IIAI Letters on Informatics and Interdisciplinary Research

Constraint-Based Protocol for Efficient Automated Negotiation in Multi-issue Negotiation

Shun Okuhara — 2025-02-22

This study investigates the impact of the number of fixed constraints on automated negotiation outcomes in a multi-issue negotiation scenario. Specifically, we analyze how the variation in the fixed constraints set by each negotiating agent influences social welfare, agreement frequency, and negotiation time. The negotiation protocol considers fixed constraints and evaluates their effects on the efficiency and feasibility of reaching agreements. Simulation results reveal that as the number of fixed constraints increases, social welfare and agreement frequency tend to decrease while negotiation time remains relatively unchanged. These findings highlight the importance of understanding the role of fixed constraints in optimizing negotiation outcomes and provide insights for designing more effective automated negotiation protocols.

Experimental Evaluation based on a Technology Acceptance Model for the Use of Congestion Mitigation Applications

Negai Nakamoto — 2025-10-02

This study proposes a web application that manages and optimizes user movement in real time to reduce congestion and the risk of crowd-related accidents at tourist sites, event venues, and evacuation areas during disasters. The application features a navigation system utilizing checkpoints, guiding users step-by-step toward their destination to prevent crowding at specific routes or locations. By employing Dijkstra’s algorithm, it calculates optimal routes based on congestion levels and the capacity of each checkpoint, enabling personalized route recommendations. In the event of a disaster, the system dynamically optimizes evacuation routes to support safe and efficient evacuation. User behavior data and congestion information are continuously recorded and analyzed to enhance system accuracy and optimize capacity settings. This system is expected to contribute to solving societal issues such as promoting sustainable tourism, preventing crowd accidents in urban areas, and supporting evacuation during emergencies.

Recreational Value of Tokyo vs. Nagasaki: A Travel Cost Analysis

Akihisa Kodate — 2025-10-02

Tourism plays a vital role in Japan’s regional revitalization and economic sustainability. However, the sector faces dual challenges: overtourism in major cities and limited visibility of regional destinations. The COVID–19 pandemic further exposed the vulnerabilities of tourism–dependent economies and highlighted the importance of domestic travel, which accounts for most of the tourism-related spending in Japan. This study compares the recreational value of urban and regional tourist sites using the Zonal Travel Cost Method. Four attractions –Tokyo Skytree, Tokyo Dome City, Huis Ten Bosch, and Glover Garden – were analyzed. Mobile phone data provided prefecture-level visitation estimates, and travel costs were calculated based on transportation expenses and entrance fees. A log-log regression model was employed to derive consumer surplus and total recreational value. The results indicate that Tokyo’s attractions generate substantially higher economic value, driven by broader geographic appeal, stronger brand recognition, and the ability to overcome distance-related barriers. In contrast, regional sites depend on localized demand
and face accessibility and visibility limitations. These findings underscore the need for demand–side policies and targeted regional promotion. By integrating big data with spatial economic modeling, this study offers a replicable framework for enhancing spatial equity and evidence-based tourism planning.

Proposal for a Novel Recipe Generation Approach Using Inverse TF-IDF and Generative AI

Mutsumi Kodama — 2025-10-02

This paper proposes a novel recipe generation system that integrates an inverse term frequency–inverse document frequency (TF-IDF) approach with generative artificial intelligence (AI) to balance creativity with practicality in home cooking. While conventional recipe recommendation systems tend to focus on commonplace recipes or staple ingredients, this study adopted an inverse approach by focusing on low-IDF ingredients and leveraging them as the foundation for generating highly reproducible recipes. Utilizing GPT-4, the system automatically constructs prompts that reflect user preferences and cooking conditions, enabling the generation of context-sensitive recipes. A lightweight web-based demo system was developed, allowing users to input two ingredients and receive original recipe suggestions along with cosine similarity scores in real time. Even when users input seemingly unrelated ingredients, the system provides composite recipe outputs, demonstrating its creative flexibility. This approach uncovers the latent creative potential in common ingredients and suggests broad applicability in the food industry, nutritional planning, and culinary education. By combining generative AI with statistical ingredient analysis, the proposed method offers a new intelligent support model for everyday cooking practices.

How Computer Vision Complements Health Care/Nursing Environment

Atsuo Yoshitaka — 2025-02-22

Aging of the population revealed that people of younger generation are facing to take care of more of old generation. It may not be satisfactory to arrange enough number of nursing staffs even in late-evening in hospitals or nursing and personal care facility, or it is not possible to watch every old people continuously in daytime. Detecting anomaly of human motion such as falling down, while no personal care assistant or nurse is accompanied, is expected to complement the insufficiency or lack of care by such staff. However, simple detection of anomaly of human motion such as falling down or sudden stoppage of the staff should be ignored as ‘false positive’ but adaptive detection of such motion by the persons who need assistance is preferable, in order to improve the operability of a system. This paper presents human anomaly detection based on human posture recognition by means of two-stage classification for the solution of abovementioned issues. Proposed method first classifies a person appeared in video into two classes, one corresponds to caregiver and another corresponds to aged people to be taken care of, and then detects anomaly motion only for the latters.

The Taxonomies Project

Andrea Valente — 2025-10-02

This paper presents Taxonomies, a STEAM project that can be placed on the intersection between art, science, and technology. In Taxonomies, art is taking the lead role, providing a goal for action, while sciences and computation provide a shared agenda and means for artistic creation. The project is a collaboration between the University of Southern Denmark, the Biotech Lab at Spinderihallerne and Rosborg Gymnasium, a Danish secondary school. During the past year a series of 3 workshops was designed, implemented, and run at Rosborg. Here we focus on the challenges, design solutions, and findings related to the third workshop, which aimed at introducing forms of creative, aesthetic coding suitable for art students with no prior experience and allowing them to explore algorithmic botany. Results include a general method to convert L-Systems to tangibles, an intuitive software simulator to create simple algorithmic plants; moreover, materials and methods developed worked well within the strict constraints of the project. A new edition of Taxonomies is being run in these months, and we are working on generalizing the workshop format to a reusable and shareable kit for other schools.

Assessing Reflective Learning through Human Revision of AI-Generated Essays

Satoshi KIMURA — 2025-10-02

This study examined how university students utilize generative AI in the context of writing admissions essays and how the depth of their reflective thinking affects the quality of AI-assisted writing. One hundred twenty-six students participated in five types of writing tasks modeled on university application prompts, with varying levels of AI involvement. Each submission was blind-reviewed using a four-level rubric designed to capture finer distinctions in structure, logic, and expression. The results showed that, while the influence of initial writing ability was limited to the early stages of AI engagement, the depth of reflection—measured as the Reflection Depth Score (RDS)—was significantly associated with the quality of outputs across all tasks. Participants with high RDS demonstrated greater score improvement in later tasks, while those with low RDS sometimes experienced declines in performance. These findings suggest that the educational effectiveness of generative AI depends not only on its available skills but also on the learner's metacognitive abilities, underscoring the importance of reflective and dialogic processes in AI-integrated writing instruction.

Optimization of Recommendation System by Improving Serendipity and Grouping Users Based on Their Number of Data Points

Haruto Domoto — 2025-02-22

Information recommendation systems aim to deliver optimal content to users, but conventional methods often only suggest similar items, leading to user boredom and reduced recommendation effectiveness. This study addresses this limitation by focusing on “serendipity”, enhancing the unexpectedness of recommendations. We compared conventional methods, existing techniques, and six newly proposed approaches. Users were grouped into three categories based on the number of data points they evaluated to analyze the impact on recommendation performance. For users with fewer data points, the best approach was to recommend items significantly different from the average user preferences. For users with more data points, recommending items that other users disliked but held high value for the target user was most effective. These strategies improved diversity and unexpectedness without sacrificing usefulness, thereby successfully enhancing serendipity. This method shows promise in increasing user satisfaction by providing a more engaging recommendation experience.

The Prediction Modeling System for Monitoring Elementary Students’ Mathematics Progress in Online Curriculum-Based Measurement

Mengping Tsuei — 2025-10-03

The curriculum-based measurement (CBM) is a data-based individualization method that monitors students’ performance and improvement over time. The purposes of this research were to develop prediction models—including ordinary least squares linear regression (OLS), Gaussian Naive Bayes, Bayesian Networks, and Random Forest—within a web-based CBM system, and to investigate their effectiveness in predicting elementary students’ mathematics performance. A total of 92 fourth-grade students participated in the study. They used mobile devices to complete CBM probes over an eight-week period. Performance metrics were analyzed to evaluate the error rate between predicted and observed scores. Overall, the results showed that OLS and Bayesian-based models were effective in predicting elementary students’ mathematics performance. Moreover, the findings indicated that distinct growth patterns still existed across different classes.

Improved Prediction of Geckler Classification in Gram Stained Smears Images for Sputum

Kei Goto — 2025-10-02

In this paper, we predict Geckler classification as Geckler classes and quality classes in Gram stained smears images for sputum, by using image classification and object detection. Here, we adopt VGG, MobileNet, DenseNet, RegNet, ConvNeXt, ViT and EfficientNet as image classifiers and YOLO11, HIC-YOLO11 and SOD-YOLO11 as object detectors. Note that, whereas the image classifiers classify the classes of images directly, the object detectors first detect buccal squamous epithelial (BSE) cells and leukocytes in the image and then predict the class of the image by the number of them.

Proposal for a PBL-type Learning Model Using 3D Gaussian Splatting

Kazuya Murata — 2025-10-02

This study proposes a next-generation problem-based learning (PBL) model for cultural tourism and education based on 3D Gaussian Splatting (3DGS), a technology that emerged in 2023. Compared with conventional photogrammetry and Neural Radiance Fields (NeRF), 3DGS enables high-definition and high-speed 3D rendering and real-time display on smartphones and stand-alone VR. In this study, this technology is not limited to a mere means of viewing, but is integrated with augmented intelligence (AI) to create a mechanism for dynamically presenting contextualized stories and learning materials to users. Specifically, using cultural resources in the Kirishima region of Kagoshima Prefecture as the subject matter, we propose a PBL model that integrates the entire process of filming, model optimization, guide presentation by AI, fieldwork, and sharing in the metaverse. In this process, the model was verified from various perspectives, including rendering delay, frame rate, learning effect, and ripple effect on the local economy. Students and tourists with prior virtual experience tended to significantly improve their learning depth and time spent at the site. Additionally, by converting the generated content to NFT and connecting it to local currencies and cultural heritage preservation funds, we propose a circular learning and economic ecosystem.

Estimating Human Difficulty for SameGame Puzzles

Motoki Miura — 2025-02-22

SameGame is a computer puzzle game in which players must select adjacent blocks of the same color vertically and horizontally to remove them from the screen. Generally, initial blocks are arranged randomly, but it is known that the difficulty varies depending on the initial blocks. It is also known that it can be impossible to completely remove all blocks on small boards. As a way to quantitatively estimate the difficulty of SameGame puzzles, we devised an index that quantifies the correspondence between blocks when they are removed and the discrete state of those blocks. We compared our proposed index with a random success rate, which examines the ratio of the number of answer steps divided by the number of all possible steps. By investigating the correlation with the clear rate based on human play data, we analyzed the effectiveness of several indices including the proposed method.

Simulating Strategic Decision-Making: A Digital System for Issue-Based Educational Games

YU-HSUN HSUEH — 2025-10-03

This study aims to develop game system for an issue-based game, <Utopia>, which integrates both online and physical interactions to enhance students’ understanding of social issues, critical thinking, and collaborative skills. <Utopia> employs data visualization to help players intuitively grasp the impact of various societal events. The gameplay involves multi-phase interactions and decision-making processes triggered by emergent events, including stages such as national conferences, joint discussions, and policy declarations. The system is supported by five key modules—parameter visualization, country profiles, chatroom, bulletin board, and trade management—which collectively facilitate students' discussion and strategic planning. It is anticipated that the <Utopia> issue-based game system will effectively foster students’ problem-solving abilities and strengthen their capacity for critical analysis and collaboration in complex scenarios. Through a combination of questionnaire surveys and behavioral data collection, this study will analyze students’ learning behaviors, team collaboration patterns, and reflections on social issues throughout the gameplay.

Fostering AI Literacy through SDG-Oriented Hands-On Learning Activity for Non-CS Students

Hercy N. H. Cheng — 2025-10-02

This study examines how AI learning activities can be designed to help non-computer science students apply AI tools to address real-world problems aligned with the United Nations Sustainable Development Goals (SDGs). A one-day hands-on workshop was implemented as the project in an introductory AI course, guiding students through goal setting, service design, prototype implementation, and ethical reflection. Students used Google Teachable Machine and Dialogflow Essentials to build AI-enabled prototypes that combined image and language understanding. To assess the outcomes, a survey was administered to first-year students, measuring AI conceptual understanding and perceptions of AI application authenticity. While no statistically significant differences in AI capability self-assessment were found, the students participating the workshop demonstrated more cautious and realistic views of AI applications. These findings suggest that experiential, interdisciplinary AI education can enhance critical engagement with AI technologies and support the development of socially responsible learners.

A Case Study for Performance Improvement in the 2024 Impact Rankings

Cherng-Min Ma — 2025-10-03

The SDGs (Sustainable Development Goals) were proposed by the United Nations in 2015 that set common goals for balancing economic, social and environmental development for individuals and organizations. Based on SDGs, the Impact Rankings of THE (Times Higher Education) is a platform for universities to demonstrate sustainable achievements and international influence. This article analyzes the 2024 ranking performance of a Taiwanese university and offers suggestions for improvement. Other universities can adjust their goal selection strategies and sustainable development practices to improve their performance on the Impact Rankings accordingly.

SNS Use among Adolescents with Neurodevelopmental Disorders: Characteristics, Challenges, and Educational Implications

Chie KATO — 2025-10-02

This study examines how adolescents with and without neurodevelopmental disorders differ in their use of Social Networking Sites (SNS). Individuals with ASD or ADHD tend to prefer interest-based platforms and one-way interactions, whereas neurotypical peers primarily use SNS to maintain offline relationships. Those with ASD or ADHD tend to prefer interest-based platforms and one-way interactions, while neurotypical peers use SNS mainly for maintaining offline relationships. The findings highlight the need for inclusive digital literacy programs in educational settings that accommodate diverse communication preferences.

Evaluating and Enhancing RAG Systems through Test and Source Analysis

Kazunori Matsumoto — 2025-10-02

This paper presents a prototype Retrieval-Augmented Generation (RAG) system developed for university curriculum guides and evaluates its performance through experiments. RAG, which combines large language models (LLMs) with independent information sources, is emerging as a solution to address generative AI challenges such as hallucinations and the lack of domain-specific knowledge. By prioritizing information from dedicated databases, RAG can enhance factual accuracy and reduce hallucinations. Through experimental trials, the system demonstrated reliable performance in some cases, although issues related to the quality of information sources and data extraction were identified. These findings underscore the importance of robust testing and systematic revisions of information sources. This paper reports on an outline of the system implementation, the guides for improvement, and the experimental results. We find that an iterative improvement process is crucial for enhancing the overall quality of RAG. This process involves not only optimizing retrieval and generation mechanisms but also continuously reviewing and refining the information sources themselves, the system can systematically adapt to ensure sustained relevance and improved response accuracy over time.

How Does the Persona Given to Large Language Models Affect the Idea Evaluations?

Hiroaki FURUKAWA — 2025-02-22

This paper investigates the effect of personas in Large Language Models (LLMs) on idea evaluation. The language comprehension ability of LLMs has recently reached a level comparable to that of humans. Consequently, LLMs are being explored for their potential application in idea evaluation. However, LLMs face several challenges in their outputs, including hallucinations and biases. To address these issues, prompt engineering is utilized to guide LLMs toward producing desired results. This study focuses on Persona as a factor in prompt engineering for LLMs. Personas enable the reproduction and control of specific personalities within LLMs. The objective of this study is to validate the relationship between personas and idea evaluation using GPT-4. The results suggest that variations in personas influence the evaluation of ideas. Furthermore, a relationship was observed between evaluation scores and the evaluation criteria deemed important by the LLM.

The Process of Content Analysis for Narrative Medicine: Methodological Exploration and AI Applications

WEI-SUNG PENG — 2025-10-03

This study investigates the application of AI in narrative medicine through content analysis, demonstrating how AI aids in identifying themes, emotions, key concepts, and structural patterns within medical narratives. A multi-stage, multi-tier analysis framework was employed, integrating natural language processing (NLP) for text preprocessing, including part-of-speech tagging. An iterative process of bilateral adjustments between human expertise and AI ensured methodological rigor, enhancing inter-rater reliability through noise removal. AI-assisted keyword extraction and semantic network analysis uncovered hidden patterns in patient-physician interactions. The findings highlight AI’s potential to advance qualitative research in narrative medicine while emphasizing the necessity of expert annotation for analytical precision.

ELEGANCE: A customizable EducationaL gamE for foreiGn guArdians to understand japaNese sChool culturE

Ziliang Wang — 2025-10-02

Aiming to address challenges faced by foreign guardians in Japan regarding language barriers and unfamiliarity with local educational practices, this study designed and developed an educational game, named ELEGANCE. The game features two customised, user-friendly learning modes: Exploring Mode, which provides guardians with detailed information about Japanese school activities and highlights essential information through multisensory interactions, and Quiz Mode, which enables guardians to access their understanding of the explored school culture content. A pilot study involving 16 foreign guardians demonstrated the game’s effectiveness, with a significant improvement in comprehension scores in the post-test, compared to the pre-test. Participants reported a low mental load and positively rated the game's immediate feedback and detailed explanation functions, indicating a well-balanced integration of educational content and gameplay. These findings highlight the potential of educational games like ELEGANCE in facilitating multicultural education, especially in effectively enhancing guardians' understanding of school-related cultural practices and knowledge.

Equity Pedagogy and the 21st Century Classroom: A Scoping Review

William Tarimo — 2025-10-02

The rapid integration of technology is transforming education, emphasizing the critical need to address inequities and implement effective equity pedagogy. While promising, ensuring equitable technology integration presents significant challenges. This scoping review synthesizes literature on equity pedagogy within technology-supported learning contexts, including frameworks such as Computer-Supported Agile Teaching (CSAT). The review explores the motivations, challenges, successes, and opportunities at this intersection, examining how technology can enhance equitable practices by focusing on issues such as access, implementation across modalities, and student support. Findings aim to inform effective and equitable technology integration in contemporary classrooms.

Perspectives on Michelin Green Star Restaurants: Inspector and Customer Review Analysis

Yu-Hsiang Hsiao — 2025-10-02

Rapid technological advancements have transformed people’s lifestyles and eating habits. While technological innovations have brought convenience, they have also contributed to the growing challenges of global warming and environmental pollution. This study aims to explore the perspectives of customers and Michelin inspectors on the service quality and sustainability practices of Michelin Green Star restaurants. Data were collected from the global Michelin Guide website and TripAdvisor, and text mining techniques were applied to analyze online reviews from both inspectors and customers. Using the BERTopic model, the study identified seven key aspects of restaurant service quality considered in Michelin restaurant evaluations, nine aspects specific to Green Star restaurant assessments, and eight aspects highlighted in customer reviews. These findings reveal the similarities and differences in the evaluation criteria used by inspectors and customers, providing valuable insights for restaurant operators.

Principle-Driven Micro-Script Generation for Informal Collaborative Learning on Discussion Boards

Hideki Kondo — 2025-10-02

This paper introduces Principle-Driven Micro-Script Generation (PDMSG), a method that leverages large language models (LLMs) to dynamically generate context-aware collaboration micro-scripts for informal learning communities. PDMSG combines four inputs—(1) activity goals, (2) community constraints, (3) learning principles, and (4) recent discussion-board logs—into a single prompt for the LLMs, which return concise next-step recommendations for each participant. A pilot in the BookClub community demonstrated that PDMSG yields context-relevant, actionable suggestions, indicating its promise for informal collaborative learning. Future work will refine delivery interfaces and empirically evaluate learning outcomes.

Development and Evaluation of a Digital Concept Mapping System for Nursing Process Learning Support

SAKIKO SUMAI — 2025-10-02

This study aimed to develop and evaluate the utility of a digital concept mapping system (AKa Tool) designed to support learning in the nursing process. The system was tested by 215 nursing university students during nursing process lectures, and a questionnaire survey was conducted regarding their user experience and the system's utility. An analysis of the responses from 69 students (response rate: 32.1%) revealed that 41.8% evaluated the system’s usability positively, whereas 36.4% evaluated it negatively. Computer skills and system user experience were found to be significantly associated, as well as system usability and its application during the lectures (p<0.01). Free-text responses regarding the desired improvements were categorized into seven groups: [Printing Issues], [Copy Function], [UI/Display], [Operability], [Shapes and Arrows], [System Stability and Performance], and [Other]. The results suggest that the system exhibits a degree of utility in nursing process learning, although further improvements are needed to en-hance its usability.

Quantitative Evaluation of Perceived Acceptability of Accentual Patterns in Four-Mora Japanese Words Based on Lexical Attributes

Ikuyo Masuda-Katsuse — 2025-03-03

We generated four-mora Japanese word utterances with various pitch accent features and mapped the perceived pitch accent acceptability of native Japanese speakers onto a distribution of pitch accent features. The results confirmed that even for words classified as having the same accent type, the acceptability distribution differed depending on the word types. These results demonstrate the diversity of pitch accent perception and provide quantitative support from the perspective of auditory perception for knowledge about accent rules reported in linguistics. We also discussed the potential application of these results to CALL systems for Japanese language learners.

Fostering Inquiry Confidence in Elementary Students through a Game-Based Socioscientific Issue Learning Approach

Yuhao Lu — 2025-10-03

This research aims to examine how the inquiry-based game Future City influences elementary students’ confidence in their inquiry skills within the framework of a progressive inquiry learning model. The study was conducted at a public elementary school in Taiwan, where data were collected through pre- and post-assessments administered to sixth-grade students. During the game, players assumed various societal roles, engaged in interactive decision-making, and explored topics such as environmental sustainability, economic growth, and social equity. The key findings reveal that, after participating in the Future City game, students exhibited a notable increase in confidence regarding their inquiry abilities—particularly in drawing conclusions. Additional skills, including formulating questions, planning data collection, and reporting findings, also showed a modest improvement in confidence.

Predicting Neoadjuvant Therapy Response in Breast Cancer Patients: A Multi-Omics and Machine Learning Perspective

Lina AlRifai — 2025-10-06

Breast Cancer (BC) treatment response varies due to underlying heterogeneity. Personalized therapy based on multi-omics profiling enhances efficacy by identifying patientspecific biomarkers and optimizing strategies. Multiomics integrates diverse biological data to understand mechanisms and enable customized treatments. Advances in ML and DL revolutionize BC therapy response prediction, leveraging multi-omics to improve precision, identify biomarkers, and refine strategies, reducing morbidity and mortality. This study presents a comparative analysis of multi-omics-dependent models for predicting neoadjuvant therapy response, highlighting techniques like DeepSurv, Gradient Boosting Machine (GBM), and Weighted MultiSource Canonical Correlation Analysis (WMSCCA). These models use data sets such as TCGA, METABRIC, and ICGC to boost predictive power. DL enables automated feature extraction, while ML offers interpretability for balanced predictive analytics. Despite progress, challenges remain, including data limitations, lack of external validation, and interpretability issues.

A Method for Converting Lecture Videos into Microcontents and Visualizing Them

Masako Furukawa — 2025-10-02

With the spread of online courses, a large amount of lecture videos has become available. In order to make more advanced use of existing lecture videos, it is necessary to convert the videos into microcontents and visualize them in a way that allows users to easily find the parts they need. For this purpose, in this paper, we propose a method for converting lecture videos into microcontents. The method vectorizes transcripts of videos using Doc2Vec and then divides videos based on the distance between these vectors. In addition, we compared visualization methods using principal component analysis, multidimensional scaling, and t-SNE, and found that t-SNE is suitable for the visualization.

Digital Knowledge Twin: Bridging the Gap Between Physical and Cyber Knowledge Spaces by Generative AI

NAOSHI UCHIHIRA — 2025-02-22

The extraction, sharing, and utilization of explicit, latent, and tacit field knowledge (called “Gen-Ba knowledge”) possessed by skilled workers in fields such as healthcare, caregiving, agriculture, maintenance and inspection, and manufacturing, have long been recognized as important yet challenging tasks. Organizations and companies capable of achieving this through human systems hold a competitive advantage. With the advent of generative AI, explicit knowledge that can be codified (documented) has become dramatically more accessible. However, the challenge remains in how to efficiently and effectively handle the latent and tacit Gen-Ba knowledge possessed by humans, which AI cannot directly process. This study proposes a “Digital Knowledge Twin”, which facilitates externalization, combination, and internalization of Gen-Ba knowledge for bridging the gap between physical and cyber spaces. In the externalization phase, Gen-Ba knowledge is accumulated in the physical space as fragments of knowledge, integrating human voice messages, photos, and physical sensor data. Then, these Gen-Ba knowledge fragments are linked with structure of operations in the cyber space in a combination phase. Finally, Gen-Ba knowledge is collaboratively internalized in the physical space through workshops, where internalization means sensemaking and symbol grounding among humans. This paper presents the necessary technologies and system architecture required for the implementation of the “Digital Knowledge Twin.” Gen-Ba knowledge management using generative AI is a challenging issue, and the theoretical contribution of this study is that it provides one promising research direction of future knowledge management.

Case Study of UEC's Novel Learning Environment for Cultivating Engineering Talents

Kahori Ogashiwa — 2025-10-02

The present study introduces practical examples of the educational programs and learning support environments aimed at cultivating UEC’s “Kō-gata” human resources—individuals equipped with solid foundational knowledge, specialized vertical expertise, and the capacity for horizontal expansion through innovation—at the University of Electro-Communications (UEC). Here, “Kō-gata human resources” are defined as individuals equipped with solid foundational knowledge, specialized vertical expertise, and the capacity for horizontal expansion through innovation, enabling them to respond flexibly to diverse societal challenges. To cultivate such talent, UEC has begun promoting an interdisciplinary minor program and educational digital transformation. The present paper highlights two key initiatives: the carbon neutrality minor that was established in 2024, and the UEC Learning Analytics Platform, which visualizes the students’ learning progress. Although still in the early implementation stages, as these efforts have been successful in promoting student autonomy and enhancing teaching quality, they are expected to lay the groundwork for future educational innovations.

Proposal of an SEIR Model Considering Inter-population Transfers and Vaccine Availability in Large Populations

Hiroyoshi Matsumoto — 2025-10-02

This study proposes an infectious disease model incorporating person-trip data and adopts an extended SEIR framework based on the classical SIR model. The analysis specifically targets the spread of the Omicron variant of COVID-19, which proliferated across Japan in the early months of 2022. Following the construction of a SEIR-based epidemiological model, a single infectious region is segmented into four distinct groups to simulate diverse transmission dynamics through person-trip movements. Using empirical records of infection cases and commuter flows in Saitama, Tokyo, Kanagawa, and Chiba prefectures from January to April 2022, the model estimates key parameter values, including infection rate, recovery rate, and mobility rate. Additionally, vaccine efficacy parameters released by the Ministry of Health, Labour and Welfare are incorporated into the simulation. Based on the estimated parameters, the study investigates the potential for mitigating the spread of infection. The model’s validity is then assessed by comparing the simulated data of new infections with actual epidemiological data from the aforementioned four prefectures used in the parameter estimation. Furthermore, the study explores various scenarios by altering the parameters related to human mobility and vaccine efficacy to evaluate which preventive measure—mobility restriction or immunity acquisition through vaccination—more effectively curtails the spread of infection.

Investigation of Effective Heart Rate Variability Indices for Emotion Estimation During Long-term Images Gazing

Tetsuya Nishibe — 2025-02-22

In recent years, demand for emotion estimation using heart rate has increased in fields such as life-log and healthcare. HRV indices can estimate the activity level of the autonomic nervous system, but their sensitivity varies depending on the task and stimuli, sometimes leading to inconsistent results. Therefore, selecting the optimal HRV indices for each task and stimuli is important, and this study focused on emotional induction during long-term visual stimuli. We constructed the experiment in which subjects gazed at images of consistent emotional evaluation values, and identified the effective HRV indices for emotion estimation. The results showed that SDRR and L were effective for estimating excitement, Mean and rMSSD for estimating disgust, and LF/HF for classifying boredom and relaxation. These results are expected to serve as a basis for emotion estimation in situations where people are exposed to visual stimuli for long periods, such as when viewing videos or scenery.

Examining the Connection Between the Color Scheme of Event Announcement Images and View Counts on Social Media Through Machine Learning Models

Kayu Morishige — 2025-10-03

In this study, we examined whether there is a relationship between the colors used in event announcement images and the number of views. We compared two machine learning models: a conventional model using event images, latitude/longitude, and titles as explanatory variables, and a proposed model incorporating the number of colors, representative HSV colors, latitude/longitude, and titles. The results showed that the proposed model performed similarly to the conventional model. Further analysis revealed that the number of colors and HSV values influenced the number of views. Therefore, it was revealed that the colors in event announcement images are related to the number of views.

Development of a Framework for Promoting the Use of Multiple Assignment Exercises in Basic Nursing Education

Hanae Okamoto — 2025-10-02

New nurses are required to be able to take care of multiple patients at the same time in the clinical setting, observe their conditions and respond to their complaints, while judging their priorities and appropriately resolving multiple issues. However, in basic nursing education, practice in which multiple patients are taken care of is limited to integrated practice in the final year of study, and systematic training is not sufficient. In response to this issue, the "multiple-task exercise," which includes preparation before practice and review after practice, has been developed to enhance the learning effects of integrated practice, and its educational effects have been demonstrated. Despite this, the practice is still limited to a few educational institutions and is not widely used. In this study, we developed an educational framework to support the development of the exercise, inspired by the Software Process Improvement Framework (SPI Framework) used in software process improvement activities, with the aim of promoting and establishing the use of multiple-task exercises. The overall picture and components of the framework are clarified, and the effects of introducing the framework are discussed.

Evaluating and Enhancing RAG Systems through Test and Source Analysis

Zelan Shi — 2025-10-02

Study on the Application of Weak Go AI in Go Education: the Balance between Go Progress and Emotional Value

CHI LIU — 2025-02-22

With the continuous advancement of artificial intelligence (AI) technology, the application of AI in Go education and training has become a hot research topic. This article explores the important role of weak Go AI in Go education, especially its application in beginner teaching. We analyzed the current Go education methods and research status, and based on this, proposed a technological innovation: by adjusting the difficulty of AI, it can promote the improvement of beginners’ Go skills while maintaining their enthusiasm for Go. Further experiments have shown that when the AI’s Go power is appropriately higher than the tester’s, it can effectively improve Go power while also considering emotional value. The research in this article proves that this innovation has profound significance for Go education.

Preliminary Practices for Java Programming Tools and TDD Courses Utilizing Generative AI and Online Education

Mika Ohtsuki — 2025-10-03

With the rapid advancement of generative AI, automation is increasingly being introduced across various stages of software development. In response to these changes, programming education must also evolve to incorporate the use of generative AI from the outset. In this study, we designed and implemented intermediate-level programming courses that integrate generative AI tools such as GitHub Copilot. The curriculum consisted of three subjects: Object-Oriented Programming, Test-Driven Development, and Practical Project Development. Each course combined on-demand instructional materials with AI-assisted exercises. As a result, learners reported high levels of satisfaction and frequently accessed course materials and assessments. Notably, many students demonstrated the ability to critically evaluate and adapt AI-generated suggestions rather than relying on them uncritically. A comparative survey between GitHub Copilot and Google Gemini revealed that students were also beginning to select AI tools based on purpose and context. These findings indicate the potential of educational designs that foster practical programming skills and cultivate AI literacy. This initiative highlights the promise of programming education that is both AI-integrated and personalized, offering new directions for curriculum innovation in higher education.

Exploring Collaborative Argumentation Through a Local-Issue-Based Board Game

Kuo-Yang Chiu — 2025-10-02

This qualitative case study explores how a locally contextualized board game can foster collaborative argumentation among high school students. We developed Z-City Myth, a role-playing argumentation game informed by dialogic argumentation and situated cognition theories, where players collaborate to interpret data, build hypotheses, and present conclusions on an investigation. Two 3-student groups (N = 6) participated in this research, including gameplay observation and post-game interviews. Thematic analysis characterized how students in both groups used data as evidence, coordinated perspectives, and applied reasoning strategies. Results suggest the game affords collaborative argumentation, demonstrating its potential in learning argumentation, such as providing authentic argumentation context and assessment. As two student groups also demonstrated issues and challenges in collaboration and argumentation, further scaffolding on both are required in the future.

An Automatic Bottom-up Idea Grouping Method based on LLMs

Takayuki Ito — 2025-02-22

This paper presents a novel automatic Bottom-up idea grouping method leveraging Large Language Models (LLMs) to facilitate the organization of ideas and concepts. Traditional methods of idea grouping, such as the KJ method, require significant manual effort to group and synthesize information effectively. Our approach utilizes the contextual understanding and grouping capabilities of LLMs to automate this process, aiming to reduce cognitive load and improve efficiency. By performing unsupervised grouping based on the insight of each idea, the model automatically generates cohesive idea groups from diverse sets of inputs in the bottom-up way. We evaluate the effectiveness of our method on qualitative datasets, comparing it with top-down categorizing method. Results indicate that the proposed bottom-up method not only aligns well with human clustering but also demonstrates a high level of interpretability and accuracy in grouping similar ideas. This study highlights the potential of LLMs in transforming qualitative analysis by offering a scalable and intuitive solution for idea grouping.

Multi-User Activity Recognition in an Indoor Environment with Transformer Architectures

MD Irteeja Kobi — 2025-10-03

This paper proposes a device-free Human Activity Recognition (HAR) system, utilising Wi-Fi Channel State Information (CSI) to maintain the privacy of users in a multi-user environment. To achieve this goal, substantial annotated training data is required, which is often imbalanced with poor generalisability in complex, multi-user environments. To overcome these gaps, a hybrid deep learning approach is proposed that integrates signal pre-processing, targeted data augmentation, and a novel CNN incorporating a Transformer model. Experimental results show that the proposed model outperforms several baselines in single-user and multi-user contexts. Our findings demonstrate that combining real and augmented data significantly improves model generalisation in scenarios with limited labelled data.

A Study on Active Inquiry Learning Using Interactive Applications in Science Museum

Ryushi Sanada — 2025-10-02

This study investigates the educational impact of digital learning tools such as interactive applications compared to video in promoting active inquiry learning in science museums. While digital learning tools are increasingly used in informal education, prior studies have largely focused on usability or factual knowledge, with limited attention to their alignment with constructivist learning or promotion of scientific reasoning. To address this gap, we developed two digital tools—an interactive application and a video—simulating scientific phenomena such as Mars’ retrograde motion and the water pearl phenomenon. A field experiment involving 42 eighth-grade students in Japan used a crossover design: one group used the interactive application before the video, and the other in reverse order. Evaluation included pre- and post-questionnaires and analysis of open-ended responses using statistical methods and text mining. Results showed that interactive applications significantly enhanced students’ interest, engagement, and exploratory behavior, while videos were more effective for structuring information and aiding retention. These findings suggest that interactive applications, when designed to support trial-and-error exploration and visual-numeric linkage, can effectively foster inquiry-based learning. The study highlights the importance of selecting media based on learning goals and stages and contributes design insights for developing digital tools in informal educational settings like science museums.

A Case Study on the Effectiveness of the “Theory and Methods for the Utilization of ICT in Education” in the Japanese Teacher Training Curriculum: An Intensive and Remote Format

Mitsuhiro Watanabe — 2025-10-02

In this study, we examined the effectiveness of the subject “Theory and Methods for the Utilization of ICT in Education” recently introduced to the Japanese teacher training curriculum. In AY2024, the course was conducted at S University in an intensive and remote format using practical tools, videos, cloud-based collaboration, and class plan creation. Pre- and post-course surveys using a 16-item checklist on ICT utilization revealed improved student confidence across all items after the course. The results of the intensive and remote classes were comparable to those of regular face-to-face classes conducted in AY2023, indicating that the proposed instructional design is effective regardless of the instructional format.

Exploring the Modality of Network-Type Thinking in Young Children with Picture Books

Ryosuke Kozaki — 2025-03-03

This study investigated the concrete modality of network-type thinking and knowledge entity formation of young children aged from four to six, employing the picture books. More precisely, we analyzed the techniques with which they associate words and concepts from read stories. As a result, it was demonstrated that the children substantially hire verbs and syntagmatic association when developing a knowledge network. In addition, it was also shown that six-year-olds recalled significantly more keywords than younger children when it came to syntagmatic (or “serial”) thinking method. Furthermore, this paper indicates some of the future possibilities of the findings that are to be applied to the more effective selection of picture books in nursery school settings for their language development.

and Robots: Developing Computational Thinking in Young Learners through a Complex Board Game

Hsuan-Wen Chen — 2025-10-03

This study investigates the effectiveness of using the educational complex board game <TechTopia> to enhance computational thinking (CT) skills among upper-grade elementary students. The game integrates interdisciplinary learning, programming, and robot-related scenarios, providing a hands-on experience that encourages students to apply CT concepts such as pattern recognition, algorithm design, and logical reasoning. It incorporates problem-solving tasks similar to those found in the Bebras challenge, allowing students to engage with real-world-inspired puzzles that support the development of core CT abilities. A pre-test and post-test were administered to measure changes in students' CT abilities, and a satisfaction survey was conducted to assess their engagement and learning experience. The results showed significant improvements in CT skills, with students achieving high accuracy rates, especially in tasks related to data representation, algorithmic thinking, and reverse reasoning. The survey also indicated high levels of student satisfaction, with positive feedback on the game's design, collaborative elements, and its impact on their problem-solving abilities. These findings suggest that game-based learning, such as <TechTopia>, can effectively promote computational thinking and enhance students' critical thinking and problem-solving skills.

Corpus Construction for Automatic Meal Planning

Michiko Yasukawa — 2025-10-02

Eating a healthy diet every day is ideal, but difficult for many people. There is a scarcity of information and available resources regarding ideal food combinations, limiting the possibility of developing effective solutions for culinary information processing. As a result, food recommendations based on existing data tend to be unhealthy because the majority of users lack nutritional knowledge. While collecting healthy and tasty menus through crowdsourcing is one solution, such data-input tasks must be carefully designed from a combinatorial perspective. We describe a methodology to operationalize the collection of appropriate data for this complex task, present notable features of the resulting data resource, and illustrate how the obtained data can be referenced in automatic menu planning.

Evaluating Instructor Role in a Personal Software Process Improvement Course Based on the Process Data

Shigeru Kusakabe — 2025-10-02

The Personal Software Process (PSP) is a well-designed personal software process effective for software engineers in understanding and improving their performance accompanied by training courses to establish and improve their software development processes. In this paper, we evaluate the importance of instructors’ role in the PSP courses. The PSP for Engineers is one of the PSP training courses offered by the SEI, where participants learn the knowledge and skills for developing high-quality software through lectures and exercises led by instructors. The lecture materials for the course have been available under a Creative Commons license from October 2018, and participants can self-learn PSP without the guidance of instructors. However, for example, it is not always easy to collect accurate and precise process data, the basis for improvement. We expect instructor guidance plays a major role in effectively improving software processes. In this paper, we analyze the importance of PSP instructors’ role based on the students’ process data collected in the PSP for Engineers course at our graduate school over years.

Interactive Computer Simulation and Animation (CSA) to Improve Student Learning of Impulse and Momentum in Rigid-Body Engineering Dynamics

Ning Fang — 2025-10-02

Engineering dynamics is a foundational engineering science course that many undergraduate students struggle with because the course requires students to have a solid conceptual understanding and problem-solving skills. This paper presents an interactive computer simulation and animation (CSA) learning module recently developed to improve student learning of impulse and momentum in rigid-body engineering dynamics. The paper describes the development of the CSA learning module and its important features. A quasi-experimental study involving pre- and post-tests on 134 engineering undergraduates in two groups (comparison vs. intervention) was conducted. The results show that the developed CSA learning module increased the normalized student learning gain by 51.5%.

Digital Technology Applications for Discovery of Contextual Links among Records

Yichen WU — 2025-10-02

Compared with those developed for born-digital records, ICT applications for digitised historical documents remain relatively immature, awaiting further development. In response to growing interest in local historical research, the utilisation of digital technologies to discover contextual links among scattered records has yielded noteworthy outcomes. This paper examines the current state of digital technology utilised for Chinese historical research and education through case studies. Three representative platforms, THDL, CGKSP, and CHLAD, which rediscover and present contextual links among fragmented Chinese medieval records, were selected for in-depth analysis. This paper examines the potential of introducing archival discourse on context to enhance the representation of historical record aggregates with greater consistency and integrity on these platforms.

Exploring the Effects of a Scaffolded Programming Learning Environment Integrated with ChatGPT on Students’ Learning Performance and Self-Efficacy

Chia-Jung Chang — 2025-10-02

This study designed an online scaffolded learning programming environment, integrating self-regulated learning model with ChatGPT, to facilitate students learning programming and explored its impact on learning performance, cognitive load, and self-efficacy. A one-group pretest-posttest experimental design, the experiment involved 58 university students who took part in three weeks learning activity. The results showed that the environment benefitted students on the enhancement of conceptual understanding of web programming, the enhancement of learning self-efficacy on logical thinking, algorithm, and debugging. Moreover, it increased students’ germane cognitive load and reduced their perceived task difficulty.

Extracting Factors through Additional Impression Evaluation Experiment Assessing Both High-rated and Low-rated Reviews Posted at EC Sites

Yuya Yokoyama — 2025-02-22

The global diffusion Internet has established electronic commerce (EC) sites where anyone can purchase online. In order to avoid a mismatch between user and products, users can write a review on the product they purchased, helping users refer to the review of the commodity and make decisions. Nevertheless, with more users and items flooded on EC sites, the issues of mismatches are becoming conspicuous. In order to solve these issues, the authors conducted impression evaluation experiment to extract the impression of low-rated reviews. However, the previous analysis yielded only three factors due to insufficient experimental materials. Therefore, this paper reports further experiment with appending high-rated reviews. As a result of the analysis, eight factors are obtained under the fifty impression words. It could be concluded that the approach of extracting impression from the statements can be applicable to the review statements of EC sites under the unbiased experimental materials.

Learners’ Gaming Experiences in the Issue-Based Board Game

GengDe Hong — 2025-10-03

An increasing number of game designers have begun incorporating contemporary social and environmental issues into their game designs, which has given rise to issue-based board games. These games involve mechanisms such as role-playing, resource allocation, and interest negotiation to immerse participants in contextualized learning experiences. Within socially interactive scenarios, learners are exposed to diverse perspectives, thereby cultivating empathy and historical thinking. By examining learners’ overall engagement and experiences with issue-based board games from their own perspectives, researchers can help address the challenge of balancing gaming enjoyment with historical learning. This study therefore employs a questionnaire to investigate learners’ gaming experiences following their participation in an issue-based board game. The results reveal a high level of overall engagement with the game mechanisms, with particularly strong ratings for interactivity and enjoyment—even when learners did not realize they were learning history. These findings indicate that the game effectively stimulates learning motivation and facilitates peer communication and collaboration.

A Knowledge Graph Approach for Analyzing Player Social Media Reviews

ZIYU ZHAO — 2025-10-02

In recent years, as the gaming industry has developed, both the number of games and players have continuously increased. However, in recent years, numerous conflicts have arisen between game developers and players, leading to significant consequences for both parties. These incidents stem from a lack of effective communication between the two sides. Meanwhile, player communities have amassed a wealth of authentic reviews posted by actual players, reflecting their most genuine opinions about the games. To address this issue, the present study conducts sentiment analysis on reviews from player communities and proposes the use of graph neural networks (GNN) along with a knowledge graph constructed from gaming wikis to uncover the deep-seated reasons behind the various emotions expressed by players. Additionally, a pre-trained large model is employed to better understand player feedback, thereby enabling game developers to establish more effective communication with their player base.

AR Training for Automotive Repair Paint Inspection: Development and Evaluation

Yuka Takai — 2025-10-02

This study presents the development and evaluation of an augmented reality (AR) training application (app) aimed at improving skills in automotive paint inspection. Although the accurate assessment of paint finish quality is essential in automotive repair, opportunities for conventional training are often limited. To address this gap, we developed a prototype AR app that simulates visual inspection tasks. The prototype AR app was subsequently evaluated by two experts and one novice, with a focus on usability and perceived effectiveness. Although participants experienced minor difficulties with camera handling, they recognized the potential of the app to support skill development. Experts’ feedback emphasized the need for enhanced realism in the representation of paint textures. Overall, preliminary findings indicate that the AR app is a promising supplementary tool that can be used in conventional training methods during automotive-repair paint inspection.

Lightweight Convolutional Recurrent Neural Networks for Sound Event Classification

Cheng Siong Chin — 2025-10-02

Sound Event Classification (SEC) is essential for applications like urban noise monitoring and smart home automation, but modern models often struggle with efficiency and deployability. This study evaluated lightweight SEC architectures namely CNN, CRNN, and Transformer using the UrbanSound8K dataset, considering both accuracy and resource consumption. CRNN emerged as the top performer, achieving around 90% accuracy with only 175,754 parameters, surpassing the efficiency of CNNs and Transformers. These results underscore the CRNN's potential for scalable and cost-effective SEC solutions, making it ideal for smart city infrastructure and resource-limited IoT applications.

Preliminary Practices for Beginner's Programming Course Utilizing Generative AI and Online Education

Miyuki Murata — 2025-10-02

Generative AI technology is rapidly advancing and is increasingly being used to automate various processes in software development, from planning to testing. In light of these technological in-novations, programming education at universities and institutes of technology must be restruc-tured to align with software development processes using generative AI. Programming and gen-erative AI are highly compatible, and generative AI can support a wide range of tasks, including automatic code generation, refactoring, code suggestion, answering programming-related ques-tions, and test code generation. In this paper, we propose a beginner-level online programming course designed to utilize generative AI as a support system for programming education. We developed educational content and implemented it in a preliminary trial with a small group of university students. Learning logs and questionnaire responses were analyzed to evaluate the ef-fectiveness of the course. Our results indicate a high level of student satisfaction with both the course content and the use of generative AI. Additionally, students demonstrated increased awareness of the importance of verifying AI-generated output and crafting appropriate prompts. These findings suggest that the integration of generative AI and on-demand learning has strong potential to enhance programming education in higher education institutions.

From Catching Pokémon to Catching Truths: Scientific Inquiry Among PokémonGo Gamers

Tieh-huai Chang — 2025-10-02

This study investigates the scientific reasoning practices exhibited by gamers in the early PokémonGo community. Through document analysis of forum posts, it reveals how gamers systematically observed phenomena, formed hypotheses, and tested them using empirical data—particularly in response to gameplay uncertainties. These practices mirror key elements of the scientific method, including experimentation, comparative analysis, and peer validation. The findings highlight how gaming contexts can foster rational inquiry and data-driven collaboration, offering insights into how informal gaming experiences may cultivate foundational scientific literacies.