With the rapid influx of various edutechs due to the recent acceleration of digital transformation in the educational field, the central axis of instruction is moving to the digital basis. In line with these changes, the introduction of the high schoo...
With the rapid influx of various edutechs due to the recent acceleration of digital transformation in the educational field, the central axis of instruction is moving to the digital basis. In line with these changes, the introduction of the high school credit system requires fundamental innovation in teaching and learning strategies. This means that it requires a fundamental reflection on how to design and evaluate classes, beyond simply changing the range of achievement standards and content elements to be learned.
In science subjects, the need for change is particularly prominent, and data analysis and inquiry-oriented classes based on real phenomena are required. Consequently, the potential of advanced technologies such as virtual reality (VR) and generative artificial intelligence (AI) is receiving considerable interest.
On the other hand, in the case of geoscience subjects, one of the high school elective subjects, instructional programs incorporating VR technology are constantly being researched and developed due to the nature of subjects dealing with natural phenomena. However, in the case of previously developed programs, most studies mainly focus on changes in learners' affective characteristics, and studies on the effect on changes in conceptual understanding or self-directed learning capabilities are relatively insufficient. To overcome these limitations, this study attempted to develop a program that combines VR-based virtual geology exploration and generative artificial intelligence technology and apply it to high school students to verify its effectiveness. In particular, the focus was on overcoming the existing static environment of instructional environment and analyzing learners' conceptual understanding, self-directed learning attitude, and impact on affective response.
The study was conducted with a total of 56 students who voluntarily participated in a general boys' high school A located in Seoul. A pre- and post-question survey was conducted on the study participants, and the objective learning effect was measured and various factors such as program satisfaction and need improvement were analyzed through open-ended questions.
As a result of the study, the program was found to exert positive influence on learners' conceptual understanding and self-directed learning competencies.
In the case of conceptual understanding, it played a role in helping to internalize the learning content more deeply by actively reconstructing the concept acquired based on the explanation of the textbook or the static image through virtual survey activities. Although there was no statistically significant change in self-directed learning competency, a slight improvement was confirmed, and learners voluntarily created questions and showed an attitude to expand knowledge. In addition, overall positive responses are generally seen in affective areas such as interest and satisfaction, showing that this program is effective in inducing learning motivation and enhancing immersion in learning.
However, since this study is limited to general male high school students, supplementary and follow-up studies are needed to apply it to various regions and school levels. In addition, it is difficult to verify the long-term sustainability effect of learners' conceptual understanding or self-directed learning competency with a short-term program. The educational effect of the program leads to changes in learners' behavior, and further research is needed to confirm whether the effect is maintained over time, and additional program composition is required. Nevertheless, this study shows that classes that combine Generative AI technology and VR-based virtual geological surveys can have a positive effect on improving high school students' understanding of concepts and learning motivation, and it can be used as basic data for the development and field application of a convergence class model that combines AI and VR in the future.