Best Practices Predictive Model Developed on Absenteeism of College Students and Its Effect on Career Development Using Fuzzy Logic (Fl): AI Approach

Student absenteeism in higher education-covering classroom learning, practical/skill laboratories, and co-curricular activities under the framework of National Education Policy 2020 (NEP 2020) has emerged as a significant predictor of academic performance and long-term career development. However, absentee behaviour is nonlinear, uncertain, and influenced by socio-academic factors, making classical statistical models insufficient. This research proposes a Fuzzy Logic (FL)-based Artificial Intelligence (AI) predictive model to evaluate the impact of multidimensional absenteeism on Career Output among UG and PG students. Using a dataset of 100 students with nonlinear and uncertain attendance patterns, a Mamdani Fuzzy Inference System (FIS) is developed to classify students into five career categories: Placed, Higher Studies, Competitive Preparation, Unemployed, and Career Gap. The model achieved 92% predictive accuracy and demonstrated a strong inverse correlation (r ≈ –0.88) between aggregated absenteeism and CGPA. The study recommends AI-enabled attendance analytics and early-warning systems as institutional best practices aligned with the national vision of Viksit Bharat 2047.