An Interval-Valued Fermatean Fuzzy SWOT-AHP-Based Model for Assessing Artificial Intelligence Adoption for Sustainable Development Goal 4

Vollständiger Abstract

Worum geht es in dieser Arbeit?

This study adopted an interval-valued Fermatean fuzzy analytical hierarchy process (IVFF-AHP)-based strategic approach to assess the strengths, weaknesses, opportunities, and threats (SWOT) related to the role of artificial intelligence (AI) in achieving quality education under Sustainable Development Goal 4 (SDG 4). First, 14 SWOT factors were identified based on a literature review. Data were then collected from three domain experts, and the IVFF-AHP approach was applied to determine the weights of the SWOT factors. The findings reveal that AI-driven personalized learning content (S2) and intelligent tutoring systems for personalized and special-needs education (O2) serve as key enablers of quality education. In contrast, insufficient training in the use of educational technology (W3) and inequality in digital and AI access (T2) constitute major barriers to achieving this goal. These findings provide a structured basis for prioritizing investments in personalized learning technologies, teacher training, and equitable digital infrastructure. The proposed framework also demonstrates the usefulness of IVFF-AHP for evaluating educational strategies when expert judgments involve uncertainty and hesitation. Moreover, the framework can be adapted to assess AI-supported education policies in different institutional and national contexts. The study makes a meaningful contribution to the decision sciences and management literature by offering practical insights for policymakers seeking to achieve quality education and concludes by outlining clear avenues for future research.