PRIORITIZATION OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN HIGHER EDUCATION USING THE PIPRECIA-S MCDM MODEL

This study applies the PIPRECIA-S model to evaluate and prioritize artificial intelligence (AI) applications in higher education. The aim is to identify which AI-driven solutions offer the greatest potential for improving teaching, learning and institutional management. Seven relevant criteria were defined based on the literature and expert consultation: pedagogical effectiveness, student engagement, adaptability, ease of implementation, data security, cost efficiency and long-term sustainability. Four AI applications were selected for evaluation: adaptive learning systems, intelligent tutoring systems, predictive analytics for student success and automated administrative tools. A panel of five domain experts independently provided pairwise comparisons to weight the criteria, while a separate panel assessed the alternatives. The results indicate that Adaptive Learning Systems achieved the highest overall score (4.375), followed by Intelligent Tutoring Systems (4.107) and Predictive Analytics (4.062), while Administrative Tools (3.647) ranked lowest. These findings highlight the pedagogical priority of AI solutions that directly enhance learning outcomes and student retention. The paper contributes by demonstrating how multi-criteria decision-making (MCDM) models, such as PIPRECIA-S, can support evidence-based policy and strategic planning in higher education, providing both theoretical insights and practical guidelines for sustainable AI integration.