Against the backdrop of the rapid development of artificial intelligence technology, such technologies are being embedded by criminals in the process of telecom fraud, continuously reshaping the modes and structures of fraud implementation. Due to their insufficient social experience and weak risk identification ability, adolescents have increasingly become key targets of AI-empowered telecom fraud. Adopting the method of autoethnography, this paper takes the researcher’s personal experience of encountering telecom fraud as a detailed case, and analyzes the implementation process and internal psychological mechanism of AI-enabled telecom fraud under the theoretical framework of the Elaboration Likelihood Model (ELM). The study finds that under the condition that peripheral cues such as acquaintance identity simulation, time pressure creation and emotional appeal enhancement are significantly amplified by AI technology, adolescent targets are more likely to be continuously drawn into the peripheral processing route. Although central processing may be briefly activated at the moment of suspicion, it is often constantly suppressed by information overload, situational coercion and urgent rhetoric, eventually leading to “act first, judge later” decisions driven by high-arousal emotions. In this process, AI technology improves the verisimilitude and credibility of fraudulent information, weakens adolescents’ ability to identify risks relying on traditional experience and routine verification methods, and thereby reshapes the psychological game structure in telecom fraud. Based on the above analysis, this paper further puts forward anti-fraud suggestions for adolescents from the perspectives of activating the central processing route, constructing technical countermeasures, and identifying and intervening in manipulated emotions.

Wenchu Gao. Psychological Game Mechanisms between Fraudsters and Adolescents in AI-Empowered Telecom Fraud—An Autoethnographic Study. The Frontiers of Society, Science and Technology (2026), Vol. 8, Issue 2: 74-81. https://doi.org/10.25236/FSST.2026.080210.

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