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

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 < 0.01), after minor modifications for the AI-use groups. A MIMIC model (Multiple Indicators, Multiple Causes) revealed that higher AI tool usage was associated with stronger beliefs that AI renders learning tasks redundant and slightly more controlled motivation (β ≈ 0.30 and 0.21, p <.001), while gender showed no significant effects. Field of study had significant impacts: STEM students reported higher redundancy beliefs and controlled motivation than humanities students (p <.01). The MIMIC analysis also identified differential item functioning (DIF) for certain items; for example, students in competitive fields endorsed the "pressure to use AI" item more than expected from their latent trait levels. These results demonstrate that the AIM-N is a reliable and valid instrument for measuring the nuanced ways AI influences student motivation and needs. The discussion addresses theoretical implications for Self-Determination Theory in the age of AI, practical implications for educators, and recommendations for future research on sustaining meaningful student engagement when AI tools are pervasive.