Artificial Intelligence and Robotics Commons^™

Rethinking Ai Literacy Education In Higher Education: Bridging Risk Perception And Responsible Adoption, Shasha Yu, Fiona Carroll, Barry L. Bentley

School of Professional Studies

As AI becomes increasingly embedded across societal domains, understanding how future AI practitioners—particularly technology students—perceive its risks is essential for responsible development and adoption. This study analyzed responses from 139 students in Computer Science, Data Science/Data Analytics, and other disciplines using both explicit AI risk ratings and scenario-based assessments of risk and adoption willingness. Four key findings emerged: (1) Students expressed substantially higher concern for concrete, explicitly stated risks than for abstract or scenario-embedded risks; (2) Perceived risk and willingness to adopt AI demonstrated a clear inverse relationship; (3) Although technical education narrowed gender differences in risk awareness, male students …

Ordered Mini-Batch Training For Differentially Private And Encrypted Logistic Regression, Ryan Leone

Theses, Dissertations and Culminating Projects

Logistic regression has found extensive use as a supervised machine learning algorithm due to its simplicity and efficiency in binary and multivariate classification tasks. As data sharing grows across connected devices, safeguarding sensitive personal and industrial information is of increased importance. Privacy-preserving machine learning techniques such as differential privacy and homomorphic encryption offer mathematically rigorous security guarantees, but introduce difficult accuracy, privacy loss, and computational overhead issues. This thesis investigates PPML for logistic regression through a collaborative mini-batch training framework. I propose and implement an ordered mini-batch strategy, compare it to standard shuffled methods, then integrate differential privacy noise injection …

Hierarchy And Ideology Antagonism: Artificial Intelligence In Ridley Scott's Alien, Grace Anastasia Pula

Theses, Dissertations and Culminating Projects

This thesis examines the objectively threatening structure of artificial intelligence (AI) in the narrative plot of Alien and how it exerts control over the humans. Using a structuralist approach with Louis Althusser's Ideological State Apparatuses (ISAs), I will examine the character relationships and how an android, Ash, enforces a patriarchal, hierarchical system. Drawing on Mark Coeckelbergh’s AI Ethics and Jacques Ellul’s The Technological Society, I will outline broader fears that technology will surpass human intellect and serve a destructive function within an oppressive system. Analyzing two examples of AI characters, the film showcases capitalist ambitions through technological identities and their …

Artificial Intelligence Adoption In The Workplace. An Exploration Of Augmentation, Oyinkansola O. Sodiya

Management Dissertations

As collaborative work with artificial intelligence (AI augmentation) gains interest, it is crucial to investigate factors that affect how employees perceive and use AI tools at work. Drawing on task-technology fit and technology adoption theories, this dissertation examines the ways in which task dimensions, organizational contexts, and individual differences affect the perceived usefulness of working with AI tools. This dissertation demonstrates that task-technology fit is fundamental. Employees in jobs with high information processing demands are likely to positively perceive the usefulness of AI augmentation relative to employees in jobs with high interpersonal demands. Employees with more proactive personalities perceive greater …

Ablative Study Of Large Language Model-Based Gesture Inference For Autonomous Navigation, Neil Loftus

Theses, Dissertations and Capstones

Human gesture inference has broad applications ranging from sign language interpretation to device control. Traditional methods often rely on extensive manually labeled hand datasets for deep learning. Furthermore, they are typically limited to a discrete set of gestures existing in these datasets. Large Language Models (LLMs) created by enterprise companies such as OpenAI have demonstrated positive results in many artificial intelligence tasks, with a notable strength being their adaptability. Existing literature has shown that LLM based systems can not only perform gesture inference but can propose user intent provided with a context and list of possible actions. We propose an …

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

Neuroevolution Of Compact Search Heuristics In Sokoban, Yeonghun Lee

Computer Science Honors Papers

This thesis investigates whether neuroevolution can produce heuristic functions for A* search on Sokoban that are drastically more parameter-efficient than a conventional gradient-trained baseline. Sokoban is a combinatorial planning puzzle that is both NPhard and PSPACE-complete, and solving it at scale requires an accurate cost-to-go heuristic to guide search. The standard approach fixes a neural network architecture and trains it by gradient descent; this thesis investigates whether evolutionary search over network topology can find heuristics that are competitive in accuracy while using orders of magnitude fewer parameters.

Four heuristic approaches are implemented and compared on the medium-difficulty split of the …

A Novel Lightweight Framework For Low-Light Image Enhancement Via Gaussian Denoising And Clahe, Daniel Oluwaseun Adesoji

Master's Theses or Doctor of Nursing Practice

Low-light image enhancement is a major challenge in digital imaging, especially in medical imaging, surveillance, and autonomous vision systems. Images captured under poor illumination often appear dark, noisy, and low in contrast, which makes it hard to observe important details. Traditional enhancement methods can improve brightness but usually introduce artifacts or increase noise. Although deep learning methods have shown strong performance, they usually require large datasets, and high computational resources. This creates a need for simpler and more efficient enhancement techniques. This study proposes a lightweight framework that incorporates Gaussian denoising with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance …

Learning Design To Advance Human-Ai Collaboration In K-12 Education, Wing Sha Chan, Jinhee Kim, Seongryeong Yu, Rita Kay Detrick

STEMPS Faculty Publications

This chapter explores key components for designing effective Human-AI Collaboration (HAC) in K–12 education, addressing the current lack of theoretical and conceptual frameworks for structuring and implementing HAC in teaching and learning. It examines four essential areas: curriculum design, student and teacher–AI interaction, learning environments, and the evolution of HAC over time. The chapter introduces the concept of HAC in K–12 contexts, highlighting how humans and AI can leverage each other's strengths through co-evolutionary processes that foster mutual learning and collaboration. It reviews current HAC practices in schools and discusses their contributions to both teaching and learning. Finally, it presents …

Data Defines Success: Algorithm For Dataset Quality Assessment In Deep Learning For Malware Detection, Matei Ionescu

Theses and Dissertations

The field of artificial intelligence is based upon the premise of constructing architectures through which to propagate training data. However, the majority of existing research literature is focused on architecture. While necessary, the attention devoted to the architecture should not so precipitously exceed that of the data. It should be noted that this disparity is not without reasonable cause. Data quality is often exceedingly difficult to verify due to particularities of the field or subfield; LLM repositories of text are distinct from image recognition pictures of dog breeds which are distinct from EEG waveforms of human brains which are distinct …

Tackling The Societal And Regulatory Challenges Of Emerging Technologies: A Case Study Of Deepfake, Jingyao Li

2026

Governing emerging technologies such as Artificial Intelligence (AI) poses enduring challenges for policymakers, industries, and societies. Early-stage governance is often hindered by limited understanding of technological implications, rapid innovation cycles, and resistance from powerful industry actors who favor minimal oversight. Yet, timely and effective governance is essential, as new technologies are most malleable in their formative stages. This dissertation examines how emerging technologies can be governed effectively by using deepfakes technology as a focal case. This dissertation comprises three interrelated studies.

The first paper reviews the literature on deepfakes and emerging technology governance, identifying the distinct characteristics of deepfake technology …

A Specification-Driven Framework For Self-Supervised Learning In Specialized Vision Domains, Mahmut S. Gokmen

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has emerged as a principled approach to visual representation learning that derives supervisory signal directly from unlabeled data, enabling foundation models to be trained at scale without manual annotation. Deployments in medical imaging and biometric recognition have demonstrated the potential of this paradigm, yet the assumptions that make SSL effective on natural image benchmarks fail systematically in specialized domains. Generic SSL pipelines encode a tacit assumption that the most informative correspondence is spatial proximity within a single acquisition. In specialized domains this assumption breaks at the level of the data-generating process: the signal that carries domain-specific information …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

Ai Vs. Genai: Combating Llm Generated Prescription Fraud With Transformer Based Detection Models, Ankitha Vokkaleri Shankarappa

Selected Full-Text Master Theses 2021-

The rapid evolution of Large Language Models (LLMs) has introduced a sophisticated new vector for healthcare fraud: the generation of high-fidelity, synthetic medical prescriptions. Traditional fraud detection systems, which rely on rule-based engines and basic statistical anomalies, are increasingly ill-equipped to identify these AI-generated forgeries that mimic the structural and clinical logic of authentic records. This thesis presents a robust detection framework using Transformer-based architectures to distinguish between human-authored Medicare Part D prescriptions and fully synthetic records generated by GPT-4.

The research was conducted across two distinct phases: an initial pilot study using 4,000 samples and a rigorous validation stress …

Erosion Of Trust In Online Information, Tirth Desai

A with Honors Projects

Researching how AI spreads misinformation and impacts trust in information.

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

Monitoring, Oversight, And Learning In Medical Ai, W. Nicholson Price Ii

Articles

When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performance on specific groups or ineffective integration into health system workflows, simply don’t fall within the contours of regularized reporting. Even if they are noticed by the health system—far from a given—there’s no obvious way to share that information more broadly. Against this backdrop, there are justified calls for better oversight and reporting. But there’s the …

A Comprehensive Survey On Facial Expression Generation: From Gans To Llm-Guided Multimodal Models, Murad Hasan, Rabab Abdelfattah, Kareem Abdelfatah, Mostafa Fouda, Ahmed Sherif

Faculty Publications

Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

How Should Ai Talk About Us? Llms And Social Generics, Tiffany A. Zhu

Philosophy Faculty Publications

How should AI-generated speech balance epistemic aims, such as precision and accuracy, with ethical and social considerations? This paper examines a subtle yet consequential aspect of LLM-driven communication: the use of generic generalizations that convey information about social groups (e.g., “immigrants work low-wage jobs”). While central to human epistemic and pedagogical practices, generics are theorized to reinforce stereotypes, essentialism, and injustice. Using ChatGPT-3.5 as a case study, I uncover tendencies for AI chatbots to inconsistently hedge and refuse generics, including those that reflect well-documented social structural patterns, such as “women are more likely to get attacked while walking alone at …

Accounting For Spatial Effects And Social Norms In Making Algorithmic Law: Insights From And Applications In Urban Mobility, Jingkang Gao

Journal of Law and Mobility

This Article examines a prominent idea in the law and technology literature: that algorithms and big data can be used to make law dynamic and personalized. As currently envisioned by legal scholars, “algorithmic law” entails laws that adjust in real time to changing conditions and vary across individuals, improving welfare by tailoring legal rules and standards to personal characteristics.

This Article argues that this vision of algorithmic law is incomplete—and often counterproductive. Existing proposals treat personalization as a function of individual attributes alone, overlooking the fact that effects of individual behavior are fundamentally interactive. Individual behavior is shaped by spatial …

Artificial Intelligence–Enabled Revenue Cycle Management And Financial Performance In Healthcare Organizations, K’Reesa Webster

Theses, Dissertations and Capstones

The purpose of this review was to examine how artificial intelligence–enabled revenue cycle management (AI-enabled RCM) systems have been associated with financial performance outcomes in healthcare organizations. A literature review following a systematic process consistent with PRISMA 2020 guidelines was conducted to identify quantitative studies published between 2015 and 2026. Eligible studies were required to report at least one financial outcome related to claim denial rate, days in accounts receivable, or operating margin. Twenty-seven studies met all inclusion criteria. Findings across these studies indicated that AI-enabled RCM systems have been associated with lower denial rates, shorter accounts receivable timelines, and …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

A Literature Review On Ethics In Medical Diagnostic Ai & Analysis Therein, Connor J. Turetzky

Master's Theses

Artificial Intelligence presents a very promising future in medicine. Being able to diagnose and recommend treatments quickly is vital in ensuring positive patient outcomes. However, the new technology is not without risk. In this narrative literature review, the risks of AI in terms of bias, ethics, and environmental impact will be explored through existing research. This paper will focus on research published between 2019 and 2026, highlighting the major ethical and systematic problems currently facing diagnostic AI. Historical bias in medical data has led to AI that share those biases, and humans inherit that bias creating a potential negative feedback …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs' consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs' responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model's output. The LLM-CXR model is selected as the medical …

Machine Learning Classification Of Prostate Cancer Genomic Sequences Using K-Mer And Sequence-Derived Features, Kuldeep Rawat, Hirendra Nath Banerjee, Jamie Noble, Saa Naudia Deloatch, Satyendra Banerjee, Sachin Shetty, Soumya Banerjee

VMASC Publications

Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer …

Dyno : Dynamic Neurosymbolic Orchestrator For Multi-Agent Systems, Ritvik Garimella, Chathurangi Shyalika, Renjith Prasad, Amit Sheth

Publications

Large Language Model (LLM)-based multi-agent systems (LaMAS) represent an emerging paradigm for tackling complex, multi-step reasoning and decision-making problems. As these systems scale, orchestration, which is the ability to coordinate, manage, and evaluate the interactions among diverse agents, becomes central to their success. While recent orchestrators such as AgentFlow have demonstrated promise in managing communication and task delegation, they remain limited in their ability to understand task semantics, coordinate heterogeneous agent types (e.g., reactive vs. cognitive), and adaptively align outputs with human-defined goals. In this position paper, we introduce the DYNO (Dynamic Neurosymbolic Orchestrator), a system developed as part of …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Sparse Gradient Training For Recommender Systems, Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Jianxin Li, Hongzhi Yin

Research outputs 2022 to 2026

Recommender systems are widely applied in numerous online platforms such as shopping and social media platforms. They typically utilize large embedding tables that map users and items to dense vectors of uniform sizes. As the number of users and items continues to grow, this design leads to significant memory consumption and computational inefficiencies. This challenge is particularly pronounced in scenarios such as federated learning, where model parameters are updated locally on edge devices with limited computational resources before being transmitted to a central server for aggregation. Numerous approaches have been proposed to address this issue, among which embedding pruning methods …

A Knowledge-Driven, Ai-Assisted Cyber Defence Framework For Iomt Remote Patient Monitoring, Kulsoom S. Bughio, David M. Cook, Abdul M. Unar

Research outputs 2022 to 2026

The rapid adoption of Internet Medical Things (IoMT) technologies in remote patient monitoring has reshaped healthcare delivery by enabling continuous, real-time clinical observation outside traditional care settings. However, this shift has also expanded the cyber-attack surface across heterogeneous, resource-constrained medical devices, wireless networks, cloud services, and third-party platforms. In cyber warfare, healthcare has become an incorporated target of geopolitics, with hospitals, remote monitoring systems, and emergency health systems being used to broaden the attack surface for adversaries to exploit. Existing security approaches for IoMT environments remain largely manual, fragmented, and reactive, limiting their effectiveness in dynamically assessing vulnerabilities and supporting …