Idris Jeelani

Work zones present unique challenges for traffic regulation and safety management. Highway infrastructure demand is escalating in the United States, leading to an increase in work zones, with thousands of crashes, injuries, and fatalities recorded annually. Traditional safety management systems often fall short in addressing the complexities of dynamic work zone settings. Given the growing body of research on technological applications for safety management in work zones, a comprehensive systematic review that synthesizes insights from previous studies is essential to guide future research, support evidence-based policy decisions, and identify practical implementation gaps. This study systematically reviews 189 peer-reviewed publications from 2000 to 2025, categorizes and critically examines current technology applications in work zone safety, and, using thematic analysis, identifies four dominant research themes: traffic control, safety monitoring, driving behavior assessment, and risk assessment. Advanced systems, such as vision-based and sensor-based hazard detection, connected vehicles, intrusion detection, simulators, and predictive analytics, are also applied across multiple themes but are less mature in deployment. Despite the proliferation of technology solutions, key challenges hinder widespread adoption. These include the limited availability of high-quality, diverse datasets; persistent false alarms and environmental interferences; concerns over data privacy and ownership; and the absence of standardized regulatory frameworks. Future opportunities should focus on integrating AI, computer vision, and real-time data fusion to develop scalable, robust, and adaptable automated safety systems. Advancing these technologies and overcoming current limitations are critical steps toward improving effectiveness, adaptability, and overall impact of work zone safety management strategies.

Digital game-based learning (DGBL) has emerged as a promising method for improving student engagement and learning in STEM education. However, many educational games are designed as serious games, focusing less on engaging elements. Furthermore, DGBL has remained unexplored in the context of materials science and engineering. This study develops and evaluates a novel framework, television media-based educational game (TMEG). The study hypothesizes that integrating elements that make television series engaging into educational games can significantly enhance the learning experience. The study explored: 1) how to integrate TV media elements into educational games and 2) the effect of such integration on learning outcomes. The proposed three-layered TMEG framework includes: an input layer combining MSE content with narrative/design elements from student-preferred TV series; a game cycle layer enabling iterative judgment-behavior-feedback loops that transition students from declarative to applied knowledge; and an outcome layer targeting behavioral, emotional, and cognitive engagement, fostering improved content understanding. A pilot game was developed using the TMEG framework and evaluated with 96 undergraduate students. Data were collected through pre-/post-tests, the system usability scale (SUS), and qualitative feedback. Results showed a statistically significant improvement in test scores (from 74% to 89%) with topic-level scores indicating the highest gains in concepts involving multistep reasoning. The mean SUS score of 76.5 and qualitative feedback indicated strong usability. TMEG offers a novel approach to engineering education by embedding conceptually rigorous content within familiar and engaging media-based narratives. It shows strong potential to improve STEM learning outcomes, particularly in MSE.

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management.