AI-Based Education Policies in Indonesia: Infrastructure Gaps and Governance Misalignment
Abstract
This systematic review analyzes the translation of national AI-based education policies into local practice across Indonesia. Synthesizing 50 empirical studies, the findings reveal that ambitious national policies frequently fail to materialize locally due to acute infrastructure deficits, insufficient teacher readiness, and systemic governance misalignment. The digital divide between urban and rural regions exacerbates these inequalities, while ethical concerns regarding data privacy and cultural relevance further complicate adoption. The review emphasizes the urgent need for targeted infrastructure investments, scalable professional development, and adaptive central-local governance frameworks to ensure equitable AI integration in Indonesian education.
1. Introduction
AI-based education policies in Indonesia promise transformative benefits, including personalized learning and improved educational management. However, their translation into practice at the local government level faces persistent barriers. Key challenges include technological disparities between urban and rural areas, insufficient digital infrastructure, lack of teacher training, policy fragmentation, and governance misalignment between central and local authorities. These issues are compounded by ethical concerns such as data privacy and algorithmic bias, as well as limited institutional capacity and resource support at the local level (Haetami, 2025; Raharjo & Rohmadi, 2025; Wadipalapa et al., 2024; Nurhaliza, 2025; Indriyani & Solihati, 2021; Herviana, 2025; Asadoma et al., 2025; Prihatin, 2025; Fauziddin et al., 2025).
The literature consistently highlights that without targeted investments in infrastructure, comprehensive teacher capacity-building, adaptive governance frameworks, and culturally responsive strategies, AI policies risk exacerbating existing educational inequalities rather than bridging them (Wadipalapa et al., 2024; Indriyani & Solihati, 2021; Asadoma et al., 2025; Prihatin, 2025).
2. Methods
A rigorous and comprehensive systematic search was conducted across multiple major scientific databases, including Semantic Scholar and PubMed. The initial search yielded a vast corpus of over two million potential records. Following robust relevance filtering and deduplication, the retained manuscripts were screened for eligibility based on predefined inclusion criteria targeting foundational context, barriers, terminology variations, comparative cases, adjacent constructs (e.g., digitalization), and breakdowns by education level. Ultimately, 50 peer-reviewed studies were included in the final synthesis. Six unique search strategies were deployed to capture both broad trends and specific barriers to AI policy implementation across Indonesian education sectors.
3. Results
**Infrastructure Gaps and Digital Divide** A recurring theme is the stark disparity in digital infrastructure between urban centers and rural or remote regions. Many schools outside major cities lack basic facilities such as computers or stable internet connections (Nurhaliza, 2025; Indriyani & Solihati, 2021; Herviana, 2025; Asadoma et al., 2025; Prihatin, 2025; Fauziddin et al., 2025). Only about 30% of Indonesian schools have adequate internet access for technology-based learning (Fauziddin et al., 2025), with rural areas particularly disadvantaged (Nurhaliza, 2025; Indriyani & Solihati, 2021). This digital divide severely limits equitable access to AI-driven educational tools.
**Teacher Training Deficits and Institutional Readiness** Teacher readiness is a critical bottleneck: most educators lack sufficient training in both technical and pedagogical aspects of AI integration (Herviana, 2025; Prihatin, 2025; Lubis et al., 2024; Rissi & Sinaga, 2025; Subiyantoro & Musa, 2024; Ardho & Permana, 2025). Studies show that while teachers are enthusiastic about AI’s potential, their confidence and skills remain moderate due to limited professional development opportunities—especially outside urban areas (Subiyantoro & Musa, 2024; Ardho & Permana, 2025). Institutional support for ongoing training is often weak or inconsistent.
**Governance Misalignment and Policy Fragmentation** Indonesia’s decentralized governance structure complicates policy translation. Centralized decision-making often clashes with local autonomy; regulatory dualism (between the Ministry of Education & Culture vs. Ministry of Religion) leads to fragmented implementation (Wadipalapa et al., 2024; Thoha et al., 2023). Local governments frequently lack resources or clear mandates to adapt national AI initiatives to their unique contexts (Wadipalapa et al., 2024). Weak coordination between central and regional authorities results in partial adoption or symbolic compliance rather than substantive change (Asadoma et al., 2025).
**Ethical Concerns and Cultural Context** Ethical issues—such as data privacy vulnerabilities, algorithmic bias favoring urban populations, and lack of culturally contextualized content—further hinder effective implementation (Raharjo & Rohmadi, 2025; Waita et al., 2025; Budi et al., 2024). There is also concern that Western-centric AI models may not align with Indonesian values or linguistic diversity (Maspul et al., 2025).
4. Discussion
The evidence demonstrates that while Indonesia’s national ambitions for AI-driven educational transformation are strong, practical realization at the local level is hampered by systemic issues: infrastructure deficits remain acute outside urban centers; teacher training programs are insufficiently scaled; governance structures create confusion rather than clarity; and ethical/cultural considerations are often overlooked (Haetami, 2025; Raharjo & Rohmadi, 2025; Wadipalapa et al., 2024; Indriyani & Solihati, 2021; Herviana, 2025; Asadoma et al., 2025). These challenges are not isolated but deeply interconnected—addressing one without the others yields only partial progress.
The quality of research is robust: multiple studies use mixed-methods approaches (surveys, interviews, case studies) across diverse regions and stakeholder groups (Herviana, 2025; Asadoma et al., 2025; Subiyantoro & Musa, 2024). However, there is a notable gap in longitudinal empirical studies tracking long-term outcomes post-policy implementation.
5. Conclusion
In summary, the failure of AI-based education policies to translate into practice at Indonesia’s local government level stems from intertwined challenges: inadequate infrastructure (especially outside cities), insufficient teacher preparation/support systems, fragmented governance structures due to decentralization/regulatory dualism, persistent regional inequalities, and unresolved ethical/cultural issues. Addressing Indonesia’s persistent gap between ambitious national policies and practical local implementation requires coordinated investment in infrastructure, robust teacher development systems, adaptive governance reforms, ethical safeguards tailored to cultural context—and above all—a commitment to equity across all regions.
Claims & Evidence
Infrastructure gaps are a primary barrier to local AI policy adoption
Consistently cited as a limiting factor across all regions; supported by quantitative data on school access.
Teacher training deficits impede effective implementation
High prevalence of untrained teachers; qualitative/quantitative evidence from multiple provinces.
Governance misalignment causes policy-practice gaps
Decentralization leads to fragmented authority; documented through case studies and qualitative interviews.
Regional inequalities exacerbate the digital divide
Urban-rural disparities repeatedly documented; directly impacts equitable access and learning outcomes.
National policies lack operational clarity for local adaptation
Policy documents reviewed show an absence of detailed, operational guidelines for local governments.
Ethical and data privacy concerns slow adoption
Frequently mentioned in theoretical discussions but less rigorously quantified; recognized as a growing strategic issue.
Research Gaps
The matrix below shows where empirical evidence is concentrated and where critical research gaps remain.
| Topic / Outcome | Urban Schools | Rural Schools | Teacher Training Programs | Policy Gen Studies |
|---|---|---|---|---|
| Infrastructure Access | 12 | 14 | 8 | 11 |
| Teacher Readiness | 9 | 11 | 13 | 8 |
| Student Outcomes | 5 | 4 | GAP | 2 |
| Data Privacy/Ethics | 4 | 2 | GAP | 1 |
Open Research Questions
How do region-specific infrastructure investments affect equitable AI adoption in Indonesian schools?
Understanding localized impacts can inform targeted interventions that bridge urban-rural divides more effectively.
What long-term effects do comprehensive teacher training programs have on successful AI integration?
Evaluating sustained outcomes will help design scalable professional development models tailored for remote and distinct contexts.
How can national policy frameworks be adapted for effective local-level implementation?
Identifying best practices for aligning central guidance with local government realities will reduce fragmentation.
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