Turkish Adaptation of the AI Motivation Scale (AIMS): A Self Determination Theory Approach

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Gökçearslan Ş., Durak H., Yılmaz N.

International Journal of Studies in Education and Science, cilt.7, sa.2026, ss.509-518, 2026 (Hakemli Dergi)

Özet

The rapid integration of artificial intelligence (AI) into higher education has underscored the need to better understand students’ motivation to learn with AI. Motivation is a key determinant of students’ engagement and effective use of AI supported learning environments; however, there is a scarcity of validated instruments grounded in robust motivational theory and adapted to different cultural contexts. Recently, the Artificial Intelligence Motivation Scale (AIMS), based on Self Determination Theory, was developed to assess university students’ motivation to learn with AI across five dimensions: intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. The present study aims to adapt the AIMS into Turkish and to examine its validity and reliability among Turkish university students. The study sample consisted of 221 undergraduate and graduate students from 10 universities in Türkiye. Following a translation and back-translation procedure, the Turkish version of the AIMS was administered online. The factorial structure of the scale was examined using first- and second-order confirmatory factor analyses, and internal consistency was assessed using Cronbach’s alpha coefficients. The results supported the original five-factor structure and demonstrated acceptable model fit and satisfactory reliability coefficients. The second-order analysis provided partial support for a higher-order AI motivation construct. Overall, the findings indicate that the Turkish version of the AIMS is a valid and reliable instrument for assessing university students’ motivation to learn with AI in the Turkish education context.