AI 기반 과정 중심 평가 체제 개발을 위한 국어과 지식 맵 구성 방안

Abstract

In this study, we applied CASE standards to the functional areas (listening, speaking, reading, and writing) of the 2015 revised Korean language curriculum to build a knowledge map to be used in an AI-based learning and assessment system, and explored the issues and solutions that emerged in the process. In the process of building knowledge maps, we identified issues such as redefining knowledge maps in the functional areas of national language and functional areas that emphasise procedural knowledge, differentiating between skill-based and knowledge-based achievement standards, and developing knowledge nodes for definitional achievement standards. The implications of this study are as follows. First, when designing knowledge maps, it is important to clearly distinguish between procedural and conceptual knowledge and systematically reflect them in the development of knowledge maps. Second, it is necessary to analyse the specific teaching content contained in the achievement standard explanations and textbooks and reflect it in the knowledge map to enable more realistic and sophisticated learning diagnosis. Third, in terms of the depth of knowledge nodes, it is necessary to develop the lowest node and then adjust it according to the learner's level to build a personalised learning assessment system.