ai-language-learning
Filtering by topic ai-language-learning(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
This study found that AI empowerment significantly improved Chinese college students' oral English fluency, pronunciation, and lexical resources through a six-week quasi-experiment using the Doubao AI platform. It also revealed a synergistic effect between self-determination theory and self-regulated learning that enhanced self-directed learning efficiency, though a 'situational gap' between virtual practice and real interaction was identified.
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 & Technology1 Jan 2026
Emotional CALL: Reframing technology-mediated language learning
Mariusz Kruk, Mirosław Pawlak
This paper introduces Emotional CALL, a framework that places emotions at the center of technology-mediated language learning. It argues that beyond anxiety, both positive and negative emotions such as enjoyment, boredom, and hope dynamically interact with appraisals, engagement, and performance in digital environments. The authors draw on L2 emotion research and recent CALL studies to propose conceptual foundations and a working framework for future inquiry, emphasizing the need to consider AI-mediated learning within this broader affective turn.
Original abstract
Research on emotions has become one of the most vibrant areas of inquiry in second language acquisition, yet its integration into computer-assisted language learning (CALL) has remained uneven. While anxiety has received sustained attention in CALL research, other emotions, both positive (such as enjoyment or hope) and negative emotions (such as boredom or shame), along with affectively relevant constructs such as flow, belongingness, and emotional engagement, have only recently begun to receive the systematic attention they deserve. Emotional CALL represents an emerging area of inquiry that places emotions at the center of how technology-mediated language learning is theorized, investigated, designed, and evaluated. Emotional CALL does not refer to a single technology, method, or chronological stage. Rather, it signals a contemporary affective turn in CALL in which emotions are understood as dynamic, socially situated, task-sensitive, technologically mediated, and pedagogically consequential processes. AI-mediated language learning gives this agenda particular urgency but does not exhaust its scope. Developments in L2 emotion research, individual differences in CALL, and recent empirical work in technology-mediated language learning provide the basis for outlining the conceptual foundations of Emotional CALL and proposing a working framework for future inquiry. The central argument is that technology-mediated language learning cannot be fully understood without attention to how learners and teachers emotionally experience digital environments. Emotional CALL therefore examines how emotions interact with appraisals, engagement, willingness to communicate, cognitive load, identity, and performance, and how pedagogical design can support emotionally responsive CALL.