In recent years, language learning has increasingly embraced learner-centered methodologies and technological advancements, including mobile technologies. These tools play a crucial role in personalizing learning materials, providing flexible, interactive, and structured resources. Since learners use these technologies both inside and outside the classroom, balancing traditional instruction with mobile-assisted learning becomes essential. To fully harness the benefits of mobile learning while maintaining alignment with curricular goals, it is necessary to determine which content is best delivered in class and what can be effectively reinforced through mobile technologies.

To address this matter, a web app for mobile devices was developed for Italian language learners aiming for B2 proficiency (CEFR) within the EsaBac program. Designed to meet curriculum requirements, the app supports both written and spoken language skills while integrating Mobile-Assisted Language Learning (MALL, Bagaglini, 2022) and Data-Driven Learning (DDL, Johns, 1991) strategies, fostering autonomy and personalization by engaging students with authentic language materials through corpus consultation. Additionally, an AI-powered chatbot provides learners with interactive practice, enhancing their engagement and reinforcing language learning outside the classroom. Insights from a pilot phase guided the structuring of the application, which offers flexible learning activities for different proficiency levels (B1-B2). These activities integrate both direct and indirect DDL techniques and were designed based on a needs assessment, a focus group interview, and an analysis of an Italian learner corpus.

This research aims to explore the integration of mobile DDL in secondary education by utilising mobile technologies, AI-driven tools and corpus-based resources to provide personalised language learning experiences. The study, currently being conducted in three French high schools, involves students from diverse linguistic backgrounds, including second-generation immigrants, dialect speakers, and those who have learned Italian exclusively in school. This diversity necessitates a personalised approach to bridge learning gaps and strengthen language skills. Data collection involves both qualitative and quantitative methods, including questionnaires, focus group discussions, automatic usage tracking, and user feedback. The objective is to assess student interaction, engagement, self-directed learning progress, and the adaptability of the app to different learner profiles.

This talk ultimately contributes to the broader discussion on how mobile learning, enhanced by corpus-based methodologies and AI chatbots, supports language acquisition in secondary education, ensuring curricular alignment, while presenting the preliminary findings of this study.

References:
[1] Bagaglini, V. (2022). Mobile Assisted Language Learning: un potenziamento dell'apprendimento formale. Italiano LinguaDue, 14(2), 255-273. https://doi.org/10.54103/2037-3597/19661
[2] Johns, T. (1991). Should you be persuaded: Two samples of data-driven learning materials. English Language Research Journal, 4, 1-16.

Keywords: Mobile-assisted language learning, Data-driven learning, secondary education, Italian, AI chatbot.