Leveraging artificial intelligence and Big Data Analytics to model career progression and professional development pathways of higher education faculty

Research output, teaching quality, and participation in professional activities are key components of a faculty career and are typically evaluated collectively. However, traditional promotion systems often involve subjectivity and bias in assessing faculty performance. Advances in Artificial Intelligence (AI) and Big Data Analytics enable a more objective, data-driven approach to evaluating faculty career development. This study aims to develop and validate an AI-based predictive model to assess faculty career trajectories using an integrated analytical framework that combines major determinants such as research productivity, teaching effectiveness, professional training, and competency development. A mixed-methods approach was employed with a sample of 135 faculty members. Quantitative data were analyzed using descriptive statistics and correlation analyses in SPSS. Additionally, a Career Progression Prediction Index (CPPI) was developed using a Random Forest Classifier to predict faculty career progression. Path analysis revealed that research productivity (β = 0.51, p = 0.001), teaching effectiveness (β = 0.48, p = 0.002), professional training (β = 0.43, p = 0.004), competency development (β = 0.39, p = 0.007), and the AI-based prediction model (β = 0.50, p = 0.005) significantly influenced career progression. The proposed AI-driven framework provides a transparent and equitable approach to faculty promotion decisions.