speaking-assessment
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- PaperLanguage Testing24 Jul 2026
Investigating the Real-World Relevance of an Academic English Speaking Test: Extrapolating Subjective Evaluations and Linguistic Performance Characteristics
Daniel R. Isbell, Dustin Crowther, Jieun Kim, Yoonseo Kim
The study examined correlations between TOEFL Essentials speaking scores and linguistic characteristics with academic speaking tasks in lab and course settings. Strong correlations were found, particularly for fluency and accuracy, supporting the extrapolation of test performances to academic contexts.
Original abstract
To support use of tests in academic contexts, it is critical to demonstrate that test scores and test performances are associated with performance in academic settings—an inferential link referred to as extrapolation in argument-based validation frameworks. TOEFL Essentials is a newer test designed to measure both general and academic English and is intended for use in higher education. The TOEFL Essentials speaking section consists of Virtual Interview, Read Aloud, and Listen & Repeat tasks, the latter two of which elicit highly constrained responses that may be less reflective of academic speaking tasks. In this study, we examined correlations of scores and linguistic characteristics across TOEFL Essentials speaking performances and (a) lab-based academic tasks (graph description, lecture response) for 149 students and (b) an authentic course-based speaking task for 65 students. Strong correlations (.65 < r < .80) were found between TOEFL Essentials speaking scores and evaluations of academic speaking. Among linguistic characteristics, fluency and accuracy variables demonstrated the largest and most consistent correlations across test and non-test tasks. Findings provide evidence relevant to the extrapolation of TOEFL Essentials speaking performances, which are based in part on highly constrained tasks, to academic settings and help inform decisions about test use.
- 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.