Reading assistant software and its impact on speaking fluency, grammar accuracy, and narrative quality among EFL learners

Despite increased integration of technology in language education, many EFL learners in Thailand continue to struggle with spoken English proficiency. Traditional instruction often lacks sufficient support for real-time speech development, and limited research has explored the use of AI-supported reading tools to address this gap. This study aimed to examine whether the use of Reading Assistant (RA) software enhances speaking fluency, grammatical accuracy, and narrative structure quality among Thai university students. A quantitative research approach was employed, involving 104 undergraduate EFL students over a 15-week intervention period. Pre-and post-tests assessed fluency (e.g., speech rate, word count), accuracy (error-free clauses), and narrative quality (coherence, sequencing, detail). Correlational analysis explored the relationship between RA software engagement and language development. Students demonstrated significant improvements in fluency and a reduction in disfluencies after sustained use of RA software. Engagement with the software was positively correlated with improvements in both fluency and grammatical accuracy. An enhanced narrative structure was also observed, particularly in terms of coherence and relevance to the visual prompts. These findings support the value of AI-supported reading tools in developing oral language skills and underscore the need for broader institutional support to ensure equitable access.