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Thoughtless Use of Generative Artificial Intelligence and College Students' Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences

● high · 2026-08-20 · generative-ai, self-directed-learning, cognitive-offloading, self-efficacy, motivation, self-regulated-learning, higher-ed, gender

Synthesis: Zhao & Gu (2026), grounded in Social Cognitive Theory, survey 487 undergraduates in Henan Province, China, and use multi-group SEM to show that the thoughtless use of generative AI (TUGA) — adopting AI answers without critically evaluating or understanding them — significantly undermines self-directed learning (SDL). This negative effect operates both directly and through the partial mediation of self-efficacy (SE) and Motivation. Multi-group analysis reveals substantial gender differences: the negative impact of thoughtless use on motivation was stronger for male students, while effects on self-efficacy were stronger for female students.

Key Findings

  • TUGA directly harms self-directed learning. Thoughtless use of GenAI had a significant direct negative effect on undergraduates' SDL (β = −0.42, t = 6.76, p < .001). The model — TUGA, self-efficacy, and motivation — explained 75.3% of the variance in SDL.
  • Strongest predictor was motivation. Among direct predictors of SDL, Motivation had the strongest positive effect (β = 0.68, t = 11.63, p < .001), followed by the negative effect of TUGA and the positive effect of Self Efficacy (β = 0.23, t = 3.93, p < .001).
  • TUGA erodes both self-efficacy and motivation. Thoughtless use significantly and negatively predicted motivation (β = −0.54, t = 8.53, p < .001) and self-efficacy (β = −0.37, t = 4.75, p < .001), while self-efficacy positively predicted motivation (β = 0.78, t = 23.56, p < .001).
  • Partial mediation through SE and motivation. Indirect effects through motivation (β = −0.37, p < .001), self-efficacy (β = −0.09, p = .012), and the sequential self-efficacy→motivation path (β = −0.20, p = .003) were all significant, alongside the significant direct effect — indicating partial mediation.
  • Gender differences. The negative impact of thoughtless use on learning motivation was significantly stronger for male students, while its effects on self-efficacy were significantly stronger for female students (partial measurement invariance: significant variance differences for motivation and SDL, not for SE and TUGA).

Study Design & Method

A quantitative study grounded in Social Cognitive Theory. 487 undergraduates from Henan Province, China, were surveyed via snowball sampling. Structural equation modeling (PLS-SEM with SmartPLS) tested the relationships among thoughtless GenAI use (TUGA), self-efficacy (SE), motivation, and self-directed learning (SDL), including mediation (bootstrapping) and multi-group analysis (PLS-MGA with the Welch–Satterthwaite method) to examine gender differences. Measurement invariance was assessed via MICOM. Model fit was acceptable (SRMR = 0.063).

Implications for AI in Education

The study links the risk of unreflective AI reliance to the erosion of students' capacity for self-directed learning, Self Efficacy, and Motivation — and shows these harms are not gender-neutral. For institutions, it reinforces the value of promoting responsible AI use that preserves critical evaluation and independent learning rather than simply banning or uncritically encouraging GenAI. Because motivation is the strongest lever on SDL, and thoughtless use suppresses it, interventions that rebuild learner agency and self-efficacy may buffer the negative effects of AI overreliance — a concern shared with the wiki's misuse-and-harm and over-reliance literature.

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Citation

Zhao, H., & Gu, H. (2026). Thoughtless use of generative artificial intelligence and college students' self-directed learning: a multi-group SEM analysis of gender differences. Scientific Reports, 16, 24567.