Standardized regression weights.

As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from 335 participants to examine how demographic factors, age, gender, familiarity with AI, and frequency of technology use influence trust across a range of cognitive tasks. The findings reveal statistically significant relationships that vary by task type, with distinct patterns emerging in memory recall, complex problem-solving, and medical decision-making. Familiarity with AI and frequent use of technology are strong predictors of trust, suggesting that exposure and experience enhance confidence in AI capabilities. Conversely, age contributes significantly to disparities in responses, especially in high-stakes domains like healthcare, where older participants exhibit greater skepticism. Gender-based differences are also observed, though less pronounced. These results underscore the importance of AI systems that are technically sound and sensitive to user diversity, advocating for personalized and context-aware trust-building strategies to support the ethical and effective integration of AI into human decision-making processes. The SEM model explained 43% of the variance in trust toward AI. Based on the findings, we recommend designing adaptive, user-centered AI systems and enhancing public education to reduce skepticism and increase familiarity.