From Diagnosis to Inoculation: Building Cognitive Resistance to AI Disempowerment
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911451862532096 |
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| author | Komissarov, Aleksey |
| author_facet | Komissarov, Aleksey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15265 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Diagnosis to Inoculation: Building Cognitive Resistance to AI Disempowerment Komissarov, Aleksey Human-Computer Interaction Artificial Intelligence Computers and Society H.1.2; H.5.2; K.3.1; K.4.2 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. |
| title | From Diagnosis to Inoculation: Building Cognitive Resistance to AI Disempowerment |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society H.1.2; H.5.2; K.3.1; K.4.2 |
| url | https://arxiv.org/abs/2602.15265 |