The integration of Large Language Models (LLMs) into cybersecurity education for criminal justice professionals is currently hindered by the "statelessness" of reactive chatbots and the risk of hallucinations in high-stakes legal contexts. To address these limitations, we propose the CyberJustice Tutor, an educational dialogue system powered by an Agentic AI framework. Unlike reactive chatbots, our system employs a "Think-Plan-Act" cognitive cycle, enabling autonomous goal decomposition, longitudinal planning, and dynamic context maintenance. We integrate a Pedagogical Scaffolding Layer grounded in Vygotsky's Zone of Proximal Development (ZPD), which dynamically adapts instructional support based on the learner's real-time progress. Furthermore, an Adaptive Retrieval Augmented Generation (RAG) core anchors the agent's reasoning in verified curriculum materials to ensure legal and technical accuracy. A comprehensive user study with 123 participants, including students, educators, and active law enforcement officers, validated the system's efficacy. Quantitative results demonstrate high user acceptance for Response Speed (4.7/5), Ease of Use (4.4/5), and Accuracy (4.3/5). Qualitative feedback indicates that the agentic architecture is perceived as highly effective in guiding learners through personalized paths, demonstrating the feasibility and usability of agentic AI for specialized professional education.

翻译：当前，面向刑事司法专业人员的网络安全教育中，大语言模型的整合受到反应式聊天机器人的“无状态性”以及高风险法律语境中幻觉风险的阻碍。为应对这些局限，我们提出网络正义导师——一个由智能体AI框架驱动的教育对话系统。与反应式聊天机器人不同，本系统采用“思考-规划-执行”认知循环，实现自主目标分解、纵向规划与动态语境维护。我们整合了基于维果茨基最近发展区理论的教学支架层，可依据学习者实时进展动态调整教学支持。此外，核心的自适应检索增强生成模块将智能体的推理锚定于经过验证的课程材料，以确保法律与技术的准确性。一项包含123名参与者（含学生、教育工作者及在职执法人员）的综合性用户研究验证了系统效能。量化结果表明用户在响应速度（4.7/5分）、易用性（4.4/5分）与准确性（4.3/5分）方面具有高接受度。质性反馈显示，智能体架构在引导学习者实现个性化路径方面被感知为高度有效，证实了智能体AI在专业职业教育领域的可行性与可用性。