Innovative Approaches in Foreign Language Learning Using AI and Project-Based Education

Касимова (Мулладжанова) , Насиба

Lingvospektr · 2025-yil

Annotatsiya

The current situation of foreign language acquisition and the problems existing in traditional pedagogical models are proposed to achieve higher learner engagement, improved language retention, and adaptive feedback mechanisms. This study aimed at opening new pathways for AI-assisted linguistic competency development that affects curriculum design and instructional methodologies and putting forward corresponding policy recommendations and technological advancements. In this study, the Analytical Hierarchy Process (AHP) regression method in AI-assisted foreign language education combines the project-based learning (PBL) approach based on collaborative problem-solving, real-world application, and digital assessment tools. Based on the innovative design method of AI-driven adaptive learning, the extracted feature representations from student interactions are used to build a more accurate predictive learning performance model. The research results show that the constructed model can systematically analyze language proficiency development trends, and AI-supported language learning is more efficient, more learner-centric, and more scalable to diverse educational contexts.

Maqola ma’lumotlari
MualliflarКасимова (Мулладжанова) , Насиба
JurnalLingvospektr
Nashr sanasi2025-05-02
Jild4
Son1
Betlar982-991
TilRus

Kalit so‘zlar

Обучение иностранным языкам с поддержкой ИИ, Адаптивные технологии обучения, Проектное обучение языкам, Оценка на основе машинного обучения, Оптимизация с помощью обучения с подкреплением, Контекстно-осознанная языковая компетенция, Sun’iy intellekt yordamidagi chet tilini o‘rganish, Moslanuvchan o‘qitish texnologiyalari, Loyiha asosidagi til ta’limi, Mashinaviy o‘rganishga asoslangan baholash, Mustahkamlovchi o‘rganishni optimallashtirish, Kontekstga mos til kompetensiyasi, AI-assisted foreign language learning, Adaptive learning technologies, Project-based language education, Machine learning-driven assessment, Reinforcement learning optimization, Context-aware linguistic proficiency

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