Journal of Intelligent Informatics and Smart Technology

Green Logistics InfoBot: A Comprehensive Carbon Footprint Information and Guidance System.

Pachara Thangpromphan, Nattavee Utakrit — Fri, 31 Oct 2025 00:00:00 +0700

Thailand's logistics sector contributes approximately 30% of the country's total carbon emissions, presenting a critical challenge for achieving the nation's carbon neutrality target by 2050. This research proposes the development of the Green Logistics InfoBot Framework, an AI-powered comprehensive information and guidance system designed to address the significant knowledge gap between environmental awareness and practical carbon reduction implementation in Thailand's logistics industry.

The proposed framework integrates five interconnected core components to deliver intelligent environmental guidance: (1) Data Integration Layer incorporating real-time logistics emission databases, fuel consumption metrics, and Thai government regulatory frameworks including the Thailand Taxonomy for Sustainable Activities and Royal Decree on Environmentally Friendly Vehicles; (2) Intelligent Processing Framework featuring multilingual natural language processing (Thai-English), machine learning algorithms for personalized carbon reduction strategies, and predictive analytics optimized for Southeast Asian supply chain patterns; (3) Knowledge Management System containing comprehensive repositories of Thailand's green logistics regulations, best practice databases from successful Thai enterprises, and integration protocols with the country's expanding electric vehicle infrastructure under the EV 30@30 policy; (4) User Interaction Interface providing conversational AI chatbot capabilities, interactive carbon footprint calculators tailored to Thai logistics operations, route optimization considering Thailand's unique geographical constraints, and multi-platform accessibility for diverse operator scales from small freight forwarders to major distribution centers; and (5) Decision Support Framework delivering evidence-based recommendations aligned with Thai regulatory requirements, cost-benefit analysis incorporating local fuel prices and carbon credit market dynamics, and performance tracking systems compatible with existing Thai logistics management platforms.

The framework addresses specific challenges within Thailand's logistics ecosystem, including fragmented environmental information, complex regulatory compliance requirements, limited access to carbon reduction technologies among small-medium enterprises, and insufficient integration between traditional logistics operations and emerging sustainable practices. By leveraging artificial intelligence and localized content delivery, the system aims to democratize access to carbon footprint management tools across Thailand's diverse logistics sector, from international shipping companies in Laem Chabang Port to regional distribution networks serving rural provinces.

This research contributes to Thailand's sustainable development goals by proposing a scalable, technology-driven solution that bridges the critical information gap between environmental policy and operational implementation. The framework's design prioritizes practical applicability within Thailand's existing logistics infrastructure while supporting the transition toward sustainable logistics systems aligned with national carbon neutrality commitments and regional ASEAN sustainability initiatives.

A Study on AI-Driven Question Generation Using ChatGPT for Python Programming Education

Fri, 31 Oct 2025 00:00:00 +0700

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: Code Understanding Questions, Output Prediction Questions, and Code Completion Questions by using ChatGPT's language model (LM). For evaluations, we have prepared 28 python source codes and applied 140 generated questions to our Language Understanding 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.

Development of An Agricultural Equipment Circulation Data Management System for Organic Rice Large-Scale Farming Group

Fri, 31 Oct 2025 00:00:00 +0700

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.