https://ph05.tci-thaijo.org/index.php/JIIST <p class="ng-star-inserted"><span class="ng-star-inserted">The <strong>Journal of Intelligent Informatics and Smart Technology (JIIST) (ISSN: 2586-9167)</strong> operates under the association of <strong>the</strong> <strong>Artificial Intelligence Association of Thailand (AIAT) and </strong><strong>the IEEE SMC Thailand Chapter</strong><strong>.</strong> It aims to be a publisher of a wide range of high-quality academic journals.</span></p> <p class="ng-star-inserted"><span class="ng-star-inserted">The journal welcomes original articles, review articles, short communications, and case reports in the field of artificial intelligence. This includes topics such as Natural Language Processing, Speech Recognition, Machine Learning, Image Processing, Robotics, Semantic Web, Intelligent Computer-Aided Instruction (ICAI), and Expert Systems, among other related fields. All articles are published in English.</span></p> Artificial Intelligence Association of Thailand en-US Journal of Intelligent Informatics and Smart Technology 2586-9167 https://ph05.tci-thaijo.org/index.php/JIIST/article/view/176 <p><span class="ng-star-inserted">Programming education is becoming more essential due to the increasing demands on coding skills and computer literacy. Consequently, many professional schools and universities are providing programming courses to train future programming engineers. However, making quizzes and practice questions for diverse programming topics can be time-consuming for the teachers, reducing the time they can spend on teaching, such as guiding students and providing personalized feedback. In this paper, we propose an AI-driven question generation using ChatGPT to support Python programming education. We developed an automated program that can generate three types of questions to cover basic programming concepts: </span><span class="ng-star-inserted">Code Understanding Questions</span><span class="ng-star-inserted">, </span><span class="ng-star-inserted">Output Prediction Questions</span><span class="ng-star-inserted">, and </span><span class="ng-star-inserted">Code Completion Questions</span><span class="ng-star-inserted"> by using ChatGPT's language model (LM). For evaluations, we have prepared 28 python source codes and applied 140 generated questions to our </span><span class="ng-star-inserted">Language Understanding</span><span class="ng-star-inserted"> lab members in online setting. The examination was conducted as an unsupervised, open-book assessment, allowing participants to reference materials while answering the questions. This setup aimed to simulate a realistic self-learning environment, reflecting how students typically engage with AI-generated questions outside of formal classroom settings. The application results confirm the validity of the proposal and our initial findings indicate that AI-generated questions are effective in supporting both educators and students for Python programming education.</span></p> Khaing Hsu Wai Ye Kyaw Thu Thazin Myint Oo Nobuo Funabiki Lu Xiqin Akihiro Yamamura Copyright (c) 2025 Journal of Intelligent Informatics and Smart Technology 2025-10-31 2025-10-31 1 7 10.14456/jiist.2025.1 https://ph05.tci-thaijo.org/index.php/JIIST/article/view/232 <p>This research aimed to develop and evaluate an information system for managing agricultural equipment loans to enhance the operational processes of the Taluk Klang Thung Organic Rice Farming Group in Tak Province, Thailand. The system development life cycle commenced with requirements elicitation through interviews to understand the group's existing operational workflows. System analysis and design were conducted using Data Flow Diagrams (DFDs), which identified six core processes. The data architecture was subsequently designed to establish relational integrity between tables governing the loan-return cycle and equipment inspection management. The system was implemented using the Django framework with an SQLite database for data persistence. To evaluate the system's efficacy and user acceptance, a satisfaction survey was administered to a sample of 30 group members. The results revealed a high level of overall user satisfaction, with a mean score (M) of 3.93 and a standard deviation (SD) of 0.48. Qualitative feedback indicated that the system was perceived as easy to use, fast, accurate, and effective for managing equipment loans, fostering user confidence. Furthermore, a theoretical validation was performed using Confirmatory Factor Analysis (CFA) to test the measurement model against the constructs of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The analysis confirmed that all observed variables could significantly explain the latent variables of the theoretical models, with factor loadings ranging from 0.75 to 0.86, all of which substantially exceed the accepted threshold of ≥0.70. These findings demonstrate that the developed system is highly suitable for practical deployment and indicate a strong propensity for sustained user acceptance and utilization.</p> Chayuti Mekurai Pattarapon Withayakhun Wanchana Joobanjong Thanin Sinpromma Sindoem Deeto Apichai Suesatsakulchai Copyright (c) 2025 Journal of Intelligent Informatics and Smart Technology 2025-10-31 2025-10-31 8 18 10.14456/jiist.2025.2