Artificial Intelligence and Robotics Commons^™

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Artificial Intelligence Use In Medical Education: Best Practices And Future Directions, Rasheed A. M. Thompson, Yash B. Shah, Francisco Aguirre, Courtney Stewart, Costas D. Lallas, Mihir S. Shah

Department of Urology Faculty Papers

PURPOSEOF REVIEW: This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS: AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in …

A Novel Approach To Attention-Based Models In Image Completion: Weighted Spatial-Attention Using Radial Distance, Tyler D. Kuper

Electrical Engineering and Computer Science Undergraduate Honors Theses

Humans infer missing visual information by focusing on spatial relationships in the context of their surroundings. Machine learning aims to replicate this skill through image completion, a fundamental task in current computer vision research. While advances in self-attention layers have recently enhanced generative machine learning models for text, these mechanisms still currently lack the capability to handle sparse image completion efficiently. We introduce a distance-based attention mechanism that uses radial-based weights to efficiently reconstruct an image. We compare this attention mechanism with self-attention and a fully connected network on an image completion task using the MNIST dataset. Our results show …

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Toward Robust Semantic Segmentation In Levee Infrastructure Monitoring: Enhancing Accuracy With High-Fidelity Synthetic Data And Ensemble Learning, Padam Jung Thapa

LSU New Orleans Theses and Dissertations

Abstract: Levees serve as critical flood protection structures, but failures due to inadequate maintenance and extreme water pressures have led to devastating events such as Hurricane Katrina. Manual inspections are slow, labor-intensive, and prone to human error, necessitating the development of automated solutions. This study proposes an AI-driven framework for levee inspection utilizing deep learning-based semantic segmentation to detect rutting and enhance the identification of sand boils. To address dataset limitations, high-fidelity synthetic images are generated using DreamBooth for fine-tuning, while ControlNet adds structural constraints to enhance realism and consistency. A semi-automatic convex hull annotation technique enhances labeling efficiency, and …

Privacy-Aware Ai-Based Agricultural Monitoring Using Internet Of Drones, Md Benozir Hossain

Honors Theses

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by …

Multimodal Learning For Visual Perception And Robotic Action, Taisei Hanyu

Electrical Engineering and Computer Science Undergraduate Honors Theses

Multimodal learning aims to weave information from images, language, depth, and other sensors into one coherent representation, much as people naturally combine sight, speech, and sound. Progress toward that goal is slowed by three gaps: vision encoders that cannot balance crisp object boundaries with global context, 3-D semantic maps that are computationally prohibitive for real-time, open-vocabulary queries, and vision-language-action pipelines that depend on large token pools with weak relational grounding.

We first introduce AerialFormer, a lightweight hybrid of convolutional and Transformer layers that captures long-range structure without sacrificing fine detail. On the large-scale iSAID benchmark it reaches 69.3% mean IoU, …

Understanding Automation From A Computer Science Perspective, Matthew Donsig

Honors Program: Senior Projects (Public)

This thesis looks into automation and analyzes its benefits and problems. It begins with an explanation of a capstone project, automating the UNL State Museum’s reservation system. Problems of automation are presented in unsuccessful attempts and some pitfalls of automation. Next this thesis turns to an examination of artificial intelligence in automation. Along with that, we look at bias in automation and how people can bias automated tools or be biased by them. Turning successful examples of automation and then automation in manufacturing shows its benefits. Automation creates new jobs or changes work as much as it eliminates positions. At …

Oculomics: Current Concepts And Evidence, Zhuoting Zhu, Yueye Wang, Ziyi Qi, Wenyi Hu, Xiayin Zhang, Siegfried K Wagner, Yujie Wang, An Ran Ran, Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Alex Suh, Yih Chung Tham, Carol Y Cheung, Xiaohong Yang, Honghua Yu, Zongyuan Ge, Wei Wang, Bin Sheng, Yun Liu, Andrew G Lee, Alastair K Denniston, Peter Van Wijngaarden, Pearse A Keane, Ching-Yu Cheng, Mingguang He, Tien Yin Wong

Faculty, Staff and Student Publications

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo

Research Collection School Of Computing and Information Systems

Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Extending Feature-Based Detection For Artificial Intelligence, Kayla Ahrndt

SPARK Symposium Presentations

AI text generation is rapidly developing, and, as a result, it is becoming increasingly difficult to differentiate it from human written text. Our base study by Leon Fröhling et al. proposed a feature-based detection model trained on GPT2, GPT3, and Grover data, as well as human-generated text. Our work extends their research by training a modified model with four neural networks on word embeddings, select features from the original study, as well as updated data (GPT3, GPT4, and Grover).

The Asset Management Optimization Engine: An Ai And Machine Learning Model Approach To Pavement Asset Management, Matt Versdahl

USF Tampa Graduate Theses and Dissertations

While state Departments of Transportation (DOT) face major funding challenges, the need to find optimal ways to preserve and maintain pavement assets remains. Asset management employs a lowest cost lifecycle method to analyze asset costs and determine the best investment strategies to preserve it throughout its lifecycle. As new technology emerges, so do opportunities to leverage it. DOTs collect a significant amount of performance data on pavement and use it to decide how to keep it in a state of good repair. The literature in this area focuses on engineering techniques applied to treatment strategies. This dissertation research focuses on …

The Present And Future Of Ai: Ethical Issues And Research Opportunities, Ankita Srivastava, Marco Marabelli, Danielle Blanch-Hartigan, Jeffrey Moriarty, Evan Carey

Computer Information Systems Faculty Publications

Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with …

Adapting To Ai: The Evolving Role Of Faculty In Higher Education, Ronald R. Danault

Faculty Publications

Artificial intelligence (AI) is changing the face of higher education, and there are important issues regarding the future of the faculty (Stoerger, 2024). Although there are concerns about the impact of AI on the conventional faculty roles in teaching, assessment, and administration, these tools are now being adopted in learning processes. Rather than dismissing AI as a threat, it acts as a catalyst for reshaping the way faculty members teach with the help of AI and, hence, become facilitators of the learning process (Haoyang & Towne, 2025).

This paper aims to discuss the integration of AI in the higher education …

Investigating Key Structures In Protective Scenes For Llms, Eben M. Weisman

University Honors Theses

This research delves into the realm of "protective scenes" within Large Language Models (LLMs), exploring their impact on bias mitigation, deception, and context preservation. The study investigates the use of roleplay prompting human-like behavior and reasoning in LLMs, focusing on the Character-LLM framework's concept of protective scenes with graduated levels of protection. By combining insights from psychology, cognitive science, and computational analysis, this research aims to develop a framework for understanding how protective scenes influence roleplay performance in LLMs, ultimately contributing to the development of more reliable and ethical AI systems.

The Present And Future Of Ai: Ethical Issues And Research Opportunities, Ankita Srivastava, Marco Marabelli, Danielle Blanch-Hartigan, Jeffrey Moriarty, Evan Carey

Philosophy Faculty Publications

Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with …

Exploring Intelligent Manufacturing: How Artificial Intelligence Affects Productivity And Labor Demand At The Enterprise Level, Jie Gu

Dissertations and Theses Collection (Open Access)

Manufacturing is a cornerstone of national economic health and social stability, yet it faces challenges such as declining profits, rising labor costs, and an aging workforce. In China, the manufacturing sector is undergoing a critical transformation, driven by technological advancements like artificial intelligence (AI) and the push for intelligent manufacturing. This study explores how AI revitalizes the manufacturing sector by enhancing enterprise productivity and reshaping labor demand, with a focus on quality inspection processes. Using a leading bearing factory as a case study, the research employs econometric models, A/B testing, and interviews to quantify AI’s impact on production efficiency, costs, …

Patient Consent And The Right To Notice And Explanation Of Ai Systems Used In Health Care, Meghan E Hurley, Benjamin H Lang, Kristin Marie Kostick-Quenet, Jared N Smith, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions …

What's The Art In Artificial Intelligence?, Emily Verla Bovino

Open Educational Resources

This workbook learns from Black and Indigenous artists working with Artificial Intelligence to confront issues of ethics and aesthetics in its technologies. It features guided learning activities with links to publicly available video lectures and online articles, and includes options for experiential learning through both a tutorial in Midjourney and a visit to the public art collection at York College in Jamaica, Queens. Featured artists include: American Artist, Rizvana Bradley, Beth Coleman, Denise Ferreira da Silva, Suzanne Kite, Sondra Perry, Mimi Onuoha and Alisha B. Wormsley. Works by Martin Puryear and Maren Hassinger are explored in the Midjourney exercise.

About …

The Evolution And Future Of Microservices Architecture With Ai-Driven Enhancements, Jill Willard, James Hutson

Faculty Scholarship

Microservices architecture has revolutionized software development by enabling the decomposition of monolithic applications into smaller, more manageable services. While this shift has reduced risks and enhanced system resiliency, the increasing complexity of managing numerous microservices presents new challenges. As Artificial Intelligence (AI) continues to evolve, there is a growing need to explore how autonomous AI agents can optimize microservices architectures, particularly in terms of communication and workflow orchestration. The purpose of this study is to investigate how AI agents can autonomously interact and manage microservices, reducing human intervention and enhancing system efficiency. The key research question guiding this investigation relates …

A Review Of Artificial Intelligence, Algorithms, And Robots Through The Lens Of Stakeholder Theory, Michael J. Matthews, Su Runkun, Lindsey Yonish, Shawn Mcclean, Joel Koopman, Kai Chi Yam

Management Faculty Publications

With the arrival of the Fourth Industrial Revolution, intelligent machines are affecting the daily lives of multiple organizational stakeholders. However, despite the continued expansion of intelligent machines in society, management scholarship has generally lagged, and current frameworks are under-equipped to offer meaningful guidance regarding the intersection of intelligent machines and organizations. We address this issue via a multidisciplinary review and a novel framework of intelligent machines and value creation. First, we discuss the characteristics of intelligent machines (i.e., autonomy, learning, inscrutability, and materiality) and how variation in these characteristics impacts their affordances and, subsequently, the value offered to stakeholders. We …

Seven Hci Grand Challenges Revisited: Five-Year Progress, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Vincent G Duffy, Qin Gao, Waldemar Karwowski, Fiona Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Keng Siau, Jia Zhou

Research Collection School Of Computing and Information Systems

Motivated by the rapid technological advancements achieved in the last five years, and the pervasiveness of Artificial Intelligence, the paper investigates the evolving role of Human-Computer Interaction and revisits the seven grand challenges outlined in 2019: human-technology symbiosis, human-environment interactions, ethics, privacy and security, well-being, health and eudaimonia, accessibility and universal access, learning and creativity, and social organization and democracy. Through literature analysis, the paper reevaluates the status of each challenge and highlights emerging requirements. Key findings reveal the widespread impact of Artificial Intelligence across all domains and emphasize the need for improved AI transparency, alignment with human values, and …

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Philosophy Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Navigating Artificial Intelligence: How Traditional Midwestern Four-Year Higher Education Institution Distance Learning Programs Are Addressing Artificial Intelligence, Eric Samaritoni

Theses, Dissertations and Capstones

AI's rapid evolution and integration into society has had and will continue to influence how distance education programs teach their students profoundly. This study aimed to explore the opinions of higher education distance education Provost administrators, program managers, directors, department chairs, and other subject matter experts on the perceived impact of AI in the classroom. This study used a qualitative, phenomenological approach to examine how AI impacts students, faculty, program delivery, institutional policies, and university budgets. Semistructured interviews were conducted with 13 faculty members meeting the criteria to answer research questions based on their experience or observations. The study used …

A Deep Reinforcement Learning Framework For Sequential Art Creation, Asmin Pothula

Computer Science and Engineering Theses - Archive

Most computational art systems rely on generative models that produce a complete artwork in a single pass, without capturing the gradual, decision-driven process through which human artists construct visual pieces. Prior research in sequential, stroke-based image generation, including differentiable neural painters and model-based reinforcement learning agents, has explored step-by-step creation, but these systems typically aim to reconstruct the input image within the same visual representation space, closely matching brushstrokes, textures, or colors to the target. In contrast, this thesis investigates sequential art creation in a different artistic representation, where the final artwork does not share the same visual form as …