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

Abstract

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. 

DOI: 10.5281/zenodo.15200126