Examining the Relationship Between Teachers’ Artificial Intelligence Awareness Levels and Decision-Making Styles
DOI:
https://doi.org/10.70232/jrep.v3i4.312Keywords:
AI Awareness, Artificial Intelligence, Decision-Making Style, TeacherAbstract
As digital transformation accelerates in education, integrating innovative technologies into pedagogical processes requires teachers to possess both high technological awareness and effective professional decision-making capabilities. The primary objective of this study is to examine the relationship between the artificial intelligence (AI) awareness levels and decision-making styles of teachers working in Samsun, Turkey, and to determine whether these variables differ significantly across various demographic characteristics. Designed as a correlational survey within a quantitative research framework, the study group comprised 156 teachers (104 females, 50 males) employed across different school levels during the 2026–2027 academic year, selected via convenience sampling. Data were gathered face-to-face using a Personal Information Form, the 51-item Artificial Intelligence Awareness Scale, and the 39-item Decision-Making Scale. Data analysis involved descriptive statistics, independent samples t-tests, one-way ANOVA, and Pearson correlation analysis. The findings revealed a positive but negligible (very weak) relationship between teachers’ overall AI awareness levels and their decision-making skills (r = .163, p < .05). While general AI awareness and decision-making skills did not differ significantly based on gender, age, professional seniority, or school level, notable differences emerged in specific sub-dimensions. Teachers with postgraduate education scored significantly higher in the Cognitive Factors and Information and Goal sub-dimensions. Furthermore, teachers in foreign language and verbal branches exhibited greater competency in target setting, and those in public and private schools demonstrated superior decision-making skills compared to instructors in short-term course centers. These results indicate that high theoretical AI awareness has not yet fully translated into pedagogical decision-support practices in classrooms. To bridge this gap, professional development programs should move beyond theoretical instruction to provide applied workshops that actively integrate AI tools into data-driven teaching and classroom management decisions.
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