Implementation of Adaptive Learning Based on Artificial Intelligence to Improve English Learning Personalization

Asep Nurul Aripin (Institut Pendidikan Indonesia Garut)
Dian Rahadian (Institut Pendidikan Indonesia Garut)
Iman Nasrulloh (Institut Pendidikan Indonesia Garut)

IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature
Vol. 13 No. 2 (2025): IDEAS: Journal on English Language Teaching and Learning, Linguistics and Lite

The diversity of proficiency levels, learning styles, and language acquisition pace among English as a Foreign Language (EFL) learners is often poorly accommodated by conventional one-size-fits-all teaching models. This article aims to examine the implementation of Artificial Intelligence (AI)-based adaptive learning in enhancing the personalization of English language learning, covering the system's working mechanisms, the cognitive foundations of its content design, its impact on language skills, and implementation challenges. This study employs a library research method with a descriptive qualitative approach, reviewing and synthesizing scientific articles on AI in English language learning, supported by a comparative analysis of two ADDIE-based instructional media development studies and the Cognitive Theory of Multimedia Learning framework. The results show that AI-based adaptive learning systems, through Natural Language Processing, Automatic Speech Recognition, and knowledge tracing, can adjust grammar, vocabulary, pronunciation, and writing materials according to learners' proficiency profiles in real time. A survey of 120 English teachers in Palembang reported that 78% of respondents considered AI supportive of personalized learning and 82% observed increased student engagement, while the needs-analysis principle in the ADDIE model applied to Google Sites-based e-learning development produced very high material validity (94.55%-95.00%) and "very good" student responses (88-94%). The discussion indicates that AI-driven content personalization can be pedagogically strengthened when aligned with cognitive load management principles (extraneous, essential, and generative processing) as emphasized by Cognitive Load Theory, so that adaptive materials are not only algorithmically efficient but also effective in building learners' integrated mental models. This study concludes that AI-based adaptive learning effectively enhances the personalization of English language learning when balanced with cognitive multimedia design principles and the teacher's pedagogical role.