https://etd.auburn.edu/handle/10415/1 2026-06-12T11:33:31Z 2026-06-12T11:33:31Z Alathamneh, Shadi https://etd.auburn.edu/handle/10415/10421 2026-06-11T13:46:36Z 2026-06-11T00:00:00Z

Enhancing Preconstruction Education Through Applied Uses of Artificial Intelligence Alathamneh, Shadi This dissertation examines the intersection of Building Information Modeling (BIM)-based Quantity Takeoff (QTO), preconstruction education, and customized generative AI tools to address the growing need for digital upskilling in construction management (CM), industry skill gaps, and changing expectations for graduate competencies. The study was guided by three related research questions. First, a systematic review was conducted using the PRISMA method to examine the current state of BIM-based QTO research. The review found that BIM-based QTO can improve productivity, accuracy, and completeness in estimating tasks, while challenges remain related to professional skills, CM graduate competencies, and software functionality. Second, the dissertation developed and evaluated DrCGPT, a custom AI teaching assistant created in Microsoft Copilot Studio to support undergraduate students in construction estimating tasks. Student feedback indicated neutral-to-positive experiences and suggested that the AI agent was a useful learning resource, although students continued to value interaction with instructors and peers. Third, the dissertation examined the use of AI agents as primary instructional tools for asynchronous BIM-based QTO learning. Findings indicate that dialogue-tree-based AI agents, supported by controlled knowledge bases, successfully guided students through structured technical tasks and supported self-paced learning, while limitations remained in flexibility and contextual understanding. Overall, this dissertation provides a case-based framework for educators and industry professionals with limited programming experience to responsibly integrate AI agents into teaching and upskilling practices.

2026-06-11T00:00:00Z Preston, Thomas https://etd.auburn.edu/handle/10415/10420 2026-06-10T13:29:31Z 2026-06-10T00:00:00Z

Examination of Acute Stress on a Neurophysiological Indicator of Incentive Salience in Cannabis Users Preston, Thomas Cannabis is one of the most widely used illicit substances in the United States, in part due to recently increased accessibility and inaccurate perceptions regarding negative health effects associated with use. Rates of Cannabis Use Disorder are expected to increase, lending to more research determining markers of disordered use, which may be targeted for intervention. Broadly, chronic substance use is associated with neurophysiological alterations in reward processing, such that the relative motivational value of stimuli indicative of substance use is greater than benign or even naturally-rewarding cues. Further, repeated substance use is known to alter how the body responds to stress, impacting attentional engagement towards substance relevant and irrelevant cues, as well as substance-related craving. Despite a breadth of literature spanning this topic, there remains a dearth of research examining how acute stress impacts neurophysiological reward processing in people with Cannabis Use Disorder. The current study sought to address this gap by measuring a neurophysiological marker of early attentional engagement, the P300, to cannabis, naturally-rewarding, and neutral pictures after an acute lab-induced stressor. Results were partially consistent with models of addiction, whereby acute stress diminished P300 amplitude to neutral and naturally-rewarding images but did not affect P300 amplitude to cannabis images. These novel findings offer insight into how those with Cannabis Use Disorder react to various cues during acute stress induction and provide future avenues of potential intervention.

2026-06-10T00:00:00Z Qasaimeh, Qais https://etd.auburn.edu/handle/10415/10419 2026-06-08T18:25:20Z 2026-06-08T00:00:00Z

Reliability Analysis and Mechanical Characterization of Lead-Free Solders Using Interpretable Machine Learning Qasaimeh, Qais Predicting solder joint reliability and efficiently characterizing the mechanical properties of lead-free solder materials remain critical challenges in the electronics industry. Accurate reliability prediction is complicated by the large number of design and process parameters, as well as the complex nonlinear interactions among thermal, mechanical, and material-related factors that govern solder behavior under harsh operating conditions. Traditional reliability assessment methods, including analytical and numerical approaches, often rely on simplifying assumptions that limit their ability to capture these nonlinear relationships, thereby reducing prediction accuracy. At the same time, conventional mechanical characterization techniques, such as tensile, creep, and shear testing, are experimentally intensive, time-consuming, and resource-intensive. These challenges highlight the need for efficient, interpretable, and data-driven methodologies to improve both reliability prediction and mechanical characterization of lead-free solder materials. Recently, machine learning (ML) techniques have emerged as promising tools for addressing complex reliability and material characterization problems in electronics manufacturing. Their ability to model nonlinear relationships and incorporate numerous interacting variables has enabled more accurate prediction of solder joint behavior compared to conventional approaches. However, despite their predictive capabilities, many ML models suffer from limited interpretability, which reduces confidence in their predictions and limits their adoption in reliability-critical applications. Therefore, developing interpretable and robust data-driven frameworks is essential for enabling trustworthy decision-making in solder reliability assessment and material development To address these challenges, this dissertation develops a sequential data-driven framework through three interconnected studies. The first study establishes an interpretable reliability-prediction framework for solder joints subjected to thermal cycling by integrating linear mixed-effects (LME) models and artificial neural networks (ANNs) with sensitivity analysis. By leveraging both experimental and simulated reliability data, this study demonstrates how interpretable ML models can accurately predict solder joint life while simultaneously identifying the critical factors governing reliability behavior. The extracted insights align with empirical knowledge and experimental observations, providing a transparent foundation for data-driven reliability assessment. Building on this foundation, the second study addresses one of the major limitations in accelerated life testing (ALT): the handling of censored reliability data. In this study, advanced survival ML methods, including random survival forests (RSF), survival support vector machines (SSVM), and extreme gradient boosting (XGB), are employed and compared against traditional survival analysis models such as Weibull regression and the Cox proportional hazards model (Cox-PHM). Furthermore, Shapley additive explanations (SHAP)-based interpretability analysis is incorporated to explain the influence of input variables on reliability predictions. The results demonstrate that survival ML methods provide superior predictive performance and improved modeling of complex nonlinear reliability behavior under censored conditions. Finally, the third study extends these data-driven methodologies to efficiently characterize lead-free solder alloys by introducing a transferability-guided transfer learning (TL) framework for predicting tensile strength. In this framework, knowledge learned from data-rich conventional solder alloys is transferred to newly developed, data-scarce alloys, enabling accurate mechanical property prediction with limited experimental data. The proposed TL framework is benchmarked against multiple conventional ML approaches and demonstrates improved robustness and predictive accuracy in low-data scenarios. In addition, transferability and interpretability analyses provide insights into the effectiveness of knowledge transfer and the physical consistency of the learned relationships. Collectively, these three studies establish a comprehensive, interpretable, data-driven framework that progresses from solder joint reliability prediction to censored reliability modeling, and finally to mechanical property characterization and alloy development. The proposed methodologies accelerate the evaluation, optimization, and adoption of high-reliability lead-free solder materials in advanced electronic packaging applications.

2026-06-08T00:00:00Z Bakir, Selim https://etd.auburn.edu/handle/10415/10418 2026-06-05T20:05:16Z 2026-06-05T00:00:00Z

A Mixed-methods Multi-study Approach to Unveil the Implications of AI Integration for Employee Outcomes in the US Hotel Industry Bakir, Selim Artificial intelligence (AI) has emerged as a pivotal technology reshaping workplace dynamics and influencing employee behavior in the hotel industry. While existing research has largely emphasized operational efficiency and technological outcomes, a broader understanding of how employees perceive, experience, and respond to AI adoption remains limited. Addressing this gap, this dissertation employs a mixed-methods approach across three interrelated studies to examine the implications of AI for employee attitudes, behaviors, and managerial practices in the U.S. hotel industry. Drawing on the Transactional Theory of Stress and Coping, the Job Demands–Resources (JD–R) model, and Conservation of Resources (COR) theory, this research integrates quantitative and qualitative insights to uncover the psychological and organizational mechanisms through which AI shapes employee outcomes. Survey data collected from 413 hotel employees in the US reveal that different approaches to AI implementation produce divergent employee responses. Specifically, AI adoption is associated with lower levels of quiet quitting, whereas AI integration is linked to higher levels of withdrawal behaviors. Competitive psychological climate emerges as a key mediating mechanism, whereas technostress does not explain these relationships in this context. Complementing these findings, qualitative evidence from semi-structured interviews with 19 hotel managers demonstrates that AI adoption significantly influences employees’ career development, well-being, and work outcomes. Managers report shifts in employees’ career perceptions, including turnover intentions, job search behaviors, and career satisfaction, as well as both positive outcomes (e.g., improved efficiency) and negative outcomes (e.g., strain and techno-anxiety). Managerial support and transparent communication are identified as critical factors in mitigating adverse effects. Further quantitative analyses show that perceived AI job-replacement threat is positively associated with technostress and turnover intention, with technostress serving as a key mechanism linking AI-related threats to turnover intention and service sabotage. Collectively, this dissertation advances theoretical understanding of AI-enabled workplaces by clarifying how AI-related demands and resource threats influence employee withdrawal and deviant behaviors. The findings offer actionable insights for hospitality organizations seeking to implement AI in ways that support employee well-being and promote sustainable service performance. Keywords: AI Threat, Hotel Employees, Competitive Psychological Climate, Technostress, Quiet Quitting, Service Sabotage

2026-06-05T00:00:00Z