Journal of Technical Education Science

AI Agents in Legal Research: From Automation to Tailored Responses to Legal Regulations

2026-01-20T16:24:58+07:00

This paper explores the application of AI agents in the legal field, particularly in optimizing the processes of legal research and analysis. The aim of the study is to investigate the potential of AI in enhancing workflows within the legal sector, ranging from automating data collection and legal analysis to predicting the outcomes of legal cases. The research method involves integrating AI agents into the n8n workflow automation platform, an open-source tool, to automate tasks such as searching for legal information, analyzing similar cases, and extracting data from legal databases. The results demonstrate that AI agents not only significantly reduce the time spent on data retrieval and processing but also improve the accuracy of case analysis and legal predictions. These findings suggest that AI can revolutionize how legal professionals and students approach and handle legal information, offering substantial potential to enhance work efficiency within the legal field. AI agents are an essential tool in the legal industry, not only supporting research but also enhancing the quality of legal services. The results of this study open up opportunities for applying AI technology to automate legal processes, particularly in resource-limited environments, helping to reduce costs and improve access to legal research tools for students and legal organizations in under-resourced areas.

Research on Optimizing Omnidirectional Control Algorithms for Patient Care Nurse Robots

2026-04-23T11:29:18+07:00

This research aims to address the instability of localization and navigation systems in service robots operating at high speeds within hospital environments. Rapid fluctuations in velocity often induce mechanical vibrations that distort data from laser scanning sensors, leading to significant errors in map matching and positioning. To overcome this challenge, the paper proposes a method of integrating a first-order low-pass filter into the control loop to smooth linear and angular velocity signals before transmission to the actuators. The approach was experimentally validated using a differential drive nurse robot in a standard corridor scenario with incrementally increasing speed levels. The results demonstrate that the proposed solution significantly improves system reliability, particularly at a velocity of 0.9 meters per second. Specifically, the task completion rate increased substantially from 60% to 92%, while the failure rate in scan matching dropped sharply from 40% to 8%. These figures confirm that suppressing high-frequency components in control commands enhances data overlap, thereby stabilizing the localization process. This solution offers high practical efficiency with low computational cost, making it highly suitable for widespread deployment on autonomous medical robot platforms.

Developing a Training Program to Enhance AI-Integrated STEM Teaching Competence for Pre-Service Teachers

2026-04-29T17:02:51+07:00

The rapid advancement of Artificial Intelligence (AI) is transforming many sectors, including education, and creating an urgent need to innovate STEM education to equip students with competencies required in the digital era. However, many current teacher education programs still lack systematic content and pedagogical approaches that enable pre-service teachers to effectively integrate AI into STEM teaching practices. This study aims to identify the core components of AI-integrated STEM teaching competence and to develop a training program for pre-service teachers. Using a systematic literature review approach, national and international studies related to AI in education, STEM pedagogy, and teacher competence development were collected, analyzed, and synthesized. Based on the review results, the study proposes a four-module training program designed to enhance AI-integrated STEM teaching competence for pre-service teachers. The program includes: (1) an overview of AI and its implications for STEM education; (2) applying AI tools in the design of STEM learning activities; (3) integrating AI into the organization and implementation of STEM teaching; and (4) utilizing AI in educational research and in guiding students’ scientific research projects. The proposed framework provides both theoretical insights and practical guidance for pedagogical universities to design AI-integrated teacher training programs, thereby contributing to educational innovation and improving the quality of STEM teacher preparation in the era of digital transformation.

The Current Situation, Motivations, and Influencing Factors in Faculty of Foreign Languages Students’ Choice to Study Japanese Amid the Expansion of Second Foreign Language Options

2026-04-29T15:40:55+07:00

This study aims to explore the current situation, motivations, and factors influencing the decision to choose Japanese as a Second Foreign Language among English majors at Ho Chi Minh City University of Technology and Engineering (HCMUTE). In this study, a descriptive quantitative approach using questionnaire was filled by 158 respondents who are studying Japanese language now. The results suggest that the appeal of Japanese remains strong. Students are overwhelmingly motivated by love of Japanese culture and career goals. However, students face major barriers stemming from the language’s inherent difficulty and the lack of opportunities to practice outside class hours. Teaching quality and the enthusiasm of the teaching staff are considered key factors that help students overcome difficulties and sustain a positive learning experience. The study proposes practical solutions such as improving teaching materials, implementing a visiting native-speaker instructor model, and establishing extracurricular clubs. These findings contribute to clarify the current situation, motivations, and factors influencing the decision to study Japanese within the context of Second Foreign Language diversification, while simultaneously suggesting directions for curriculum adjustments, support activities, and academic counseling for students.

A Design of Regenerative Braking Model for PMS Motor in Electric Vehicles

2026-05-26T15:16:46+07:00

Regenerative braking is a beneficial feature of electric vehicles compared to traditional vehicles, especially operating in urban traffic conditions. Mechanical energy due to the inertia of the vehicle’s movement is regenerated into electrical energy stored in the battery system for reuse to supply the motor. This paper presents a design of a regenerative braking model for a PMS motor, including a description of the control circuit design, load adjusting circuit, testing setup, and result evaluation. A 3-phase asynchronous motor is used to create the environment as the deceleration speed of the vehicle. The control circuit changes the speed of the driving motor to simulate the deceleration values during braking. The electrical energy generated during regenerative braking will be conducted through a electrical load with capability of varying value. The test results show the energy obtained corresponding to different braking speeds and accelerations.

Active Learning in Civil Engineering: Implementation, Perceived Effectiveness, and Predictors at HCM-UTE

2026-05-26T10:43:57+07:00

As higher education undergoes a paradigm shift toward student-centered pedagogy, adopting active learning approaches has become essential, particularly in engineering disciplines where practical relevance is paramount. This study examines the implementation and perceived effectiveness of active learning within the Faculty of Civil Engineering at the Ho Chi Minh City University of Technology and Engineering (HCM-UTE), drawing on quantitative data from both instructors and students. The results identify Group Discussion and Case Studies as the most prevalent methods, both being highly regarded by both cohorts. However, a notable perception gap exists between instructors and students regarding the extent of Technology-Enhanced Learning (TEL) integration. Regression analysis (Adjusted R² = 0.47) confirms that Case Studies (b =0.35) and Project-based Learning (b =0.27) serve as the strongest predictors of overall learning effectiveness. Conversely, TEL failed to emerge as an independent predictor, suggesting that its pedagogical value is contingent upon being anchored in robust instructional designs tailored to professional contexts. These findings suggest that instructors should prioritize developing authentic learning scenarios to better cultivate professional competencies in students.

Numerical Simulation-Based Design of Experiments for Optimization of Injection Molding Parameters to Minimize Warpage in Plastic Parts

2026-04-29T16:27:13+07:00

This study developed a numerical simulation model of the injection molding process to analyze the influence of processing parameters on product deformation and to determine an optimal parameter set for warpage minimization. The model successfully reproduced the filling, packing, and cooling stages, enabling detailed monitoring of temperature distribution, pressure evolution, and deformation fields throughout the entire molding cycle. The simulation results provide a scientific basis demonstrating that product warpage is primarily governed by two key mechanisms: differential temperature effects and volumetric shrinkage. For Ultramid A3 (PA66), volumetric shrinkage plays a dominant role, directly contributing to post-cooling deformation. By integrating the Design of Experiments (DOE) method with numerical simulation, the optimal processing parameters were determined as follows: melt temperature of 280 °C, mold temperature of 80 °C, packing time of 4 s, and maximum packing pressure of 60%. These conditions ensure a balanced interaction among filling behavior, packing effectiveness, and cooling rate, thereby significantly enhancing dimensional stability and reducing warpage. The proposed approach demonstrates strong effectiveness in injection molding process optimization and exhibits high potential for practical industrial application.

The Impact of Corporate Welfare Policies on Job Satisfaction of Garment Workers

2026-05-12T11:34:54+07:00

This study investigates the effects of corporate welfare policies on job satisfaction among garment industry workers in Vietnam. A quantitative research design was employed, utilizing survey data collected from 240 garment workers at manufacturing enterprises. Exploratory factor analysis, reliability testing, and multiple regression were employed for data analysis. The results indicate that salary and allowances, health insurance and medical care, and working conditions have significant positive impacts on job satisfaction. Conversely, work–life balance policies were found to negatively affect satisfaction. Other welfare factors, including social welfare, training and career development, and recognition and rewards, do not exhibit statistically significant effects. The regression model explains over 80% of the variance in job satisfaction, highlighting the critical role of economic benefits and social security-related welfare in a labor-intensive, low-income context. The findings suggest that improving financial compensation, health-related benefits, and workplace conditions is essential for enhancing job satisfaction and retention among garment workers.

The Impact of a Short-Term Workshop on Students’ Intention to Use 3D Printing Technology in Education: A Case Study

2026-05-12T12:25:39+07:00

3D printing technology has increasingly been applied across various domains of everyday life; however, it still faces certain limitations in educational contexts. Therefore, greater attention should be given to enhancing the intention to use this technology among students in education-related majors. To address this need, this study designed a short-term workshop on the application of 3D printing technology in education and examined its impact on a sample of 30 students studying in Ho Chi Minh City. The results show that the workshop not only helped students develop more positive perceptions of 3D printing technology but also increased their intention to use this technology in the future. In addition, students were able to initially apply this technology to design simple educational activities related to their academic disciplines and provided positive feedback on the workshop. However, they also encountered several challenges, including limited design skills and the insufficient availability of 3D printers during the workshop. These findings suggest the need for further studies to develop high-quality, long-term training programs with adequate infrastructural support in order to optimize learning outcomes for students.

Modeling and Simulation of Lithium-Ion Batteries Using Silicon-Based Anodes

2026-05-28T15:01:02+07:00

Lithium-ion batteries employing silicon-based anodes exhibit significantly higher lithium storage capacity than conventional graphite anodes, thereby improving overall capacity and energy density. However, silicon undergoes substantial volume expansion during repeated charge and discharge cycles, which adversely affects structural integrity, durability, and electrochemical performance. To investigate these effects, a simulation model was developed using MATLAB and Simulink, integrating electrical, thermal, and state-of-charge (SOC) components. The model evaluates Constant Current – Constant Voltage (CC-CV) charging and discharging behavior under different C-rate conditions, including 1C, 3C, and 5C. Simulation results show that voltage response, temperature variation, and SOC dynamics are strongly influenced by both the anode material and applied current load. Silicon-based anodes generate more heat and display greater voltage fluctuations compared to graphite. To address these limitations, several strategies such as surface coatings, hollow structural designs, and silicon–carbon composite materials are proposed to reduce volume expansion and improve stability. Overall, the simulation provides valuable insights for material selection and battery design optimization.