Earticle

인공지능(AI)이 빠르게 보편화되고 있으며, 교육 뷴야에서도 큰 영향을 미치고 있다. 우리나라에서는 ML과 지 능형 교육 도구를 통합한 AI·디지털 기반 교육 플랫폼을 통해 학습자 중심의 맞춤형 교육을 지원하고 있다. AI·디 지털 교육 플랫폼에서의 교수 및 학습 과정에는 학습자의 개인 데이터가 포함되며, 이를 분석하여 개인화된 향상 된 학습 경험을 제공하기 위해 상당한 수의 메타데이터가 생성된다. 이렇게 생성된 메타데이터에 어떤 개인 데이 터가 포함되는지, 학습과 관련된 데이터에 대해 보호는 어떻게 이루어지는지가 주요한 이슈이다. 따라서 본 논문 에서는 2025년 도입되는 디지털 교과서가 AI 교육 플랫폼에서 학습자의 개인 데이터가 위협받지 않도록 기존 문 헌 분석을 토대로 학습자의 학습 데이터 보호 방안에 대해 제시하였다.

Artificial Intelligence (AI) is rapidly becoming ubiquitous and greatly impacts education. In Korea, an educational platform powered by AI supports learner-centered and personalized education. The platform integrates ML and intelligent educational tools. The teaching and learning process in this platform involves learners' data, and a significant amount of metadata is generated to be analysed in order to provide personalised and enhanced learning experiences. However, what personal data is included in this metadata and how to protect learning-related data is an issue. Therefore, based on the analysis of the existing literature, this paper proposes a plan to protect learners' learning data so that the digital textbooks introduced in 2025 do not pose a threat to learners' personal data in the AI education platform.

This study aims to present design principles for a next-generation learning management system (LMS) that reflects edutech trends and the higher education environment from 2025 to 2030, specifically for designing an agentic AI LMS based on generative AI. To achieve this, a review of prior research on Learning Management Systems (LMS), Large Language Models (LLM), Retrieval-Augmented Generation (RAG), LTI Advantage, and Learner Experience (LX) confirmed that the core requirements for next-generation LMS converge on personalized learning support, real-time feedback, learning analytics and prediction, and instructor task automation. Accordingly, we demonstrated an RAG-based architecture mitigating LLM hallucination and timeliness issues, an LTI Advantage-based hub structure for securely integrating external AI tools, and an intent-based LX design prototype minimizing cognitive load. Simulation evaluation showed that applying RAG v2 reduced the hallucination rate from 37.0% to 8.0% and improved the evidence provision rate to 97.2%. Furthermore, after LTI Advantage integration, the data loss rate decreased to 0.4%, and the interoperability index was evaluated at 0.89. The application of intent-based LX resulted in perceived usefulness of 4.62/5 and perceived ease of use of 4.48/5, confirming its effectiveness in improving the learning experience. In summary, the agentic LMS combining RAG LTI Advantage intent-based LX presents a practical design alternative capable of transforming existing management-centric LMSs into learner-centered, active learning environments.