Sep 2026· Educational Point· Vol 3, pp. e186· 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.
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· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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.· arXiv.org· 62 citations· ⚡3
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.· International Conference on...· 48 citations· ⚡4
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.· arXiv.org· 44 citations· ⚡2
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.· arXiv.org· 41 citations
Professor Ali Jadbabaie describes how AI, digital transformation, climate change, and complex geopolitics are forcing engineers and policymakers to rethink how infrastructure is designed, built, and managed.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.