ARTIFICIAL INTELLIGENCE REVOLUTION: ENHANCING MODERN EDUCATION THROUGH ZONE OF PROXIMAL DEVELOPMENT APPROACH
ARTIFICIAL INTELLIGENCE REVOLUTION: ENHANCING MODERN EDUCATION THROUGH ZONE OF PROXIMAL DEVELOPMENT APPROACH
Dawood Al-Hamdani
TLDR
Strong positive correlations were found between teachers’ beliefs in student-centred pedagogy, formative assessment, and overall acceptance of the AI–ZPD model, providing robust evidence that pedagogical and assessment beliefs are key determinants of teachers’ readiness to adopt AI-supported ZPD-oriented instruction.
Abstract
This study examines teachers’ attitudes and readiness to implement an Artificial Intelligence–supported instructional model grounded in the Zone of Proximal Development (AI–ZPD). The proposed model integrates AI-driven personalization, real-time assessment, scaffolding, and collaborative learning to enhance constructivist teaching practices. A sample of 50 teachers completed a survey measuring six attitudinal dimensions: Technology, Pedagogical Alignment, Interaction and Collaboration, Teacher Professional Role and Autonomy, Student Impact, and Assessment Support. Descriptive results indicated moderately positive attitudes across all dimensions (M = 3.10–3.18 on a 5-point scale). Hypotheses testing showed that teachers with prior AI experience demonstrated higher descriptive acceptance of the model, although this difference was not statistically significant. Regression analysis confirmed that demographic variables, including years of experience and prior AI use, did not significantly predict acceptance of the model (F(3,46) = 0.59, p = .63, R² = .04). However, strong positive correlations were found between teachers’ beliefs in student-centred pedagogy, formative assessment, and overall acceptance of the AI–ZPD model (r = .94–.97, p < .001). These results provide robust evidence that pedagogical and assessment beliefs—rather than background factors—are key determinants of teachers’ readiness to adopt AI-supported ZPD-oriented instruction. The study contributes a theoretically grounded AI–ZPD framework and offers practical implications for professional development, policy design, and AI-enhanced teaching and learning.
