University English Writing Teaching Quality Evaluation Driven by Artificial Intelligence in the Context of New Liberal Arts: Linguistic Neutrosophic Multivalued Approach - Xiaoling Lyu; Feng Li; Yiming Zhao (Neutrosophic Sets and Systems)
In the evolving landscape of higher education, English writing instruction is being reshaped by the fusion of Artificial Intelligence (AI) and the New Liberal Arts framework. This study aims to develop a comprehensive quality evaluation model for university-level English writing education by integrating AI-driven insights with pedagogical innovation. This study uses decision making approaches to select the best criterion and alternative. We use the RAM method to rank the alternatives. The decision-making approach is used under the Linguistic Neutrosophic Multivalued to deal with uncertainty and vague information. Eight distinct criteria are established to assess instructional efficacy, linguistic competence development, cognitive engagement, and adaptive feedback mechanisms. Furthermore, eight alternatives, ranging from intelligent tutoring systems to generative text platforms, are evaluated. The framework not only enhances the objectivity of quality assessment but also promotes personalized and dynamic learning environments aligned with educational goals. The research offers actionable guidance for educators, administrators, and policymakers seeking to modernize writing instruction under the paradigm of AI and humanistic learning.