Assessment and Validation of the Scale of Professional Readiness of Future Teachers for the Use of Neural Network Technologies in the Educational Process

Berdimuratova Zhanar, Anara Karymsakova, Zhandos Zulpykhar, Lili Nurliyana Abdullah, Meruert Serik

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.18178/ijiet.2026.16.10.2727
Data confidence 70 medium
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Abstract

The rapid integration of neural network technologies into education has increased the need to assess future teachers’ professional readiness for the pedagogically appropriate, ethically responsible, and methodologically effective use of artificial intelligence tools in teaching practice. However, there remains a lack of validated instruments specifically designed to measure this form of professional readiness in teacher education. The aim of this study was to develop and validate a scale for assessing the professional readiness of future teachers to use neural network technologies in the educational process. The study involved 150 students enrolled in teaching programs at L.N. Gumilyov Eurasian National University. The developed instrument included 36 items organized into three theoretically derived content domains: cognitive, operational, and reflexive-motivational readiness. In the present study, the scale was validated as an overall measure of professional readiness using the Rasch model, while the internal multidimensional structure requires further verification through Exploratory Factor Analysis (EFA) or Confirmatory Factor Analysis (CFA) in future research. Data was analyzed using the Rasch measurement model to evaluate one-dimensionality, category functioning, item fit statistics, reliability and separation indices, Wright map targeting, and Differential Item Functioning (DIF). The results demonstrated acceptable psychometric properties of the scale. The main measured dimension explained 48.7% of the total variance, while all response categories functioned in an ordered manner. Most items showed acceptable Infit and Outfit statistics, with only two items demonstrating borderline fit values. Reliability indices were high for both items (0.95) and respondents (0.91), indicating stable measurement performance. DIF (Differential Item Functioning) analysis revealed no substantial gender bias across items. The findings suggest that the proposed scale is a reliable and valid instrument for assessing future teachers’ professional readiness to use neural network technologies in educational settings. The scale can be applied in teacher education research, curriculum evaluation, and the development of AI (Artificial Intelligence)-related pedagogical competencies in higher education.