Evaluation and Quality Assurance in Digital Learning Environments Focus on English Language Teaching
Keywords:
Digital learning environments, Competence-oriented evaluation, Quality assurance, Algorithmic bias, Communicative competenceAbstract
With the rapid integration of artificial intelligence (AI) technologies such as natural language processing (NLP) tools, adaptive learning platforms, and automated writing evaluation (AWE) systems into higher education, digital learning environments have become the new norm for English Language Teaching (ELT) in Chinese universities. This paper focuses on the transformation, challenges, and quality assurance (QA) mechanisms of ELT evaluation in AI-enhanced digital contexts, drawing on empirical data from the author’s teaching practice at Anhui Sanlian University (2021–2023) and theoretical frameworks of educational evaluation, communicative competence, and AI ethics. The study explores three core dimensions: (1) the paradigm shift from knowledge-centric, summative evaluation to competence-oriented, personalized assessment driven by AI; (2) key challenges including the limited validity of AI assessments in measuring higher-order competences (e.g., critical thinking, cultural appropriateness), algorithmic bias against non-native English varieties, digital divides, and over-reliance on technology; and (3) a multi-dimensional QA framework tailored to AI-integrated ELT, emphasizing stakeholder collaboration, CEFR-aligned competence standards, continuous data-driven improvement, and ethical equitable practices. Empirical findings from student surveys (n=386), teacher interviews (n=15), and course analytics indicate that the proposed framework enhances evaluation validity (AI-human assessment alignment coefficient = 0.85), and improves student satisfaction with digital learning (from 58% to 82%). This research contributes to the literature on digital education evaluation by providing context-specific, actionable guidelines for ELT educators, program administrators, and policymakers seeking to leverage AI’s potential while ensuring the rigor, fairness, and effectiveness of evaluation in digital learning environments.
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