发表机构
Arizona State University; Amazon AGI（亚利桑那州立大学; 亚马逊AGI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出CyberAGENTS框架，通过结构化自主机制实现游戏化网络安全学习，经课堂评估验证其可提升学习者参与度与对AI响应的信任度，为相关系统设计提供蓝图。
AI 中文摘要
游戏化学习在需要主动解决问题和迭代技能培养的学习领域（如网络安全教育）中尤为有效。生成式AI智能体为大规模自适应提供此类体验开辟了路径，但也带来了教育场景中已被充分记录的风险：行为不一致、推理内容生成错误（幻觉）以及与教学框架不匹配。因此，将这些系统建立在学习科学基础上至关重要。我们提出CyberAGENTS，这是一个用于游戏化网络安全学习的智能体框架，通过本体论引导的验证、模式约束的行为控制以及基于能力的进阶实现结构化自主。该系统围绕基于能力的进阶模型构建，该模型按难度和前置关系组织主题，反映了循证的支架式教学原则。学习循环被分解为四个专门的智能体：挑战智能体、支持智能体、评估智能体和奖励智能体，每个智能体都由编码操作模式和进阶逻辑的行为模式约束，在不消除生成灵活性的前提下限制智能体的自主性。网络安全本体论在所有生成内容展示前对其进行验证，确保领域一致的推理和安全约束。我们通过面向本科生的课堂部署，结合教育工作者和领域专家的评估来评估CyberAGENTS。结果表明，当行为模式和本体论验证处于激活状态时，学习者的参与度提升、对反馈的理解更清晰，且对AI生成响应的信任度更高。与无约束配置的初步比较进一步支持了结构化控制在稳定教学行为中的作用。这些发现为设计基于教学原理的智能体游戏化学习系统提供了蓝图。
英文摘要
Gamification is especially effective in learning domains requiring active problem-solving and iterative skill-building, such as cybersecurity education. Generative AI agents offer a path to delivering such experiences adaptively at scale, but introduce well-documented risks in educational settings: inconsistent behavior, hallucinated reasoning, and misalignment with pedagogical frameworks. Grounding these systems in learning science is therefore essential. We present \model, an agentic framework for gamified cybersecurity learning that enables structured autonomy through ontology-guided validation, schema-governed behavioral control, and competency-based progression. The system is organized around a competency-based progression model that structures topics by difficulty and prerequisite relationships, reflecting evidence-based principles of scaffolded instruction. The learning loop is decomposed into four specialized agents: challenge, support, evaluation, and reward, each governed by behavioral schemas that encode operational modes and progression logic, bounding agent autonomy without eliminating generative flexibility. A cybersecurity ontology validates all generated content prior to display, enforcing domain-consistent reasoning and safety constraints. We evaluate CyberAgents through classroom deployment with undergraduate students, complemented by expert evaluations from educators and domain specialists. Results indicate improved engagement, clearer feedback interpretation, and greater learner trust in AI-generated responses when behavioral schemas and ontology validation are active. Preliminary comparisons with an unconstrained configuration further support the role of structured control in stabilizing instructional behavior. These findings offer a blueprint for designing pedagogically grounded agentic gamified learning systems.
