KJCKorea
Journal Central

This study aims to develop a video DB server construction and management system for autonomous driving technology development. In order to verify the artificial intelligence (AI) performance of autonomous vehicles, it is essential to build a large-capacity video DB, and for this purpose, a system that systematically stores and manages data collected in various driving environments is required. We analyzed cases of DB construction using various video data such as ㅡmoving object detection (MOD), free space detection (FSD), parking line recognition, and front vehicle recognition (VD), and developed an effective DB server and management system. In addition, we established a plan to verify the reliability of the AI algorithm by introducing performance evaluation equipment based on HILS (Hardware In the Loop Simulation) in the future. In order to supplement actual driving data, we propose a method to improve the safety and reliability of autonomous driving AI by building a virtual DB using simulation tools such as PreScan. As a result, the developed video DB server and management system are expected to be utilized as an important basic infrastructure for autonomous driving technology development and verification.

This study was conducted to explore the needs of field teachers in order to explore the factors that should be considered in organizing a curriculum to strengthen AI recurrence of prospective early childhood teachers. For this purpose, the research method used the FGI technique. Three areas and three questions were organized through literature analysis. The FGI target was five teachers at early childhood education institutions with three to six years of field experience. The researchers and five field teachers conducted FGI on three items, and the researchers and five teachers extracted the meaning of the discourse produced through FGI together. As a result, a total of 12 meanings were extracted, including four meanings of the sufficiency of university education, five meanings of important teacher competencies in the field, and three meanings of AI-related competencies. Based on the extracted meanings, the curriculum organization direction was presented as the actual subjects of lesson planning and operation utilizing various play topics, interpersonal skills (infant and parent), community awareness and communication methods, basic AI subjects, AI and class design, and AI-based education.

This study aimed to examine the effects of an AI feedback–based digital writing program. Specifically, we statistically verified changes in participants’ digital writing competencies in an AI-based extracurricular program at A University and conducted an in-depth exploration of AI feedback experiences through reflective journal analysis. Over a seven-week period, learners drafted texts based on weekly prompts and repeatedly revised them using immediate AI feedback. The results showed significant improvements in areas such as digital information searching and utilization, writing planning and organization, and document revision. Notably, learners’ writing self-efficacy and interest increased through the gradual refinement of their work. However, their beliefs about digital writing did not undergo major changes in the short term, and limitations arose from AI feedback’s insufficient consideration of creative and emotional aspects, as well as contextual nuances. These findings suggest the importance of maximizing the benefits of AI feedback while also supplementing it with instructor or peer feedback to address contextual and affective elements. Furthermore, instructional designs should train learners to selectively accept or reject AI feedback according to writing genre and individual characteristics. This study holds significance in empirically demonstrating that AI-based writing instruction can effectively enhance learners’ writing performance and motivation in actual educational settings.