Enhancing Inclusivity in Swedish ESL Classrooms: Integrating Generative Artificial Intelligence for Personalised Learning

Abstract

This study investigates the impact of Generative Artificial Intelligence (GAI), specifically ChatGPT, on personalised grammar instruction in English as a Second Language (ESL) settings. Using a 2x2 factorial design and a mixed-methods approach, the research examines how two dimensions of personalisation—content (based on language proficiency) and topic (based on learner interests)—affect student motivation, engagement, and perceived task suitability. A sample of 140 Swedish students was divided into four experimental groups to explore the independent and combined effects of these personalisation strategies. Results from MANOVA and observational data show that combining content and topic personalisation significantly enhances motivation and task completion rates, supporting the theoretical basis in Self-Determination Theory (SDT). The study introduces the Personalization-Motivation Integration Framework (PMIF), which conceptualizes how relevance and autonomy jointly drive engagement in AI-mediated learning. These findings suggest that GAI tools can play a pivotal role in inclusive, individualized education by aligning instructional content with learner needs and interests.

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