CFA dataset.

This study develops and validates the Scale of Students’ Perception of AIGC Feedback for English Pronunciation Learning. The research was conducted at a university in northern China using a convenience sampling method. The exploratory factor analysis (EFA) involved 207 participants, while the confirmatory factor analysis (CFA) included 229 participants. Based on interviews with 10 students who had used AIGC tools for English pronunciation learning, 16 representative items were identified. Expert validation was performed through interviews with 8 experts—four English pronunciation teachers with extensive experience using AIGC in teaching, and four AIGC specialists. Content validity was confirmed, and all items were retained. The EFA results revealed four dimensions: Accuracy, Strictness, Clarity, and Personalisation. The CFA results demonstrated good structural and convergent validity. However, the discriminant validity was slightly problematic. Concurrent validity was confirmed by the high correlation between the scale and perceived English Pronunciation Self-efficacy. The study has several limitations, including its cross-sectional design, limited sample diversity, and reliance on traditional validation methods (EFA and CFA), suggesting the need for test-retest reliability, a more diverse sample, and alternative methods like Item Response Theory (IRT) or Network Analysis in future research. The validated scale offers valuable insights into how students perceive and interact with generative AI tools, and it can serve as a useful instrument for educators and researchers interested in exploring the impact of AI feedback systems on language learning.

本研究开发并验证了学生英语发音学习人工智能生成内容（Artificial Intelligence Generated Content，AIGC）反馈感知量表。本研究在中国北方某高校开展，采用方便抽样法。探索性因子分析（Exploratory Factor Analysis，EFA）纳入207名被试，验证性因子分析（Confirmatory Factor Analysis，CFA）则纳入229名被试。研究团队通过对10名使用AIGC工具开展英语发音学习的学生进行访谈，最终确定16个代表性题项。随后通过访谈8位专家开展专家效度检验：其中4位为具备丰富AIGC教学应用经验的英语发音教师，另外4位为AIGC领域专家。经检验，内容效度良好，所有题项均予以保留。探索性因子分析结果显示该量表包含四个维度：准确性、严格性、清晰性与个性化。验证性因子分析结果表明量表具备良好的结构效度与聚合效度，但区分效度存在些许不足。通过该量表得分与英语发音学习自我效能感感知得分间的高相关性，验证了同时效度。本研究存在若干局限：采用横断面设计、样本多样性不足，且仅依赖传统验证方法（EFA与CFA）。未来研究可通过重测信度检验、扩大样本多样性，以及采用项目反应理论（Item Response Theory，IRT）或网络分析等替代方法加以完善。经验证的该量表可为理解学生对生成式AI工具的感知与互动方式提供重要参考，同时也可为探究AI反馈系统对语言学习影响的教育工作者与研究者提供实用的研究工具。