With the recent rapid advancement of artificial intelligence (AI) technology, the field of
education is undergoing a major digital transformation. Furthermore, ahead of the introduction
of AI digital textbooks in 2025, the establishment of personalize...
With the recent rapid advancement of artificial intelligence (AI) technology, the field of
education is undergoing a major digital transformation. Furthermore, ahead of the introduction
of AI digital textbooks in 2025, the establishment of personalized educational environments is
accelerating. However, dance education, classified as the ‘Expression Area’ within the current
middle school physical education curriculum, fails to fully realize its intrinsic artistic value and
educational effects due to a lack of teacher expertise, simple activity-oriented class operations,
and ambiguity in evaluation.
Accordingly, this study aims to restructure the ‘Expression Area’ of the 2022 Revised
Physical Education Curriculum by applying Wiggins & McTighe’s Backward Design
principles. Based on this restructuring, the study seeks to develop an ‘AI Digital Textbook
Teaching-Learning Model for Dance Education.’
To achieve this, the study analyzed the domestic and international status of AI digital
textbook implementation and the current state of the Expression Area in middle school
physical education textbooks through a literature review. Additionally, surveys and in-depth
interviews were conducted with ten in-service physical education teachers to identify
difficulties in dance classes and requirements for digital textbooks. Based on these findings,
the ‘AI Digital Textbook Teaching-Learning Model for Dance Education’ was designed by
integrating the stepwise procedures of Backward Design with the PATROL model, a digital
textbook design framework. The final model was confirmed after verifying its suitability and
field applicability through validation by an expert group.
The results of this study are as follows: First, to address the problems of current dance
classes, a class system centered on learners’ understanding and evidence of performance was
established by applying Backward Design, in which objectives, assessments, and activities are
consistently aligned. Second, an AI-based personalized learning environment was implemented
by combining WHERETO, a learning content planning element of Backward Design, with the
PATROL model. In the developed model, AI functions as an artistic stimulator, a facilitator of
creative expression, and a supporter of reflection and collaboration, designed to provide
individualized feedback and data tailored to the learner’s level and competence. Third, expert
validity verification results showed that the model has high suitability in terms of the
connectivity between goals and activities, as well as the validity of the educational application
of AI technology. The prototype produced based on this model was evaluated to increase
accessibility to dance classes and enable systematic guidance.
This study is significant in that it presents a practical model capable of overcoming the
structural limitations experienced by classes in the ‘Expression Area’ of physical education and
promoting the qualitative improvement of dance education through AI technology. In
particular, it established that AI serves not as a replacement for dance, but as a companion
that expands physical experiences and artistic expression. This will serve as crucial
foundational data for dance education to build a new educational paradigm suitable for the
digital age and to demonstrate its academic validity. When supported by continuous field
application and follow-up research, expression-centered subjects, including dance education,
will solidify their status as core subjects in future school education