Predictors of Artificial Intelligence Supported Inclusive Teaching Preparedness among Bachelor of Education Lecturers in Selected Ugandan Universities

Abstract This study examined the predictors of Artificial intelligence (AI) supported inclusive teaching preparedness among lecturers teaching Bachelor of Education (B.Ed.) programmes in selected Ugandan universities. Specifically, it investigated the influence of institutional support, curriculum integration, continuous professional development, and self-efficacy on lecturers' readiness to integrate artificial intelligence into inclusive teaching. A cross-sectional survey design was employed involving 210 lecturers from selected public and private universities in Uganda. Data were collected using a structured questionnaire and analysed using descriptive statistics, Pearson's correlation, and multiple linear regression. The findings revealed a relatively low level of preparedness for AI-supported inclusive teaching (M = 2.47, SD = 0.75). Institutional support (r = .624, p < .001), curriculum integration (r = .587, p < .001), continuous professional development (r = .641, p < .001), and self-efficacy (r = .723, p < .001) were all positively associated with preparedness. Collectively, these variables explained 62.2% of the variance in preparedness (R² = .622, Adjusted R² = .615, F (4,205) = 64.18, p < .001), with self-efficacy emerging as the strongest predictor (β = .381, p < .001), followed by institutional support (β = .269, p < .001), continuous professional development (β = .214, p = .002), and curriculum integration (β = .163, p = .010). These findings highlight the importance of strengthening institutional support, embedding AI and inclusive pedagogy within teacher education curricula, expanding AI-focused professional development, and enhancing lecturers' confidence in applying AI technologies. The study contributes empirical evidence from the Ugandan higher education context and provides practical insights for universities and policymakers seeking to promote responsible, inclusive, and sustainable AI integration in teacher education.

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-18
DOI
https://doi.org/10.5281/zenodo.21990128
Primary Topic
Technology-Enhanced Education Studies
Type
article
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article

Predictors of Artificial Intelligence Supported Inclusive Teaching Preparedness among Bachelor of Education Lecturers in Selected Ugandan Universities

Specioza Asiimwe, Prof. D.Nagaraja Kumari, Mugyenyi Edison, Mr. Odit Emmanuel et al.
Zenodo (CERN European Organization for Nuclear Research)
Technology-Enhanced Education Studies
article

Predictors of Artificial Intelligence Supported Inclusive Teaching Preparedness among Bachelor of Education Lecturers in Selected Ugandan Universities

Specioza Asiimwe, Prof. D.Nagaraja Kumari, Mugyenyi Edison, Mr. Odit Emmanuel, Dayamani Yetcherla
article en

Abstract

Abstract This study examined the predictors of Artificial intelligence (AI) supported inclusive teaching preparedness among lecturers teaching Bachelor of Education (B.Ed.) programmes in selected Ugandan universities. Specifically, it investigated the influence of institutional support, curriculum integration, continuous professional development, and self-efficacy on lecturers' readiness to integrate artificial intelligence into inclusive teaching. A cross-sectional survey design was employed involving 210 lecturers from selected public and private universities in Uganda. Data were collected using a structured questionnaire and analysed using descriptive statistics, Pearson's correlation, and multiple linear regression. The findings revealed a relatively low level of preparedness for AI-supported inclusive teaching (M = 2.47, SD = 0.75). Institutional support (r = .624, p < .001), curriculum integration (r = .587, p < .001), continuous professional development (r = .641, p < .001), and self-efficacy (r = .723, p < .001) were all positively associated with preparedness. Collectively, these variables explained 62.2% of the variance in preparedness (R² = .622, Adjusted R² = .615, F (4,205) = 64.18, p < .001), with self-efficacy emerging as the strongest predictor (β = .381, p < .001), followed by institutional support (β = .269, p < .001), continuous professional development (β = .214, p = .002), and curriculum integration (β = .163, p = .010). These findings highlight the importance of strengthening institutional support, embedding AI and inclusive pedagogy within teacher education curricula, expanding AI-focused professional development, and enhancing lecturers' confidence in applying AI technologies. The study contributes empirical evidence from the Ugandan higher education context and provides practical insights for universities and policymakers seeking to promote responsible, inclusive, and sustainable AI integration in teacher education.

Zenodo (CERN European Organization for Nuclear Research)
Andhra University (IN), Lira Hospital (UG)
Quality Education
Openalex Percentile: Top 2%
Technology-Enhanced Education Studies
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