt engineering, retrieval augmented generation (RAG), and production deployment are underrepresented in computer science curricula. Existing interactive tools address individual concepts but lack pedagogical scaffolding or structured learning pathways. LLM Odyssey addresses this gap through three learning tiers aligned with Bloom's revised taxonomy: Cognitive Core (7 foundational games), Systems Forge (5 production engineering games), and Foundry Arena (capstone challenges). Each game incorporates five pedagogical strategies drawn from the literature: immediate formative feedback, scaffolded hints grounded in the Zone of Proximal Development, progressive difficulty informed by flow theory, worked examples to manage cognitive load, and authentic scenarios drawn from production practice. The platform was deployed in Winter 2026 semester at a Canadian college for an initial review. Feedback confirmed functional requirements and identified adaptive difficulty as a priority for future development. A formal mixed methods evaluation protocol (N=50) has been designed, comprising pre and post knowledge tests, validated surveys, engagement analytics, and interviews, and is documented here to enable future evaluation studies with the publicly available platform.

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