Identification and Prioritization of Teachers’ Readiness Components for Employing Artificial Intelligence in the Teaching–Learning Process
Keywords:
Artificial intelligence, teacher readiness, educational innovation, technological competence, pedagogical adaptationAbstract
Purpose: This study aimed to identify and prioritize the key components of teachers’ readiness for employing artificial intelligence (AI) in the teaching–learning process.
Methods and Materials: The research employed a mixed-methods sequential exploratory design consisting of qualitative and quantitative phases. In the qualitative phase, an extensive literature review was conducted until theoretical saturation was achieved, and data were analyzed using NVivo 14 to extract major categories, subcategories, and open codes. Seven primary readiness dimensions emerged: technological, pedagogical, cognitive, psychological, ethical, organizational, and cultural readiness. In the quantitative phase, a structured questionnaire was developed based on these themes and administered to 200 teachers from public and private schools in Tehran. The data were analyzed using SPSS version 26, applying descriptive statistics and the Friedman ranking test to determine the relative significance of each readiness dimension.
Findings: The Friedman test results indicated significant differences in the mean ranks of the readiness dimensions (p < 0.05). Technological readiness achieved the highest mean rank (M = 4.62, SD = 0.54), followed by pedagogical readiness (M = 4.45, SD = 0.61) and cognitive readiness (M = 4.27, SD = 0.57). Psychological readiness (M = 4.10, SD = 0.66), ethical readiness (M = 3.95, SD = 0.63), organizational readiness (M = 3.82, SD = 0.69), and cultural readiness (M = 3.64, SD = 0.72) ranked lower, highlighting a clear prioritization pattern. These findings suggest that teachers view technical competence and instructional adaptability as foundational elements of AI integration, while ethical, institutional, and cultural factors play supportive yet less immediate roles.
Conclusion: Strengthening professional development, ethical awareness, and institutional support is essential to ensure sustainable and effective AI adoption in education.
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