This study examined whether counterarguments generated by large language models (LLMs) influence the moral judgments of younger and older adults and whether these effects vary as a function of dilemma type, cognitive functioning, trust in AI, and prior experience using LLMs. Using the switch and footbridge trolley dilemmas, 130 participants (56 younger adults and 74 older adults) were presented with ChatGPT arguments that opposed their initial judgments. Results revealed that more than 30% of participants reversed their moral judgments in both dilemmas (32.31% in the switch dilemma and 36.92% in the footbridge dilemma), suggesting that LLMs possess substantial persuasive power. Older adults tended to be more likely than younger adults to reverse their judgments, and they showed a significantly greater degree of judgment change in the switch dilemma. Notably, in the emotionally aversive footbridge dilemma, older adults with lower cognitive functioning were significantly more likely to align with the LLM-generated counterargument. General trust in AI and prior experience with LLMs did not predict judgment reversal, supporting a disconnect between trust and persuasion. Instead, individual factors such as lower initial confidence and higher perceived task difficulty were associated with greater susceptibility to AI influence. These findings suggest that, although LLMs may serve as tools for cognitive offloading that compensate for age-related cognitive decline, they may also pose a risk of undue persuasion for cognitively vulnerable individuals.

翻译：本研究探讨了大型语言模型（LLMs）生成的反驳论点是否影响年轻人和老年人的道德判断，以及这种影响是否因困境类型、认知功能、对AI的信任以及使用LLMs的先前经验而有所不同。通过使用开关和天桥电车困境，130名参与者（56名年轻人和74名老年人）被呈现了针对其初始判断的ChatGPT论点。结果显示，在两种困境中，超过30%的参与者改变了道德判断（开关困境中为32.31%，天桥困境中为36.92%），表明LLMs具有强大的说服力。老年人比年轻人更有可能改变判断，且在开关困境中表现出显著更大的判断变化程度。值得注意的是，在情感厌恶的天桥困境中，认知功能较低的老年人显著更倾向于认同LLM生成的反驳论点。对AI的普遍信任和与LLMs的先前经验并未预测判断的反转，支持了信任与说服之间的脱节。相反，较低的初始信心和较高的感知任务难度等个体因素与更大的AI影响易感性相关。这些发现表明，尽管LLMs可能作为认知卸载工具补偿与年龄相关的认知衰退，但它们也可能对认知脆弱的个体构成不当说服的风险。