Strengthening Elementary School Teachers’ Digital Competence through AI-Based Instructional Design Training in Indonesia’s 3T Regions

Abstract

This quantitative study investigates the effectiveness of an artificial intelligence (AI)-based instructional design training program in strengthening the digital competence of elementary school teachers in Indonesia’s frontier, outermost, and underdeveloped (3T) regions. Education quality and access in 3T regions remain constrained by geographical isolation, limited infrastructure, and uneven professional development opportunities for teachers. Building on the European Digital Competence Framework for Educators (DigCompEdu) and its six areas of educator digital competence, the study designed a structured training program that introduced teachers to generative AI tools for lesson planning, resource development, and assessment. A one-group pretest–posttest design was implemented with 120 elementary school teachers from several 3T districts. Data were collected using a Likert-scale digital competence questionnaire adapted from DigCompEdu and analyzed with descriptive statistics and paired-sample t-tests. Findings show statistically significant improvements in teachers’ overall digital competence scores across all six DigCompEdu areas, with the largest gains in “Teaching and learning” and “Facilitating learners’ digital competence.” The results indicate that contextually grounded AI-based instructional design training can be an effective strategy to narrow digital competence gaps between teachers in peripheral and non-peripheral regions. Implications for policy, teacher professional development, and further research on AI-supported pedagogy in disadvantaged contexts are discussed.