Personalized Learning Pathways with AI in Reader Response Theory: A Case Study of Laskar Pelangi by Andrea Hirata

Ekarini Saraswati

Authors

Ekarini Saraswati University of Muhammadiyah Malang

Keywords:

Personalized Learning Pathways (PLPs), Artificial Intelligence (AI), AI as Supporting Learning Tools, Reader Response Theory (RRT), Laskar Pelangi, Andrea Hirata

Abstract

Engaging students with Andrea Hirata's Laskar Pelangi poses challenges due to the diverse backgrounds and interpretive abilities of students when analyzing the novel's themes of education, poverty, and hope in Indonesia. This study aims to enhance student engagement and understanding by applying Personalized Learning Pathways (PLPs) enhanced by Artificial Intelligence (AI) within the framework of Reader Response Theory (RRT) to tailor learning experiences. The method employs AI-driven PLPs to assess students' prior knowledge, cultural backgrounds, and learning preferences, allowing for individualized content delivery. AI provides personalized feedback, guiding students through the novel's key themes—such as the importance of education and the characters' perseverance despite poverty—while encouraging them to reflect on their own experiences. By dynamically adjusting the learning path based on each student's progress, the AI fosters deeper connections between the novel's narrative and the students' personal contexts. The study concludes that integrating PLPs with AI significantly improves students' comprehension and engagement with Laskar Pelangi by creating an interactive, reflective learning environment. This personalized approach enables students to relate more closely to the novel's themes while also addressing challenges such as varying levels of reading proficiency. However, successful implementation depends on addressing cultural sensitivity, technology access, and ensuring a balanced integration of AI with teacher-led discussions.

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