用免疫理论设计抗AI操控的教育框架，让学习者提前体验AI陷阱。
From Diagnosis to Inoculation: Building Cognitive Resistance to AI Disempowerment
- 基于免疫理论，通过模拟AI错误模式训练认知抵抗力。
- 在线课程中引入AI作为协教员，实践八项核心学习目标。
- 框架与实证诊断结果高度吻合，适合教育研究者和AI从业者。
Sharma等（2026）的实证研究揭示了人工智能助手交互可能引发情境性人类失能，包括现实扭曲、价值判断偏差和行为扭曲。尽管该研究提供了关键问题诊断，但具体的教学干预仍缺乏探索。本文提出一个围绕八个跨领域学习成果（LOs）构建的AI素养框架，该框架在教学实践中独立发展，并随后发现与Sharma等人的失能分类体系高度一致。报告了一个公开在线课程的案例研究，采用师生协同教学法，由AI担任主动角色的协教员来实施该框架。借鉴接种理论（McGuire, 1961），该理论已被剑桥学派应用于虚假信息预阻断（van der Linden, 2022；Roozenbeek & van der Linden, 2019），论证了单纯知识传授无法建立AI素养，必须通过有指导地暴露于AI的失效模式——如阿谀奉承式验证和权威投射行为——来实现。这一将接种理论应用于特定AI扭曲现象的做法，在作者所知范围内尚属首次。讨论了教学衍生框架与实证驱动分类之间的收敛性，认为两种独立路径得出相似问题描述，增强了诊断与教育应对方案的可信度。
原文摘要 · Abstract (English)
Recent empirical research by Sharma et al. (2026) demonstrated that AI assistant interactions carry meaningful potential for situational human disempowerment, including reality distortion, value judgment distortion, and action distortion. While this work provides a critical diagnosis of the problem, concrete pedagogical interventions remain underexplored. I present an AI literacy framework built around eight cross-cutting Learning Outcomes (LOs), developed independently through teaching practice and subsequently found to align with Sharma et al.'s disempowerment taxonomy. I report a case study from a publicly available online course, where a co-teaching methodology--with AI serving as an active voice co-instructor--was used to deliver this framework. Drawing on inoculation theory (McGuire, 1961)--a well-established persuasion research framework recently applied to misinformation prebunking by the Cambridge school (van der Linden, 2022; Roozenbeek & van der Linden, 2019)--I argue that AI literacy cannot be acquired through declarative knowledge alone, but requires guided exposure to AI failure modes, including the sycophantic validation and authority projection patterns identified by Sharma et al. This application of inoculation theory to AI-specific distortion is, to my knowledge, novel. I discuss the convergence between the pedagogically-derived framework and Sharma et al.'s empirically-derived taxonomy, and argue that this convergence--two independent approaches arriving at similar problem descriptions--strengthens the case for both the diagnosis and the proposed educational response.
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