用13个游戏教大模型工程，从基础到实战全流程体验
WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts
- 分三阶游戏体系，对应认知、系统、实战层级
- 每关融合反馈、提示、难度递进等教学策略
- 适合高校课程设计者与自学大模型工程的学生
本文介绍 LLM Odyssey，一个开源的基于浏览器的严肃游戏平台，包含13个交互式游戏，用于教授大语言模型（LLM）工程概念。当前计算机科学课程中，分词、Transformer架构、提示工程、检索增强生成（RAG）及生产部署等内容仍缺乏系统教学。现有工具多聚焦单一概念，缺乏教学支撑与学习路径设计。LLM Odyssey 通过三个学习层级弥补此缺口：认知核心（7个基础游戏）、系统锻造（5个工程实践游戏）、铸坊竞技场（综合挑战）。每个游戏融合五种教学策略：即时形成性反馈、基于最近发展区的分步提示、基于心流理论的渐进难度、降低认知负荷的工作示例，以及来自真实生产场景的仿真任务。该平台于2026年冬季在加拿大一所学院试用，反馈确认功能完整性，并指出自适应难度为未来重点。已设计正式混合方法评估方案（N=50），包括前后测知识测试、有效问卷、行为数据分析与访谈，供后续研究复现。
原文摘要 · Abstract (English)
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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