Teachers’ Lived Experiences and Readiness for Coding and AI Education: A Narrative Case Study from South Kalimantan
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Coding and AI, Competency Standards, Capacity Building, Patern Analysis, Teacher Readiness
Abstract
Background. The Indonesian government’s policy to introduce coding and artificial intelligence (AI) learning from primary to vocational education has raised critical questions about teacher readiness, particularly in regions outside Java where this dimension is still underexplored.
Purpose. This study aims to: (1) map competency standards for teaching coding and AI; (2) analyze teachers competencies readiness; and (3) formulate contextual strategies for teacher capacity building.
Method. Using a mixed method, this study involved six teachers from elementary, junior high, and vocational schools in South Kalimantan that have been appointed to implement coding and AI learning. Data were collected through in-depth interviews and questionnaires, then analyzed using the pattern matching analysis model of Vargas and the interactive model of Miles, Huberman, and Saldana.
Results. Three key findings emerged: first, competency standards validated by experts. Second, teacher readiness was identified at an average of 74.24%, falling into the "ready" category, with the lowest achievement in professional competency at an average of 67.14%. Pattern matching analysis revealed two significant relationships: personal and social competency areas, which showed consistent correlations across all educational levels, drawing on interview data, indicating that teachers’ ethical motivation drives collaborative initiatives; and second, pedagogical and professional competency areas, which were identified as moving in tandem, indicating that subject matter mastery directly impacts the ability to design instruction. Based on this analysis, the third findings of this study yielded a three-stage teacher capacity development strategy: initial project-based training, ongoing development through professional learning communities, and ongoing mentoring.
Conclusion. The primary contribution of this study lies in providing an empirically grounded and contextualized strategic framework for enhancing the capacity of coding and AI teachers also to the discourse on teacher readiness for digital transformation in education, particularly in underrepresented regions such as South Kalimantan
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Copyright (c) 2026 Susanti Sufyadi, Sulistyo Rini, Lazaro Kumala Dewi, Adinda Sadilla, Muhammad Afriandy
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