Skip to content

Explaining Cross-National Variations in AI Policy Enactment: Indonesia, Finland, and South Korea

Sep 2026 · Educational Point · 25 references
Global Educational Policies and Reforms

Abstract

The development of artificial intelligence in education has outpaced the institutionalisation of policies governing its use, accountability, and implementation. Cross-national scholarship remains dominated by discussions of adoption, pedagogical opportunities, and ethical risks, while variation in policy enactment across national education systems remains underexplained. This study examines artificial intelligence policy enactment in Indonesia, Finland, and South Korea through a comparative qualitative policy analysis of 22 core and supporting policy documents. The analysis focused on policy orientation, the locus of enactment authority, regulatory-ethical institutionalisation, and implementation mode. The findings show that the main differences across the three systems do not lie in technology adoption itself, but in the logic through which policy is enacted. Indonesia demonstrates capacity-led enactment shaped by gradual implementation and teacher capacity building. Finland demonstrates regulatory-literacy enactment mediated by education providers and structured through regulation, ethics, and AI literacy. South Korea demonstrates scaled rollout enactment driven by state direction, infrastructure support, and personalised learning reform. The study proposes a three-part typology comprising capacity-led enactment, regulatory-literacy enactment, and scaled rollout enactment, and argues that cross-national variation in artificial intelligence policy in education is best explained through the arrangement of authority, regulatory-ethical institutionalisation, and implementation instruments.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.