Personality assessment in career guidance and personnel selection traditionally relies on self-report questionnaires, which are susceptible to response bias, fatigue, and intentional distortion. Game-based assessment offers a promising alternative by capturing implicit behavioral signals during gameplay. This study proposes a multi-genre serious-game framework combined with machine-learning techniques to predict suitability for software development roles. Developer-relevant personality and behavioral traits were identified through a systematic literature review and an empirical study of professional software engineers. A custom mobile game was designed to elicit behaviors related to problem solving, planning, adaptability, persistence, time management, and information seeking. Fine-grained gameplay event data were collected and analyzed using a two-phase modeling strategy where suitability was predicted exclusively from gameplay-derived behavioral features. Results show that our model achieved up to 97% precision and 94% accuracy. Behavioral analysis revealed that proper candidates exhibited distinct gameplay patterns, such as more wins in puzzle-based games, more side challenges, navigating menus more frequently, and exhibiting fewer pauses, retries, and surrender actions. These findings demonstrate that implicit behavioral traces captured during gameplay is promising in predicting software-development suitability without explicit personality testing, supporting serious games as a scalable, engaging, and less biased alternative for career assessment.

翻译：职业指导与人才选拔中的人格评估传统上依赖自陈式问卷，这些问卷易受反应偏差、疲劳效应和故意扭曲的影响。基于游戏的评估通过捕捉游戏过程中的内隐行为信号，提供了一种有前景的替代方案。本研究提出一个结合机器学习技术的多类型严肃游戏框架，用于预测个体对软件开发岗位的适应性。通过系统性文献综述和对专业软件工程师的实证研究，我们识别了与开发者相关的关键人格特质和行为特征。设计了一款定制化移动游戏，旨在激发与问题解决、规划、适应性、坚持性、时间管理和信息寻求相关的行为。采用两阶段建模策略，仅基于游戏行为特征预测岗位适应性，并对细粒度游戏事件数据进行了收集与分析。结果表明，我们的模型达到了最高97%的精确率和94%的准确率。行为分析显示，适配的候选人展现出独特的游戏行为模式，例如在解谜类游戏中获胜次数更多、完成更多支线挑战、更频繁地浏览菜单，同时表现出更少的暂停、重试和放弃行为。这些发现表明，游戏过程中捕获的内隐行为痕迹无需显式人格测试即可有效预测软件开发适应性，支持了严肃游戏作为一种可扩展、具吸引力且偏差更小的职业评估替代方案。