Interdisciplinary Plugged-Unplugged Training Successfully Boosts Early Childhood Teachers' Coding and AI Competence

Ilustration by AI 

FORMOSA NEWS

A new study from Universitas Negeri Makassar demonstrates that a professional development program using an interdisciplinary plugged-unplugged approach has successfully enhanced early childhood education (ECE) teachers' competence in coding and Artificial Intelligence (AI) by over 100 percent.

Published in July 2026 in the Jurnal Multidisiplin Madani (MUDIMA), the research was conducted by Rika Kurnia R, Hajerah, Andi Nur Maharani Islami, and Ahmad Syawaluddin. As digital technologies rapidly reshape global education, these findings prove that AI literacy and coding can be effectively mastered by early childhood teachers through a practical, flexible model that does not depend heavily on expensive computational devices.

Addressing the Need for Teacher Readiness in Coding and AI

Global educational trends increasingly demand the integration of computational thinking and foundational AI concepts from the earliest stages of formal schooling. Young children are naturally curious, highly receptive, and eager to explore the world around them. However, many early childhood educators in Indonesia still face significant challenges, including a lack of formal training, low confidence, and limited technical knowledge to teach these concepts effectively.

The plugged-unplugged framework offers a practical solution to these infrastructure and training challenges. By combining screen-based ("plugged") activities with screen-free ("unplugged") tasks, this interdisciplinary approach seamlessly embeds coding and AI concepts into existing curriculum domains, such as language, mathematics, science, and social-emotional development.

Research Methodology: A Three-Week Training Model in Makassar

This pre-experimental study (One-Group Pretest-Posttest) was conducted in Makassar, South Sulawesi, involving 14 early childhood teachers across seven schools who had no prior formal training in coding or AI.

The professional development program spanned three weeks, totaling 18 instructional hours. The curriculum covered four core thematic areas: fundamental coding and computational thinking concepts, AI literacy for ECE, plugged implementation with interactive software, and unplugged activity design. Teacher competence was evaluated before and after the intervention using a validated observational rubric across four key domains:

1. Conceptual Understanding: Knowledge of basic coding and AI concepts.
2. Pedagogical Integration: Ability to connect coding and AI to the early childhood curriculum.
3. Practical Implementation: Skill in facilitating both plugged and unplugged learning tasks.
4. Reflective Practice: Capacity to evaluate and adapt activities based on observed learning outcomes.

Data were statistically analyzed using the non-parametric Wilcoxon Signed-Rank Test.

Key Findings: Teacher Competence Scores More Than Double

Data analysis revealed statistically significant and substantial gains in teacher competence across all four evaluated domains.

Key takeaways from the research findings include:

• Composite Score Increase: The mean composite score for teacher competence surged from 1.43 (baseline/low) at the pre-test to 2.89 (developing/proficient) at the post-test, representing an overall increase of 102.1%.
• Domain-Specific Growth: Practical Implementation registered the largest gain at 115.2% (from 1.45 to 3.12), followed by Pedagogical Integration at 108.6% (from 1.39 to 2.90), Conceptual Understanding at 102.1% (from 1.41 to 2.85), and Reflective Practice at 93.2% (from 1.48 to 2.86).
• Statistical Significance and Effect Size: The Wilcoxon Signed-Rank Test yielded a result of $p = .001$ ($p < .05$) with a calculated effect size of $r = .882$. This falls into the "very large" effect size category, confirming meaningful improvement across all participating teachers.

Educational Implications and Public Policy Impact

This research demonstrates that an interdisciplinary plugged-unplugged training model provides a highly effective and scalable solution for early childhood education, particularly in resource-constrained environments. It enables schools with limited hardware to introduce digital competencies without financial strain.

Starting with hands-on, screen-free (unplugged) activities allows educators to build conceptual understanding and confidence before moving to screen-based (plugged) applications. This sequence effectively reduces technological anxiety among ECE teachers. The study serves as a valuable framework for education authorities, teacher training colleges, and policy makers seeking to incorporate digital literacy into national teacher training programs.

Author Profiles

This study was authored by an academic team from Universitas Negeri Makassar, Indonesia:

• Dr. Rika Kurnia R, S.Pd., M.Pd. (Corresponding Author) – Senior lecturer and researcher in Early Childhood Education and Educational Technology, Universitas Negeri Makassar (Email: rika.kurnia@unm.ac.id).
• Dr. Hajerah, S.Pd., M.Pd. – Lecturer and researcher in the Early Childhood Education Department, Universitas Negeri Makassar.
• Andi Nur Maharani Islami, S.Pd., M.Pd. – Researcher and lecturer specializing in digital learning media and ECE, Universitas Negeri Makassar.
• Dr. Ahmad Syawaluddin, S.Pd., M.Pd. – Senior lecturer and expert in educational technology, Universitas Negeri Makassar.

Research Source

Article Title: Enhancing Teacher Competence in Coding and Artificial Intelligence Through an Interdisciplinary Plugged-Unplugged Approach
Journal: Jurnal Multidisiplin Madani (MUDIMA)
Publication Year: 2026 (Vol. 6, No. 7, pp. 969–974)
DOI: https://doi.org/10.55927/mudima.v6i7.95

URL : https://journalmudima.my.id/index.php/mudima