Mathematical Resilience in AI-Assisted Mathematics Learning Among Junior High School Students - Mesrah Mesrah; Sukmawati Sukmawati; Andi Husniati (Jurnal Pendidikan Terapan, 2026)
Purpose: This study aims to explore students’ mathematical resilience in overcoming learning barriers through AI-assisted mathematics learning at SMP Negeri 2 Tombolopao. Methods: This study employed a qualitative case study design involving junior high school students with varying levels of mathematical ability. Data were collected through semi-structured interviews, classroom observations, field notes, and documentation of students’ interactions with Artificial Intelligence-based learning systems. The data were analyzed using thematic analysis through coding, categorization, and theme identification to examine learning barriers, adaptive strategies, and the role of Artificial Intelligence in supporting mathematical resilience. Findings: The findings revealed that students experienced multidimensional learning barriers, including difficulties in conceptual understanding, mathematics anxiety, low self-confidence, and weak self-regulation. AI-assisted learning supported students by providing immediate feedback, flexible learning opportunities, and emotionally safe environments that encouraged persistence, reflective thinking, and adaptive problem-solving strategies. Students demonstrated increased confidence and willingness to retry mathematical tasks after interacting with Artificial Intelligence-based learning support. Research Implications: This study contributes theoretically to the development of mathematical resilience in digital learning contexts and provides practical implications for integrating Artificial Intelligence into mathematics instruction to support adaptive and student-centered learning at the junior high school level.