TIAN JING XUAN
The Education University of Hong Kong
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Presentation Fostering Spoken Corpus and AI Literacy for Preservice English Teacher Training more
Sun, Jun 14, 15:45-16:10 Asia/Tokyo
This qualitative study aims to initiate a 5-month online yarning circle (relational indigenous methodology fostering mutual help through reflection and discussion [Shay, 2021]) in teacher training on enhancing English pre-service teachers’ (EPTs’) corpus and AI literacy in English oral teaching skills. Three doctoral students who had received applied linguistics training for over 4 years served as mentors and paired up with 3 undergraduate students who were EPTs. The three mentors facilitated weekly discussion with their mentees on key indicators that influence English oral performance and communication strategies that could be used in English oral communication and co-developed corpus and AI-integrated English speaking lesson plans. Qualitative results identified the effectiveness of this online yarning circle. Both mentors and mentees highlighted the benefits gained from their partnerships in English oral teaching. The EPTs reported growth in their linguistic knowledge, corpus and AI literacy, and the ability to integrate linguistic knowledge with educational technology to design English speaking lessons. Mentors learned how to develop teacherfriendly resources from EPTs. For instance, mentors provided guidelines on key indicators that influence English speaking performance. The EPTs offered suggestions to make the indicators more suitable for secondary school contexts based on their teaching practice in local schools.
Presentation From Data to Dialogue: Using Corpus and AI to Enhance Self-Directed English Speaking more
Sat, Jun 13, 15:50-16:15 Asia/Tokyo
Technology is advancing rapidly and is increasingly encouraged for use in language teaching and learning. This study aims to design a Corpus- and AI-aided self-directed English speaking training programme, examine its effectiveness, and explore learners’ attitudes toward this approach. Thirty-one undergraduate EFL learners participated. They completed a pre-test, a five-session Corpus- and AI-supported speaking training, a post-test, a learning portfolio, a survey, and a follow-up interview. Findings showed that participants’ overall speaking performance improved after the training, along with gains in four subskills: lexical resource (LR), grammatical range and accuracy (GA), fluency and coherence (FC), and pronunciation (PN). Among these, the greatest progress occurred in LR, while PN showed the least improvement. Learners agreed that the inclusion of a spoken corpus enabled them to identify linguistic features influencing oral proficiency, and that AI tools were effective in supporting speaking practice. The AI-enhanced environment also promoted interactive, self-directed learning. Most participants expressed positive attitudes toward the combined Corpus and AI-aided approach and demonstrated willingness to continue interacting with AI tools despite some limitations. However, a few learners reported their insufficient AI literacy and concerns about AI feedback accuracy. Overall, the study provides theoretical and pedagogical insights for enhancing English speaking instruction.