Scale for students’ attitude towards AIGC feedback in english pronunciation learning: Development, validation and application - Weihe Zhong; Yanchao Yang; Bosheng Jing; Xinxin Yang; Zehan Tan; Qiu Wei (PLoS ONE, 2025)
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.