Speaker
Description
Independent study of English phonology poses a significant challenge for Vietnamese EFL students, particularly in analyzing syllable structures. Consequently, many learners turn to generative AI tools like ChatGPT or Gemini; however, these models frequently provide inconsistent or incorrect results upon every page refresh. To address this, this study evaluates the effectiveness of SyllableLab, a researcher-designed, rule-based web platform developed to foster learner autonomy and parsing accuracy through deterministic syllable division, visual tree diagrams, and gamified features. Applying a mixed-methods research design, a pilot study was conducted with 40 Vietnamese EFL students who engaged with the platform during their self-regulated study. Data were gathered through pre- and post-tests to measure phonetic accuracy, alongside an open-ended survey to investigate student perceptions of their learning agency. The quantitative results demonstrated a significant improvement in the students' ability to accurately parse onsets, nuclei, and codas. Qualitatively, the findings revealed that SyllableLab’s algorithmic stability and step-by-step interactive scaffolding successfully mitigated AI-dependency, empowering learners to self-correct and confidently direct their own phonological study. The study concludes that specialized, rule-based Computer-Assisted Language Learning (CALL) applications provide a critical pedagogical baseline for reliable, independent language acquisition in the AI era.
Keywords: Learner Autonomy, SyllableLab, Rule-Based Parsing, Generative AI Inconsistency, Phonology Self-Study.
