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

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 from95 academic staff members of the University of Vlora “Ismail Qemali” were gathered via an online survey. Theanalysis, conducted using univariate ordinal logistic regression, pinpointed key predictors of educational AItools adoption likelihood and interest in attending AI professional development opportunities. Rigorousevaluation 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 adoptingeducational AI tools, underscoring the pivotal importance of fostering a genuine interest in AI. Furthermore, theresearch identifies gender as a significant factor influencing interest in attending AI professional developmentopportunities, while negative perceptions of AI’s role in education tend to reduce such interest. These findingsstress the need for targeted efforts to enhance educators’ readiness for AI, mitigate gender disparities, andcorrect misconceptions about AI. By revealing the complex factors affecting educators’ willingness to adopt AItechnologies, this study advocates for a holistic strategy encompassing a broader range of influences. It providesactionable insights for educational policymakers, curriculum developers, and AI tool creators to create anenvironment conducive to AI adoption in higher education. Although limited by its use of convenience samplingand focus on a single institution, this research offers essential insights into the dynamics of AI adoption ineducation. It lays a foundation for strategies that encourage innovation, inclusivity, and a forward-thinkingapproach to integrating AI into future teaching and learning.