AI evidence extraction

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, Angeliki Tsameti · PLoS ONE · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

6.59

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1371/journal.pone.0341134

Methodology & findings

Study design

Survey-based confirmatory factor analysis (CFA) with multi-group invariance testing and MIMIC (Multiple Indicators, Multiple Causes) modeling.

Sample

N = 904, 4 groups

Primary method

Confirmatory Factor Analysis (CFA), multi-group CFA for invariance testing (configural, metric, and scalar invariance), MIMIC model (Multiple Indicators, Multiple Causes), Cronbach's alpha and McDonald's omega for internal consistency, differential item functioning (DIF) analysis. Model fit assessed using CFI, TLI, and RMSEA.

Main result

The study found that "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." Additionally, "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."

Reports effect sizes and confidence intervals.

Research paradigm

Positivist/quantitative

Author conclusions

The authors conclude that "These results demonstrate that the AIM-N is a reliable and valid instrument for measuring the nuanced ways AI influences student motivation and needs." They further state that "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."

Risk of bias

Selection bias: Cross-sectional survey design with volunteer sample may not be representative of all higher education students; Single-item measurement for amotivation indicator may lack reliability; Potential self-selection bias in survey completion; Temporal precedence unclear in cross-sectional design; Selection bias: Sample limited to university students; generalizability to other populations unclear; Cross-sectional design: Cannot infer causality; AI usage and motivation may have bidirectional relationships; Single-item amotivation measure: Reduced reliability for this construct; Differential item functioning: Some items function differently across demographic groups (field of study); Selection bias (self-report survey data); potential social desirability bias in responses about AI use and motivation; cross-sectional design limits causal inference; convenience sampling not specified in abstract.

Limitations

The paper states that "a single-item amotivation indicator" had internal consistency issues, suggesting measurement limitations
Additionally, the authors note differential item functioning (DIF) was identified, meaning "students in competitive fields endorsed the 'pressure to use AI' item more than expected from their latent trait levels," indicating potential measurement bias across groups.

Open questions raised

The authors identify the need for future research on sustaining meaningful student engagement when AI tools are pervasive, and recommend continued investigation of theoretical implications for Self-Determination Theory in the age of AI.
Longitudinal research needed to establish temporal relationships between AI adoption and motivation changes
Investigation of interventions to sustain meaningful engagement when AI tools are pervasive
Research on how AI integration affects basic psychological needs across different educational contexts
Exploration of mechanisms underlying the relationship between AI usage and controlled motivation
Future research on sustaining meaningful student engagement when AI tools are pervasive; exploration of how Self-Determination Theory applies in AI-integrated educational contexts; further investigation of differential item functioning across demographic groups.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 57%

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