Validating the AIM–N: An AI-motivation and needs scale with multi-group invariance and MIMIC-DIF evidence in higher education

Laura Maska, Patra Vlachopanou, Dimitrios Kalamaras and Angeliki Tsameti

PLOS ONE, 2026, vol. 21, issue 3, 1-31

Abstract: The rapid adoption of generative AI in higher education raises critical questions about its impact on student motivation and basic psychological needs. This study introduces and validates the AI-Motivation and Needs (AIM-N) scale, a new instrument assessing how AI integration influences students’ motivational orientations and need satisfaction in learning. Survey data were collected from N = 904 university students. A confirmatory factor analysis (CFA) supported a multi-factor structure for the AIM-N, comprising two subscales of AI-related redundancy beliefs (task-level and motivational-level) and three subscales of AI-related motivational orientations (intrinsic, identified, controlled), with acceptable model fit (CFI ≈ 0.96, TLI ≈ 0.95, RMSEA ≈ 0.05) and strong factor loadings. Internal consistency was good for most subscales (Cronbach’s α = 0.70–0.90; McDonald’s ω in similar range), except a single-item amotivation indicator. Multi-group CFA indicated that the AIM-N achieved configural, metric, and scalar invariance across gender, study level (Bachelor’s, Master’s, PhD), academic field, and frequency of AI use (ΔCFI

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0341134

DOI: 10.1371/journal.pone.0341134

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