Large Language Model Counterarguments in Older Adults: Cognitive Offloading or Vulnerability to Moral Persuasion?

2026-04-24

AI Summary

This study investigates the influence of personalized counterarguments generated by large language models (specifically ChatGPT) on moral judgments across different age groups. By integrating AI-generated rebuttals into the classic trolley dilemma paradigm and combining cognitive assessments with psychometric measures, the research finds that over 30% of participants revised their initial moral judgments. Older adults were significantly more persuadable, particularly when exhibiting lower cognitive functioning; additionally, individuals with lower initial judgment confidence or higher perceived task difficulty were more susceptible to influence. The work provides the first systematic evidence of heightened sensitivity among older adults to AI-mediated moral persuasion and reveals a notable dissociation between participants’ stated trust in AI and their actual susceptibility to being persuaded by it.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• large language models
• moral judgment
• older adults
• cognitive vulnerability
• AI persuasion

Innovation

• large language models
• moral judgment
• cognitive offloading
• age differences
• AI persuasion