Despite the growing body of research exploring artificial intelligence (AI) in educational contexts, there remains a critical gap in understanding the specific effects of generative AI (GAI) on English language learning, particularly within higher education in Thailand. This study addresses this gap by employing an explanatory sequential mixed-method design to explore both student and teacher perspectives. Quantitative data were analyzed using descriptive statistics, Mann-Whitney U tests, Spearman correlation analysis, and Kendall–Theil regression to examine students' acceptance, usage, and the relationship between GAI engagement and academic performance. Qualitative data, collected through structured written interviews, were analyzed using thematic analysis to uncover key themes from both student and teacher narratives. Quantitative findings reveal high student acceptance of GAI tools in terms of performance expectancy (M = 3.66, SD = .58), effort expectancy (M = 3.61, SD = .59), facilitating conditions (M = 3.51, SD = .59), and use behavior (M = 3.52, SD = .48), while social influence remained relatively lower (M = 3.31, SD = .54). Mann-Whitney U tests showed no statistically significant differences in GAI usage across high- and low-performing students (p > .05). Correlation analyses indicated strong associations between performance expectancy and other factors (ρ = .87, p < .001), yet no significant correlation with GPA (ρ = −.06, p = .76). Qualitative results from student reflections revealed improved efficiency, enhanced engagement, and increased linguistic confidence. Yet, concerns about overreliance on AI and the necessity for critical use were noted. Teacher narratives accentuated ethical concerns and the potential for GAI misuse, highlighting the need for balanced, responsible integration of AI into pedagogical practices. These findings underscore the potential of GAI to enhance learning experiences while emphasizing the importance of maintaining academic integrity and fostering critical thinking.