From Data to Decisions based on AI-Driven Insights: Intelligent Evaluation of English Translation Pedagogy in Higher Education under Interval Complex Neutrosophic Set

In the wake of rapid technological advancements, higher education institutions are increasingly exploring artificial intelligence (AI) for enhanced language pedagogy. This study delves into the intelligent evaluation of English translation teaching, leveraging AI-driven tools to provide real-time feedback, identify errors, and personalize instruction. Through a carefully structured Multi-Criteria Decision-Making (MCDM) model, we assess six innovative AI-powered alternatives using eight pedagogically relevant criteria. The goal is to identify the most effective solution that aligns with curricular standards and improves learning outcomes. The research aims to bridge data-driven technologies with humanistic language instruction. We use the interval complex neutrosophic set (ICNS) to deal with uncertainty and vague information. The ARAS method is used under the ICNS to rank the alternatives. The application is provided to show the validation of the proposed approach.