DEVELOPING ADAPTIVE LEARNING STRATEGIES USING ARTIFICIAL INTELLIGENCE FOR STUDENTS FACING CHALLENGES IN LEARNING ENGLISH

Аннотация

This research explores the effective use of Artificial Intelligence in crafting customized learning strategies aimed at students who regularly face difficulties in mastering the English language. As AI-enhanced educational tools continue to develop, the potential for delivering personalized instruction has greatly improved. The research explores key obstacles encountered by such learners, analyzes current AI-based educational interventions, and introduces a flexible model for implementing AI-driven adaptive methods tailored to individual learning profiles. The results indicate that AI significantly contributes to enhancing English learning outcomes through individualized support, prompt responses, and continuous encouragement. Keywords: Artificial Intelligence in Education, Adaptive Learning, English Language Acquisition, AI-powered Language Tools, Learner Motivation, Personalized Instruction, NLP in ELT, Data-Driven Language Learning, Learner Autonomy, Feedback Systems. Introduction. In today’s rapidly evolving digital landscape, Artificial Intelligence (AI) is reshaping educational practices across the globe. Among its most impactful uses is the creation of adaptive learning systems, particularly in English language instruction. Although numerous English learning materials are available, many students still struggle due to limited language exposure, diverse learning preferences, low confidence, or cognitive obstacles. This thesis investigates how AI tools can assist these learners by offering customized solutions aligned with their personal learning profiles and objectives. Challenges Faced by Students in Learning English. English language learners often face multiple barriers, especially when traditional instruction fails to accommodate their specific learning needs. Some of them: Lack of Motivation: One of the fundamental psychological factors influencing the success of English language learners is motivation. When learners are not sufficiently motivated—either intrinsically or extrinsically—their willingness to engage with the language meaningfully tends to decline. According to Dörnyei (2001), lack of motivation can severely hinder classroom participation, persistence, and progress, especially in environments that fail to stimulate curiosity or offer personalized support. Language Anxiety: Fear of making mistakes hinders active participation. Limited Vocabulary and Grammar Retention: “Traditional memorization methods may not suit all learners. Traditional memorization strategies, while still prevalent in many English classrooms, often fail to promote deep learning or long-term retention, especially when disconnected from meaningful context and communicative use” (Nation, 2001, p. 96). Ineffective Feedback: One of the persistent barriers in language education arises from the delivery of poorly constructed feedback, which may be too general, delayed, or disconnected 1 71 International scientific journal ISSN: 2181-3906 «MODERN SCIENCE АND RESEARCH» 2025 VOLUME 4 / ISSUE 8 / UIF:8.2 / MODERNSCIENCE.UZ from the learner’s specific performance. As noted by Shute (2008), feedback that is vague or delayed can be ineffective and may even negatively impact learners’ motivation and progress. Delayed or generic feedback instead of constructive one can fail to address learners’ actual errors. Time Constraints: Many students struggle to balance language learning with other responsibilities. These challenges necessitate a more individualized and dynamic educational approach. Role of Artificial Intelligence in Adaptive Learning. AI offers dynamic solutions by adapting teaching content and strategies based on learner behavior and performance. Major contributions of AI to language education include: Natural Language Processing (NLP): Tools like Grammarly and ChatGPT offer grammar correction and conversational practice. NLP-based tools, as Pérez-Marín and Pascual- Nieto (2011) note, allow learners to receive immediate and context-aware language corrections, enhancing their grammatical and semantic competence. Machine Learning: AI algorithms identify patterns in learner responses to personalize lessons. Speech Recognition and Feedback: Apps like ELSA Speak improve pronunciation by providing instant correction. Chatbots and Virtual Tutors: These provide 24/7 interaction and guidance. Learning Analytics: AI systems track progress and suggest next steps based on performance metrics. Data-driven learning: Recent advancements in AI-powered educational tools have also enabled learners to engage with corpora-based resources, providing authentic language input and data-driven insights for developing lexical and grammatical competence (Green, 2018). Beyond conventional AI applications like virtual tutors and NLP-enhanced feedback systems, corpora- based learning introduces a powerful, data-rich dimension to language acquisition. Artificial Intelligence can be trained to extract and present contextually relevant corpus data based