hj howard Chen
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Presentation Developing an N-gram–Based Spoken Lecture Corpus Tool to Support Non-Native EMI Teachers more
Many non-native English-speaking instructors face linguistic challenges when delivering English-medium instruction (EMI), particularly in using discipline-appropriate spoken academic phraseology. This paper presents the development of an n-gram–based spoken lecture corpus tool designed to support EMI teachers through large-scale lecture data. The corpus was compiled from approximately 1,100 open-source academic lecture transcripts across multiple universities and disciplines, resulting in about eight million words. The tool enables users to search four- to six-word n-grams extracted from authentic lectures. Given a target word or phrase, the system generates frequent n-gram patterns and provides multiple contextualized examples from real lecture transcripts, allowing users to observe how academic language is used in spoken teaching contexts. The tool was introduced to a group of university instructors teaching EMI courses. Informal feedback indicates that the system was perceived as intuitive and useful for lecture preparation, phrase selection, and increasing confidence in English delivery. Participants particularly valued access to spoken academic patterns that are rarely addressed in conventional EMI training materials. Although large-scale evaluation has not yet been conducted, this study demonstrates the potential of repurposing open lecture transcripts into practical corpus-based support tools and highlights the pedagogical value of n-gram exploration for EMI teacher development.
Presentation Designing a Gamified ASR-Based Oral Practice Game Using Google AI Studio for Young ESL Learners more
Recent advances in automatic speech recognition (ASR) and generative AI have enabled new forms of oral language practice beyond traditional, test-oriented speaking tasks. This paper reports on the design and classroom use of a gamified ASR-based oral practice platform developed with Google AI Studio for elementary-level ESL learners. The system integrates ASR into a fast-paced pronunciation game inspired by falling-block mechanics. Learners practice words, phrases, and sentences by speaking aloud: accurately pronounced items disappear, while inaccurate attempts cause items to fall and accumulate as “bricks,” ending the game once a preset height is reached. The platform was rapidly prototyped using Google AI Studio to manage ASR processing, pronunciation tolerance thresholds, and prompt-based feedback, allowing flexible refinement without complex backend development. The web-based game was piloted in an elementary classroom setting. Questionnaire data and classroom observations indicate generally positive learner responses, including high engagement, increased willingness to repeat pronunciation attempts, and reduced speaking anxiety compared with traditional drill-based activities. Teachers also reported sustained attention and voluntary practice. Although no quantitative pronunciation gains were measured, the findings suggest that gamified ASR environments can serve as effective supplementary tools for young learners and highlight the pedagogical potential of generative AI for CALL development.