Cycle tourism is increasingly recognized as a means of regional revitalization worldwide. To support this trend, planning suitable cycling routes is essential. Especially, routes considering narrative, i.e., defined as the thematic and emotional coherence that links tourist spots into a meaningful story, are preferred by cycle tourists. However, traditional methods prioritize only physical intensity and route distances. To address this gap, we propose an AI-based system that generates narrative-rich cycling routes. Our proposed system gives the emotional features extracted from Points of Interest as narrative quality. Our evaluation revealed that our approach improved the narrative-aware route quality from 1.561 to 1.726 (increased by 10.6%).

As generative AI (GenAI) becomes ubiquitous in education, clarifying how learners and educators perceive, and co-design technology is a pressing challenge. This study involved a one-day participatory ideathon in Japan, with nine pre-service English teachers and six high school students co-creating English lesson ideas that integrate GenAI and textbook-based instruction. Using the Keep-Problem-Try framework, participants submitted one hundred sixty-one reflective sticky notes and fifty-five unique lesson proposals. Qualitative analysis was conducted using open and axial coding to identify thematic categories, while the quantitative analysis applied a rubric-based evaluation by GPT-4o across three dimensions: innovativeness, feasibility, and pedagogical alignment, followed by Mann-Whitney U tests for group comparison. The results showed a strong tendency toward experimental approaches, as indicated by the predominance of “Try” entries and a consistent emphasis on UI/UX usability across all categories. These patterns emphasize the foundational role of interface design and highlight the need to control for design-bias when conducting knowledge-based engineering (KBE)-oriented experiments. No statistically significant differences were found between finalist and non-finalist lesson ideas, indicating a convergence in participants’ design perspectives regardless of finalist status. Additionally, pre- and post-workshop surveys analyzed via Wilcoxon signed-rank tests revealed a significant increase in participants’ expectations for GenAI in education (p <.05), confirming the ideathon’s effectiveness in transforming perceptions. These findings offer design guidelines for future KBE experiments with GenAI, particularly regarding baseline conditions and interface specifications.

Due to the recent popularity of sports bicycles, many people have started tourism with cycling. Beginner cyclists often try to complete long distance cycling without sufficient training, causing overuse of leg muscles. Overuse of leg muscles caused by an irregular bicycle riding form leads to diseases such as iliotibial band syndrome. It is helpful to automatically identify beginner cyclists of irregular forms to avoid these diseases. However, none of the multimodal AI models recently attracting attention can achieve this. This paper proposes a system architecture for detecting individually optimized irregular bicycle riding form. Our proposed architecture uses bicycles installed with the Inertial Measurement Unit (IMU) and 2D Light Detection and Ranging (2D LiDAR) sensors. The sensor data obtained from IMU are analyzed by Recurrent Neural Network-based models, and the results are refined by multimodal classifier models combined with the 2D LiDAR data. The model selected by the proposed method obtained 0.900 in accuracy, 0.892 in precision, and 0.841 in recall for an f-score of 0.862, demonstrating the effectiveness of meta-model approaches.