Likelihood of AI Tools Adoption and Interest in Professional Development Opportunities in Higher Education: An Ordinal Logistic Regression Analysis

Abstract

This study explored the factors influencing academic staff’s readiness to use artificial intelligence
(AI) tools and participate in AI-related professional development, utilizing a quantitative approach. Data from
95 academic staff members of the University of Vlora “Ismail Qemali” were gathered via an online survey. The
analysis, conducted using univariate ordinal logistic regression, pinpointed key predictors of educational AI
tools adoption likelihood and interest in attending AI professional development opportunities. Rigorous
evaluation of model fit, influence diagnostics, and cross-validation was conducted to ensure the findings’
reliability and accuracy. Results highlight the critical role of interest in AI educational tools development,
technological proficiency, and past use of AI educational tools in determining the likelihood of adopting
educational AI tools, underscoring the pivotal importance of fostering a genuine interest in AI. Furthermore, the
research identifies gender as a significant factor influencing interest in attending AI professional development
opportunities, while negative perceptions of AI’s role in education tend to reduce such interest. These findings
stress the need for targeted efforts to enhance educators’ readiness for AI, mitigate gender disparities, and
correct misconceptions about AI. By revealing the complex factors affecting educators’ willingness to adopt AI
technologies, this study advocates for a holistic strategy encompassing a broader range of influences. It provides
actionable insights for educational policymakers, curriculum developers, and AI tool creators to create an
environment conducive to AI adoption in higher education. Although limited by its use of convenience sampling
and focus on a single institution, this research offers essential insights into the dynamics of AI adoption in
education. It lays a foundation for strategies that encourage innovation, inclusivity, and a forward-thinking
approach to integrating AI into future teaching and learning.