Personality traits and intention to use AI in pre-service teachers: the mediating role of AI use motivation
CURRENT PSYCHOLOGY, cilt.45, ss.1-16, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 45
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s12144-026-09795-y
- Dergi Adı: CURRENT PSYCHOLOGY
- Derginin Tarandığı İndeksler: Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Sociology Source Ultimate (EBSCO), Social Sciences Citation Index (SSCI), IBZ Online, BIOSIS, Psycinfo
- Sayfa Sayıları: ss.1-16
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
This study examined whether the five components of AI use motivation derived from Expectancy-Value Theory (EVT), expectancy, attainment value, utility value, intrinsic value, and cost, account for the associations between Big Five personality traits and intention to use AI among pre-service teachers. Although direct associations between personality and technology-related intentions are well documented, the motivational mechanisms underlying these associations, particularly at the level of distinct EVT components, remain underexplored. Data were collected from 605 pre-service teachers at two universities in Türkiye (Mage = 23.57, SD = 5.36). Hypotheses were tested with path analysis using observed composite variables and bias-corrected bootstrapping (5,000 resamples). Openness to experience predicted all five EVT components and retained a direct effect on intention to use AI (β = 0.16, p < .001), showing the strongest total effect (β = 0.35). Attainment value (β = 0.25), intrinsic value (β = 0.22), and utility value (β = 0.14) positively predicted intention, whereas expectancy and cost were non-significant once the value components were included, despite a strong negative bivariate correlation between cost and intention (r = − .60). Indirect effects indicated a selective pattern of mediation: openness operated through attainment, intrinsic, and utility value, and conscientiousness through intrinsic and utility value, whereas no indirect effects emerged for agreeableness, extraversion, or neuroticism. Neuroticism showed a positive direct effect (β = 0.17) that emerged only after the remaining traits were controlled for, a pattern consistent with statistical suppression. Prior AI use also predicted intention (β = 0.18, p < .001), and the model explained 58% of the variance. The findings indicate that motivational mediation between personality and intention to use AI is trait-specific rather than general, underscoring the value of modeling EVT components separately.