Integrating Artificial Intelligence (AI) into language learning has substantially enhanced accessibility and engagement by offering interactive and adaptive experiences. However, many AI-driven chatbots lack contextual relevance, limiting their effectiveness for vocational high school students who require career-oriented language instruction. This study aims to develop and evaluate EduNetPro (Education Network for Professional Learning), a multilingual AI-driven chatbot designed to enhance French language learning among vocational students by incorporating adaptive learning pathways, interactive exercises, and gamification elements. Adopting a Design-Based Research (DBR) approach, this study employs a mixed-methods approach, combining quantitative assessments (pre-test and post-test evaluations) with qualitative analyses (student interviews and chatbot interaction logs) to assess EduNetPro’s effectiveness. The findings indicate a 24% improvement in language proficiency, with 80% of students reporting increased engagement and 90% highlighting the efficacy of real-time feedback. Despite these positive outcomes, the chatbot encountered challenges in processing complex dialogues, particularly those involving idiomatic expressions and context-dependent language, emphasizing the need for advancements in Natural Language Processing (NLP) models. This study contributes to the ongoing discourse on AI in education, demonstrating how chatbot-driven learning can address the unique needs of vocational students by providing contextualized, career-focused language instruction. Future research should further explore AI-driven emotional intelligence, enhanced personalization strategies, and long-term language retention to optimize AI-assisted learning environments. © 2025 by the authors.
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