TITLE:
A Generative AI Teaching Ecosystem and English Majors’ Learning Emotions, Classroom Engagement, and Oral Willingness to Communicate: Evidence from an English Audio-Visual-Oral Course
AUTHORS:
Yue Zhang
KEYWORDS:
Generative Artificial Intelligence, English Audio-Visual-Oral Course, Foreign Language Enjoyment, Classroom Engagement, Willingness to Communicate, Foreign Language Speaking Anxiety
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.11,
November
5,
2026
ABSTRACT: Since generative artificial intelligence (AI) entered the foreign language classroom, evidence on its relationship with learners’ emotions and participation has come mostly from short-term studies of single tools; semester-long, multi-component course ecosystems remain under-examined. This study compared six intact classes of first-year English majors in an English Audio-Visual-Oral course at an application-oriented university in China: five classes (n = 144) taught within an “AI teaching ecosystem” (graded AI text-dialogue tasks, AI micro-lectures, and AI review songs embedded in the course’s online smart course) and one class (n = 30) taught conventionally. Learning emotions, classroom engagement, oral willingness to communicate (WTC), AI literacy, and final examination scores were compared at the end of the semester. Neither primary outcome showed a between-group difference: speaking anxiety, measured by a single retained item that replaced the planned eight-item scale (g = 0.23), and WTC (g = 0.05). Among the other outcomes, the AI-ecosystem classes reported higher private and social enjoyment (g = 0.62 and 0.83). In student-level models that treat students as independent observations, both differences survived correction for multiple comparisons, and social enjoyment did so in every sensitivity analysis; because the conventional condition comprised a single class, these p values do not provide condition-level evidence. Classroom engagement, AI literacy, and examination scores showed no detectable differences. Within the AI-ecosystem group, acceptance of the AI activities correlated positively with enjoyment, engagement, and WTC. Drawing on the asymmetry of antecedents across constructs and a “shared experience” hypothesis, the paper argues that the differences associated with an AI teaching ecosystem systematically embedded in a course concentrate in the emotional ecology of the classroom, particularly collective enjoyment. For anxiety, the null result is consistent with the absence of a direct pathway from text-based dialogue tasks to oral classroom situations; for WTC, a null between-group contrast coexists with a positive within-group association that this design cannot resolve. Because the teaching condition was confounded with class, teacher, and additional graded practice in this intact-class comparison, the associations cannot be interpreted causally.