Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

翻译：机器人教育能够培养计算思维、创造力和问题解决能力，但由于技术复杂性，其教学实践仍面临挑战。基于游戏的学习和游戏化方法虽能提升学习参与度，但两者的比较性影响尚不明确。本研究首次采用PRISMA框架，对机器人教育中基于游戏的学习和游戏化方法进行了系统综述与比较性综合分析，从四个数据库中筛选了2014年至2025年间的12,485条记录，最终纳入95项研究进行分析。我们对每项研究的方法、学习情境、技能水平、教学形式、教学策略及学习成果进行了编码（编码者间信度 k = .918）。分析揭示了三种主要模式：(1) 方法-情境-教学策略的耦合关系（基于游戏的学习更多见于非正式教育环境，而游戏化方法在正式课堂中占主导地位 [p < .001]，且更倾向于采用项目式学习 [p = .009]）；(2) 教学重点集中于入门编程与模块化套件，高级软件（约17%）、高级硬件（约5%）或沉浸式技术（约22%）的应用有限；(3) 研究周期普遍较短，且多依赖自我报告数据。基于此，我们提出了八个未来研究方向，并构建了一个设计空间，用以阐明最佳实践与常见误区，从而为机器人教育提供可操作的指导。