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

Authors

Xiaoling Lyu Shanghai Zhongqiao Vocational and Technical University, Jinshan, Shanghai, 201500, China
Feng Li Blue Elephant Intelligence (Hangzhou) Technology Co., Ltd. Hangzhou, 310000, China
Yiming Zhao Xianda College of Economics and Humanities, Shanghai International Studies University, Chongming, Shanghai, 202162, China

Keywords:

Linguistic Neutrosophic Multivalued; Artificial Intelligence; Liberal Arts; English Writing Teaching.

Abstract

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.

DOI: 10.5281/zenodo.15207909