Generative AI Compensates for Age-Related Cognitive Decline in Decision Making: Preference-Aligned Recommendations Reduce Choice Difficulty

2025-11-26

AI Summary

Cognitive aging—particularly declines in working memory and processing speed—impairs older adults’ ability to make complex decisions, leading to increased choice difficulty and reduced satisfaction. To address this, we conducted a cross-age controlled experiment, the first to empirically test whether generative AI (GPT-4o), calibrated via preference-aligned personalized recommendations, can compensate for age-related cognitive limitations. In a music selection task, AI assistance significantly reduced perceived choice difficulty across all age groups. Crucially, it eliminated the satisfaction gap between older adults with low versus high cognitive functioning (assessed via WAIS-IV), thereby attenuating the association between cognitive capacity and decisional burden. Results demonstrate that generative AI not only alleviates information search load but also enhances decision equity and subjective experience quality. This study provides empirical grounding and a novel paradigm for AI-augmented decision support in aging populations.

Technology Category

• Humans and AI: AI for Accessibility
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Learning Preferences or Rankings

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Due to age-related declines in memory, processing speed, working memory, and executive functions, older adults experience difficulties in decision making when situations require novel choices, probabilistic judgments, rapid responses, or extensive information search. This study examined whether using generative AI during decision making enhances choice satisfaction and reduces choice difficulty among older adults. A total of 130 participants (younger: 56; older: 74) completed a music-selection task under AI-use and AI-nonuse conditions across two contexts: previously experienced (road trip) and not previously experienced (space travel). In the AI-nonuse condition, participants generated candidate options from memory; in the AI-use condition, GPT-4o presented options tailored to individual preferences. To assess cognitive function, we also administered the Wechsler Adult Intelligence Scale-Fourth Edition. Results revealed that in the AI-nonuse condition, older adults with lower cognitive function reported higher choice difficulty and lower choice satisfaction. Under the AI-use condition, choice satisfaction did not change significantly, but perceived choice difficulty decreased significantly in both age groups. Moreover, AI use attenuated the associations observed among older adults between lower cognitive function and both greater difficulty and lower satisfaction. These findings indicate that preference-aligned option recommendations generated by AI can compensate for age-related constraints on information search, thereby reducing perceived choice difficulty without diminishing satisfaction.

Problem

• AI reduces decision difficulty for older adults with cognitive decline
• Generative AI compensates for age-related memory and executive function limitations
• Preference-aligned recommendations help overcome information search constraints

Innovation

• Generative AI provides preference-aligned recommendations
• AI compensates age-related cognitive decline in decisions
• AI reduces choice difficulty without lowering satisfaction