发表机构
Tufts Institute for Artificial Intelligence; Tufts University（塔夫茨人工智能研究所; 塔夫茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究介绍开源游戏平台LLM奥德赛，含13个交互式游戏，覆盖LLM工程多主题，按布鲁姆分类设三个学习层级，已在加拿大学院初步部署，将开展50人混合方法评估。
AI 中文摘要
本工作进展（WIP）创新实践类论文介绍了LLM奥德赛，这是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，用于教授大型语言模型（LLM）工程概念。在计算机科学课程中，分词、Transformer架构、提示工程、检索增强生成（RAG）和生产部署等主题的教学内容不足。现有的交互式工具仅能覆盖单个概念，但缺乏教学支架或结构化学习路径。LLM奥德赛通过与布鲁姆修订分类法对齐的三个学习层级解决了这一缺口：认知核心（7个基础游戏）、系统锻造（5个生产工程游戏）和锻造竞技场（顶峰挑战）。每个游戏都结合了文献中的五种教学策略：即时形成性反馈、基于最近发展区的支架式提示、基于心流理论的渐进难度、用于控制认知负荷的示例以及来自生产实践的真实场景。该平台于2026年冬季学期在加拿大的一所学院部署，进行初步评估。反馈确认了功能需求，并指出自适应难度是未来开发的优先事项。一项正式的混合方法评估方案（N=50）已设计完成，包含前测和后测知识测试、经过验证的调查、参与度分析以及访谈，本文档将其记录下来，以便未来使用该公开平台开展评估研究。
英文摘要
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.
Comments5 pages, 4 figures. Accepted at the 2026 IEEE Frontiers in Education Conference (FIE 2026), Work in Progress track
