Artificial Intelligence and Robotics Commons^™

Labor Market Responses To Ai: Measuring Wage Effects Across U.S. Occupations, Kaitlin Pham, Karla Rodriguez

Undergraduate Economics Working Paper Series

Artificial intelligence (AI) is rapidly changing economies around the world, with some experts predicting an impact greater than the Industrial Revolution. As AI becomes more common in daily life and business, questions have grown about how it might affect jobs, wages, and inequality. The rise of automation and highly capable AI models has made people wonder which occupations will benefit and which might be at risk. This study looks at how exposure to AI technologies affects wage trajectories in the United States. Using occupational-level data from O*NET and the U.S. Bureau of Labor Statistics, we build an AI exposure index …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Ai Literacy: An Annotated Oer Bibliography, Houy Yvonne

UNLV Best Teaching Practices Expo

"Scalable, discipline-agnostic AI literacy instruction can be implemented incrementally without requiring full course redesign in higher education, supporting both technical understanding and critical engagement with the social and ethical dimensions of AI: The curated list of open educational resources (OER) on this poster enable a flexible, modular approach to teaching foundational AI literacy. The annotated list includes self-paced, hands-on projects with complementary educator-guided activities and discussion to support conceptual understanding of machine learning, training data, and algorithmic bias, drawing on OER such as Code.org’s AI curriculum, MIT RAISE’s Day of AI, and the multi-lingual Elements of AI course. Many resources …

Ankimedbench: Evaluating Hierarchical Medical Knowledge In Language Model Embeddings, Neel Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Despite achieving over 90% accuracy on medical benchmarks, recent studies show physicians cannot effectively leverage language models to improve clinical reasoning. Current benchmarks test isolated factual recall, but clinical practice requires hierarchical navigation through diagnostic categories—starting broad and narrowing systematically from chest pain to cardiovascular pathology to myocardial infarction to specific STEMI types. Existing evaluations cannot measure whether models preserve this taxonomic structure essential for clinical reasoning.

We introduce AnkiMedBench, built from 16,512 medical flashcards used by students preparing for licensing exams. Cards are organized across six hierarchy levels spanning 16 broad medical specialties to 672 specific diseases and conditions. …

The Artificial Intelligence (Ai) Economy In The Mountain West, 2025, Dre Boyd-Weatherly, Sean Curry, Krish Sharma, Kristian Thymianos, William E. Brown Jr.

Economic Development & Workforce

This fact sheet reports on the artificial intelligence (AI) readiness of five Mountain West metropolitan statistical areas (MSAs): Phoenix-Mesa-Chandler, AZ; Salt Lake City-Murray, UT; Denver-Aurora-Centennial, CO; Las Vegas-Henderson-North Las Vegas, NV; and Albuquerque, NM. Using data from the Brookings Institution's “Mapping the AI Economy” report, the MSAs are benchmarked based on their overall population, employment, talent, adoption, and innovation.

Preparing For The Artificial Intelligence (Ai) Economy In The Mountain West, 2025, Kian Parikh, Taylor Volk, Maisoon Faris, Kristian Thymianos, William E. Brown Jr.

Economic Development & Workforce

This fact sheet presents data from the Brainly report, “Here Are the States Most (and Least) Prepared to Win the AI Race in 2025” for the five Mountain West states of Arizona, Colorado, New Mexico, Nevada, and Utah. This fact sheet highlights the national and individual rankings of four key metrics for each Mountain West state: the fixed percentage of businesses using artificial intelligence (AI); the number of AI jobs per 1,000 workers; the number of AI-related degrees per 10,000 people ages 20-24; and federal funding for small business technology innovation per $1 million of gross domestic product (GDP).

Artificial Intelligence (Ai) And The Anthropic Economic Index In The Mountain West, 2025, Cason Noll, Olivia K. Cheche, William E. Brown Jr.

Economic Development & Workforce

This fact sheet presents 2025 data on the state of artificial intelligence (AI) adoption among the five Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The data are sourced from the “Anthropic Economic Index,” which provides data on Claude.ai (an AI large language model) and its adoption across all 50 U.S. states and Washington, D.C. This fact sheet focuses on Claude.ai usage, the most common topic Claude.ai has been used for, and augmentation and automation shares for each Mountain West state.

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

"Opting Out Of Ai”: Exploring Perceptions, Reasons, And Concerns Behind Faculty Resistance To Generative Ai, Aya Shata

Hank Greenspun School of Journalism and Media Studies Faculty Research

Research on Generative Artificial Intelligence (GAI) in higher education primarily focuses on faculty use and experiences, with limited attention given to why some abstain from using it. Drawing from Innovation Resistance Theory, this study aims to address this gap by exploring the perceptions of both faculty users and non-users of GAI, identifying the reasons and concerns why they avoid GAI. A survey of 294 full-time higher education faculty from two mid-size U.S. public universities was conducted. Using qualitative and quantitative analysis, results show that over one-third of the faculty members opted out of using GAI for five primary reasons: not …

Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian

UNLV Theses, Dissertations, Professional Papers, and Capstones

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …

Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …

Designing Ai-Driven Dining: A Ux Approach To Enhancing The Self-Service Experience, Kaylin Joung, Yuki Hayashi, Dailuaine Esguerra

Undergraduate Research Symposium Posters

Artificial intelligence has transformed many industries, yet its integration into self-dining experiences is still emerging. This research explores how AI can enhance self-dining by introducing technology like interactive kiosks and robot servers to improve efficiency, personalization, and customer convenience. By addressing current gaps, we aim to create a more seamless and engaging dining experience.

Reframing Information Seeking In The Age Of Generative Ai: A Critical And Humanistic Approach, Joseph Kevin Sebastian

Library Faculty Research

Information-seeking has long been the subject of theoretical modeling, often drawing from cognitive, behavioral, computational, and even evolutionary perspectives to explain how individuals navigate, filter, and utilize information. Several dominant frameworks—Carol Kuhlthau’s Information Search Process, Marcia Bates’ Berrypicking Model, Peter Pirolli & Stuart Card’s Information Foraging Theory, Kiyohiko Nakamura’s Information Criteria framework, and Ian Ruthven’s Information Shaping Theory —have provided structured ways of understanding how people interact with information environments. However, while these frameworks offer valuable insights, they often operate within mechanistic or efficiency-driven paradigms, which risk overlooking the complex, embodied, and socioculturally situated nature of human information behaviors. These …

Artificial Intelligence And Communication Technologies In Academia: Faculty Perceptions And The Adoption Of Generative Ai, Aya Shata, Kendall Hartley

Hank Greenspun School of Journalism and Media Studies Faculty Research

Artificial intelligence (AI) is ushering in an era of potential transformation in various fields, especially in educational communication technologies, with tools like ChatGPT and other generative AI (GenAI) applications. This rapid proliferation and adoption of GenAI tools have sparked significant interest and concern among college professors, who are dealing with evolving dynamics in digital communication within the class-room. Yet, the effect and implications of GenAI in education remain understudied. Therefore, this study employs the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT) as theoretical frameworks to explore higher education faculty’s perceptions, attitudes, usage, and motivations, as the underlying …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Autonomous Driving Trajectory Prediction, Carlos Funes

Undergraduate Research Symposium Lightning Talks

Autonomous driving is undoubtedly one of the world's most revolutionary technologies, opening the door to a more secure traffic environment. This innovation has led to vehicles being able to drive by themselves without the necessity of a person behind the wheel, as well as cruise control, lane-keeping assist, and automatic emergency braking. Unfortunately, there is still plenty of work before autonomous driving becomes more popular among drivers. While at UNLV as an undergraduate student/research assistant, one of my goals is to learn how these technologies work to bring ideas into the automotive industry by refining solutions to problems within these …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Intersections Of Living And Machine Agencies: Art-Based Models Of Adaptive Conversation With The More-Than-Human World, Carlos Castellanos

Tradition Innovations in Arts, Design, and Media Higher Education

Today’s AI systems are not built to have reciprocal interplay with their environments and thus they demonstrate little interest in emergence, adaptation or developing mutually productive relationships with the natural world. Is a different kind of AI possible? How can artists contribute to its development? In this essay, I will discuss ways in which the arts might help guide us towards a new kind of AI, built upon adaptive conversation (i.e. shared construction of meaning) with nature. I will discuss how can work with AI while also challenging its prevailing ontology, and even suggest alternative ontologies and epistemologies. I will …

Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Water droplet behavior on soil surfaces plays a critical role in numerous environmental processes, including soil erosion, hydrological dynamics, and ecosystem health. Accurate characterization of soil water repellency, quantified by parameters such as water droplet penetration time (WDPT) and contact angles (WDCA), is essential for informed decision-making in agricultural management, forestry practices, and land-use planning. Despite the significance of these parameters, challenges exist in reliably estimating them due to the complex and dynamic nature of soil-water interactions. This thesis address challenges in estimating WDPT and WDCA, by leveraging state-of-the-art image processing techniques and machine learning algorithms. The research focuses on …

Experiment Development And Validation Of A Granular Jamming Robotic Gripper, Jacob R. Dowd

UNLV Theses, Dissertations, Professional Papers, and Capstones

A granular jamming gripper (GJG) is widely known as a Universal Gripper because of the wide range of objects that it can grasp and the simplicity of control, design, and manufacturing. Despite multitude of research improving the GJG, here, we focus on the base version of the GJG and attempt to glean the range of objects that it may reliably grasp. Despite the limited range of objects, which were a sphere, rectangular prism, and cylinder, we gleaned geometric properties as it relates to successful and unsuccessful grasping. This was based on the two types of testing: push and pull testing …

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Evocative And Provocative Image-Making In The Age Of Generative Ai, Julian Kilker

Tradition Innovations in Arts, Design, and Media Higher Education

Editorial for inaugural AI-focused special issue of Tradition-Innovations in Arts, Design, and Media Higher Education, published under the auspices of the Alliance for the Arts in Research Universities (a2ru). Discusses three articles by five authors in this issue: (1) Choreographing Shadows: Interdisciplinary collaboration to orchestrate ethical image-making by Mark Burchick and Diana Pasulka; (2) Giving Up Control: Hybrid AI-augmented workflows for image-making by Joshua Vermillion; and (3) Hands are Hard: Unlearning how we talk about machine learning in the arts by Adam Hyland and Oscar Keyes.

Editing this special issue explored several key questions: What does “innovation” mean when …

Ocr Post-Processing Using Large Language Models, Mahdi Hajiali

UNLV Theses, Dissertations, Professional Papers, and Capstones

Optical Character Recognition (OCR) technology transforms textual visuals into an electronically readable, non-graphical format of the text. This allows the editing and other text manipulation of the content by language technology software such as machine translation, text comprehension, query-answering systems, and search engines. While Optical Character Recognition (OCR) systems continually progress towards greater precision, several complications persist when dealing with low-resolution source images or those with multicolored backgrounds. Consequently, the text derived from OCR necessitates additional refinement to optimize accuracy, beneficial for various subsequent applications. It is recognized that the character accuracy of OCR-generated text may influence certain natural language …

Facial Expression Recognition Using Convolutional Neural Networks (Cnns) And Generative Adversarial Networks (Gans) For Data Augmentation And Image Generation, Shekhar Singh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature …

A Mechanically Intelligent Hosing-Drone, Blake Hament

UNLV Theses, Dissertations, Professional Papers, and Capstones

This manuscript presents a ”mechanically intelligent” approach to designing a Hosing-Drone for heavy-duty pressure washing. Spraying a hose creates strong reaction forces and torques. Previously demonstrated spraying robots are over-engineered to be very massive with huge inertias. These high inertias ”wash out” the reaction from the spraying. In the proposed approach, the contributions from all observable fluid dynamics, fluid structure interactions, and aerodynamics are studied individually and for the coupled system. Experimental data is collected and fit to dynamic models. These models are used to design a smaller, lighter, more agile vehicle than has been previously demonstrated. An impedance controller …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virginia B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …