From cognitive need to problematic use: a chained mediation path moderated by academic stress and AI literacy

TL;DR

This study explores how lower cognitive need leads to problematic use of Generative AI in university students, influenced by academic stress and AI literacy.

Contribution

The study identifies a new 'cognitive relief to avoidance' mechanism in AI use and emphasizes AI literacy as a protective factor.

Findings

Lower need for cognition increases positive affect, leading to avoidance motivation and problematic AI use.

High academic stress amplifies the pathway from lower cognition to problematic use.

High AI literacy reduces dependency risks by attenuating the mediation chain.

Abstract

The rapid adoption of Generative AI (GenAI) in higher education raises concerns about psychological dependency. Grounded in the I-PACE model, this study investigates how lower need for cognition (NFC) is associated with problematic use via positive affect and Avoidance-Oriented GenAI Motivation, moderated by academic stress and AI literacy.

To test the hypothesized model, we employed a two-wave, time-lagged survey design with a sample of university students (N = 452). Data were analyzed using structural equation modeling (SEM) to assess the serial mediation effects and the moderated mediation dynamics.

Analysis confirmed a serial mediation chain: lower NFC predicted stronger positive affect, increasing avoidance motivation and subsequent problematic use. This pathway was significantly amplified by high academic stress but attenuated by high AI literacy, which neutralized dependency risks.

Findings extend the I-PACE model to instrumental technologies, identifying a specific “cognitive relief to avoidance” mechanism. The study highlights AI literacy as a vital protective resource, suggesting educators should prioritize fostering critical digital competencies over prohibitive policies to mitigate reliance risks.

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