Innovative Research on AI-Enabled Innovation of Multiple Evaluation Systems in College English Teaching
Year: 2025
Type: article
Abstract: With the deep integration of artificial intelligence (AI) and education, the reform of college English teaching evaluation has entered a new stage. Traditional college English evaluation systems are faced with problems such as over-reliance on summative assessment, a single evaluation dimension, and lagging feedback. This study explores the innovative path of multiple evaluation systems in college English teaching empowered by AI, aiming to construct a dynamic, comprehensive, and personalized evaluation model. Through literature review, case analysis, and empirical research, it is found that AI technologies such as natural language processing, machine learning, and big data analytics can effectively support the diversification of evaluation subjects (teachers, students, peers, AI systems), the enrichment of evaluation dimensions (knowledge mastery, language competence, learning processes, and innovative thinking), and the intelligence of evaluation feedback. However, challenges such as algorithmic bias, data privacy risks, and the weakening of teacher-student interaction still exist. Finally, this study proposes countermeasures, including optimizing AI algorithms, strengthening data security, and balancing AI and human roles, to provide theoretical and practical references for the reform of college English teaching evaluation.
Authors: Shuo Chen, Jing Yang
Institution: Changchun University of Finance and Economics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4414002836