用智能代理整合法规与科研文献，实时生成可信禁毒教育内容。
Agentic AI for Substance Use Education: Integrating Regulatory and Scientific Knowledge Sources
- 构建可自主检索法规与论文的AI系统，动态更新知识源。
- 专家评估显示回答准确率高，四项评分均超4.1分（满分5分）。
- 适合健康教育、政策制定者及需要权威信息的场景。
传统毒品教育面临可扩展性差、个性化不足和信息过时等问题。尽管人工智能有望提升教育效果，但在提供实时、权威的毒品使用教育方面仍鲜有探索。本文构建了一个基于智能体的AI网络应用，实时整合美国缉毒局文件与同行评审文献，实现透明、情境敏感的毒品教育。系统采用检索增强生成技术，结合经筛选的102篇文档与动态PubMed查询，文档经语义切分并转化为向量存储以支持高效检索。我们邀请五位领域专家提出30个专业问题，并由两名独立评估者对90次系统交互（每问题含主问及两次上下文追问）进行评分，依据事实准确性、引用质量、情境连贯性和法规适用性四个维度，采用五级李克特量表打分。平均得分在4.18至4.35之间（整体范围4.05–4.52），评价者间一致性良好（科恩κ=0.78）。结果表明，融合权威监管资料与实时科学文献的智能体架构，是实现可扩展、精准且可验证健康教育的可行方向，值得通过纵向用户研究进一步验证。
原文摘要 · Abstract (English)
The delivery of traditional substance education has remained problematic due to challenges in scalability, personalization, and the currency of information in a rapidly evolving substance use landscape. While artificial intelligence (AI) offers a promising frontier for enhancing educational delivery, its application in providing real-time, authoritative substance use education remains largely underexplored. We built an agentic-based AI web application that combined Drug Enforcement Administration records with peer-reviewed literature in real-time to provide transparent context-sensitive substance use education. The system uses retrieval-augmented generation with a carefully filtered corpus of 102 documents and dynamic PubMed queries. Document storage was semantically chunked and placed in a vector representation in order to be easily retrieved. We conducted an expert evaluation study in which a panel of five subject matter experts generated 30 domain-specific questions, and two independent raters assessed 90 system interactions (30 primary questions plus two contextual follow-ups each) using a five-point Likert scale across four criteria: factual accuracy, citation quality, contextual coherence, and regulatory appropriateness. Mean ratings ranged from 4.18 to 4.35 across the four criteria (overall category range: 4.05-4.52), with substantial inter-rater agreement (Cohen's kappa = 0.78). These findings suggest that agentic AI architectures integrating authoritative regulatory sources with real-time scientific literature represent a promising direction for scalable, accurate, and verifiable health education delivery, warranting further evaluation through longitudinal user studies.
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