The The Dual Impact of Artificial Intelligence: Stressor or Support?

Stressor or Support?

Abstract

Academic institutions are rapidly integrating AI tools into curricula without really understanding how different student populations can experience varying levels of intellectual insecurity based on their usage patterns, stress levels, and developmental stages. This study investigates the psychological factors driving AI-related intellectual insecurity among university students through an ordered logistic regression analysis of 1,085 respondents. We examine how AI usage patterns, academic stress, confusion, peer pressure, mental health dependencies, and age interact to shape students' intellectual self-doubt in the context of artificial intelligence adoption. The findings further reveal that AI-related insecurity is not merely a technology competence issue but rather emerges from complex interactions between usage patterns and psychological contexts. We find that peer pressure as the most robust predictor of insecurity that emerges from AI usage patterns, indicating that social comparison and competitive academic environments create anxiety regardless of actual AI adoption decisions. Most significantly, the interaction between AI use and academic stress reveals that high AI usage only generates insecurity under conditions of elevated stress. Under low stress, the same usage patterns enable adaptive integration, whilst high stress triggers maladaptive dependency and desperate over-reliance. The age-use interaction demonstrates generational differences, with younger students experiencing increased insecurity from high AI use due to identity formation vulnerabilities, whilst older students with established credentials can adopt AI confidently. These findings validate a quadrant model where students' positions along stress and usage dimensions determine distinct psychological profiles ranging from comfortable traditionalists to over-reliant strivers experiencing existential dependency.