A novel interval valued Fermatean fuzzy FUCA approach to assess entrepreneurship education and career planning

Abstract Entrepreneurship education and career planning play a crucial role in equipping learners with the skills, adaptability, and strategic insight required in today’s dynamic labor market. However, the evaluation of entrepreneurship education and career planning strategies remains a challenging decision-making problem due to the presence of multiple conflicting criteria, uncertainty, expert hesitation, and subjective assessments. Furthermore, there is a lack of a robust multicriteria decision-making (MCDM) framework capable of simultaneously handling interval-valued Fermatean fuzzy uncertainty and stable ranking behavior in such educational decision environments. This paper proposes a novel Interval-Valued Fermatean Fuzzy Faire Un Choix Adequat (IVFF-FUCA) method to address these challenges and provide a reliable decision-support framework. The proposed approach integrates IVFF information with the FUCA mechanism to effectively capture uncertainty and hesitation while generating stable and discriminative rankings of alternatives. The case study presents a hypothetical scenario involving four decision-makers, seven criteria, and fifteen alternative strategies, where benefit criteria include entrepreneurial skill development, career readiness, adaptability, leadership enhancement, and innovation integration, while cost criteria include implementation cost and resource/time consumption. The IVFF-FUCA approach aggregates expert judgments, prioritizes alternatives, and manages uncertainty throughout the evaluation process. Sensitivity and stability analyses demonstrate that the obtained rankings remain robust under variations in decision-maker weights and criterion importance. Comparative analysis with existing IVFF-based MCDM approaches further confirms the superior discrimination capability and ranking stability of the proposed method. The findings identify high-performing strategies, including Policy-Driven Entrepreneurship Education Models and AI-Based Career Guidance Systems, and provide useful insights for educators, career advisors, and policymakers. The main contribution of this study is the development of a new IVFF-FUCA decision-making framework that extends the applicability of FUCA under IVFF environments for evaluating entrepreneurship education and career planning strategies under uncertainty.