mixed-methods
Filtering by topic mixed-methods(2)Clear all filters
- PaperOpenAlex — TESOL research21 Jul 2026
The Impact of AI Empowerment on the Development of College Students' English Speaking Skills: A Mixed-Methods Analysis Based on Speech Competence Assessment and Self-Directed Learning Efficiency
Jian Li, Luo ZhiYao
A mixed-methods study with 20 Chinese college students found that using the Doubao AI platform for autonomous speaking practice significantly improved fluency, pronunciation, and lexical resources. AI empowerment enhanced self-directed learning efficiency through synergy between self-determination theory and self-regulated learning, though a 'situational gap' between virtual practice and real interaction persisted.
Original abstract
In the context of the digital transformation of global education, artificial intelligence (AI) has become a transformative force in the field of second language acquisition. This study explores the impact of AI empowerment on the development of Chinese college students’ oral English competence and the efficiency of self-directed learning (SDL). The core of the research is to explore the synergy between the self-determination theory (SDT) and the self-regulated learning (SRL) model to clarify the interaction between technology empowerment and psychological mechanisms. Under this framework, SDL is the main learning mode, while SDT provides motivational basis (answering students “why” initiates SDL), and SRL provides strategic guarantee (answering students “how” manages learning process). This study adopts a mixed research method and conducts a six-week quasi-experiment with 20 International Business English majors students from Guangdong University of Foreign Studies. During the winter vacation, participants used the Doubao AI platform for autonomous speaking practice and received instant, data-driven feedback. The data were comprehensively analyzed through pre-test and post-test (measuring fluency, lexical resources, accuracy and pronunciation), validated questionnaires and qualitative interviews. The results show that AI empowerment significantly improves objective speaking ability, especially in terms of fluency, pronunciation and lexical resource. More importantly, the study found that there is a deep synergistic effect between the learner’s motivation process and the strategy process: the satisfaction of the two basic psychological needs of autonomy and ability (SDT) provides the internal motivation for students to enter the three cycle stages of SRL; the effective strategy execution guided by the role of AI “virtual supervisor” further strengthens the learners’ sense of achievement, thus jointly improving the overall SDL efficiency. Despite these advances, the study also found a “situational gap” between virtual practice and real social interaction. This study highlights the importance of an AI-supported teaching model that combines technical efficiency with human-led strategic guidance to optimize the self-directed development of oral English in the digital age.
- PaperLanguage Learning & Technology20 Jul 2026
The development of EFL learners’ self-efficacy in technology use environments
Xuelian Li, Manzhen Yang
A mixed-methods study tracked 102 university EFL learners over 15 weeks, finding significant increases in overall, speaking, listening, and writing self-efficacy, but not reading self-efficacy, in technology-rich environments. Qualitative data revealed how technological affordances shaped the four sources of self-efficacy, explaining the lack of reading self-efficacy growth. The study offers pedagogical implications for strengthening reading self-efficacy and optimizing technology use in language learning.
Original abstract
Self-efficacy plays a critical role in language learning motivation, performance, and achievement. However, limited research has examined how different dimensions of English self-efficacy evolve over time and the mechanisms underlying such development in technology use environments. To address these gaps, the present study adopted a mixed-methods design. A total of 102 university students completed questionnaires at three time points over a 15-week period, and 10 participants subsequently took part in semi-structured follow-up interviews. Quantitative findings revealed a significant increase in overall English self-efficacy, as well as in three specific dimensions: speaking, listening, and writing self-efficacy. Qualitative findings further demonstrated how the four sources of self-efficacy, shaped by technological affordances, were associated with students’ efficacy beliefs and helped explain why reading self-efficacy did not show a statistically significant increase within the same learning ecology. The study concluded by discussing pedagogical implications for strengthening reading self-efficacy and optimizing the use of technology in language learning, as well as limitations of the study and directions for future research.