Artificial Intelligence and Robotics Commons^™

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Study On Strategies And Measures And Insights For Introducing Artificial Intelligence Talents In Japan, Fangxin Hu, Ru Ma, Yujun Su

Bulletin of Chinese Academy of Sciences (Chinese Version)

In response to the aging population and the need for innovation in science and technology development, Japan regards AI as a key technology to solve social problems. In addition to accelerating the training of domestic AI talents, Japan is also vigorously introducing overseas AI talents. This study sorts out and analyzes Japan’s long-term, annual, and AI-specific strategic planning for the introduction of AI talents, including Basic Plan for Science, Technology and Innovation, Comprehensive Innovation Strategy, Strategic Plan for Artificial Intelligence Technology, and AI Strategy, and explores Japan’s specific implementation measures such as updating the national residence management system, improving the …

Activating Learning With Ai: Early Evidence From Undergraduate Sports Management Courses, Sherry Andre

Faculty and Staff Publications & Presentations

Presented research at the GSBA Conference on integrating AI into undergraduate sport management courses to enhance student engagement, critical thinking, and applied learning. The session shared early evidence on how AI can support skill development, improve classroom experiences, and better prepare students for careers in the evolving sport industry.

Project Risk Management In Ai-Enabled Systems: Managing Ethical, Privacy, And Governance Risks, Onome Cynthia Anakanire

Harrisburg University Dissertations and Theses

This research examined how Artificial intelligence (AI) has been embedded in project-based work, particularly in finance and software industries, where it enables efficiency and assists in complex decision-making. However, these innovations introduce significant ethical, privacy, and governance risks that traditional project risk management frameworks fail to adequately address. This study investigated how project managers can systematically integrate the management of these emerging risks into AI-enabled projects. Using a qualitative research design, the study drew on semi-structured interviews with project managers, compliance officers, and AI developers in finance, software and related sectors. Supplementary data included internal project documentation and risk registers. …

Ai In The Workplace: Understanding Role Ambiguity, Employee Motivation, And Learning Engagement, Sheena Leah Metzger

Theses, Dissertations and Capstones

The rapid integration of artificial intelligence (AI) into organizational processes has altered how work is performed and experienced by employees, yet empirical research examining the human implications of AI-driven change remains limited. This study examined the perceived impact of AI implementation on role ambiguity, employee motivation, and training engagement, and investigated the moderating role of perceived organizational support (POS). Grounded in Job Demands–Resources (JD-R) Theory and Organizational Support Theory (OST), the research examined how employees interpreted and responded to AI-related changes in their work environment.

The results offer valuable insights into a shifting perception of how employees experience technology in …

Shaping The Future: Emerging Technologies And Their Role In Industry 4.0 And Beyond, Liuliu Qin

Information Technology & Decision Sciences Faculty Publications

This paper provides a comprehensive review of emerging technologies driving the transition from Industry 4.0 to Industry 5.0. It examines the foundational concepts and pillars of Industry 4.0 and explores the transformative roles of Artificial Intelligence (AI), Extended Reality (XR), Collaborative Cobots (Cobots), Brain–Computer Interfaces (BCIs), quantum technologies, and next-generation connectivity (5G/6G). By integrating technological, human-centric, and sustainability perspectives, the study outlines how these emerging technologies reshape industrial systems and enable intelligent, adaptive, and inclusive futures.

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 …

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 …

A Methodological Framework For Modernizing Engineering Course Content With Large Language Models, Katherine Smith, Dalya Ismael, Otilia Popescu, Murat Kuzlu, Adel El-Shahat, Vukica M. Jovanovic

Engineering Technology Faculty Publications

The rapid evolution of technology presents challenges for engineering educators. While the core engineering methods often remain relevant over time, course materials rapidly become outdated in presentation and pedagogical approach. This paper presents a methodological framework for using large-language models (LLMs) to modernize engineering course content with a case study in an advanced technical analysis course.

The methodology follows a phased approach that is designed to be repeatable and verify the accuracy and completeness of course content. During the first phase, an LLM is used to map outdated text-heavy content to a modern format using a LaTeX template. The second …

Lost In Instructions: Study Of Blind Users' Experiences With Diy Manuals And Ai-Rewritten Instructions For Assembly, Operation, And Troubleshooting Of Tangible Products, Monalika Padma Reddy, Aruna Balasubramanian, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

AI tools like ChatGPT and Be-My-AI are increasingly being used by blind individuals. Although prior work has explored their use in some Do-It-Yourself (DIY) tasks by blind individuals, little is known about how they use these tools and the available product-manual resources to assemble, operate, and troubleshoot physical/tangible products – tasks requiring spatial reasoning, structural understanding, and precise execution. We address this knowledge gap via an interview study and a usability study with blind participants, investigating how they leverage AI tools and product manuals for DIY tasks with physical products. Findings show that manuals are essential resources, but product-manual instructions …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern Artificial Intelligence (AI) systems exhibit fluid agency in multi-step workflows: lacking human-like consciousness or culpability, yet they display behavior that is (i) stochastic (probabilistic and path‑dependent), (ii) dynamic (co‑evolving with user interaction), and (iii) adaptive (able to reorient across contexts). These properties generate valuable outputs but collapse attribution, irreducibly entangling human and machine inputs. Doctrines that assume traceable provenance—authorship, inventorship, and liability—fracture under this unmappability, yielding ownership gaps and moral “crumple zones.”This Article argues that only functional equivalence stabilizes doctrine under unmappability: Where provenance is indeterminate, legal frameworks should treat human and AI contributions as equivalent for allocating rights …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

The Ai Revolution In Virtual Try-Ons: A Means-End Chain Model Perspective, Ju-Young M. Kang, Ji Young Lee, Dooyoung Choi, Sumin Helen Koo, Jeehyun Song, Youngjin Bahng

Educational Leadership & Workforce Development Faculty Publications

Leading brands have begun to implement artificial intelligence-driven virtual try-on (AI VTO) technology, which helps reduce returns and increase conversion rates, repeat purchases, and customer loyalty. This research aimed to examine how retail user experience with specific perceived quality factors of AI VTOs influences users’ value equity and downstream loyalty and to investigate the moderating effects of clothing in relation to the self as structure and concern for physical appearance, based on a Means–End Chain model. Data were collected from 509 U.S. online apparel shoppers using a consumer panel. Structural equation modeling and multigroup analysis were used for data analysis. …

From Enhancement To Substitution: A Strategic Provocation On Simulation-Based Sport, Grant B. Morgan, Andreas Stamatis

Journal of Applied Sport Management

Advances in artificial intelligence, large-scale machine learning, and simulation technologies are rapidly transforming how sport is played, analyzed, and consumed. To date, most scholarly and industry discussions frame these technologies as tools that enhance embodied sport by improving performance, officiating, media production, and fan engagement. This paper extends that conversation by posing a more provocative strategic question: under what conditions might simulation move from enhancement to substitution? Focusing explicitly on sport as a business and entertainment enterprise, we argue that many of sport’s core sources of cultural and economic value—uncertainty of outcome, narrative continuity, legitimacy, and collective meaning—are structurally …

Artificial Intelligence In Insurance Fraud Detection: Applications And Implications For Internal Audit, Maria Davis

Honors Projects

Artificial Intelligence (AI) is being used in accounting and the insurance industry to assist with auditing and fraud detection. The Big Four public accounting firms have invested heavily in AI implementation efforts. Across these firms, AI has been used to reallocate auditors’ time from mundane tasks to more complex tasks that require human judgement. Within the insurance industry, machine learning, deep learning, and natural language processing, among other AI tools, have proven helpful in fraud detection efforts. While the positive impacts of AI usage are clear, concerns surrounding the replacement of human jobs, a lack of transparency in auditing, heavy …

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.

From Static Prediction To Mindful Machines: A Paradigm Shift In Distributed Ai Systems, Rao Mikkilineni, W. Patrick Kelly

Barowsky School of Business | Faculty Scholarship

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore …

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).

Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading …

How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …

Quantum Leap: Harnessing Quantum–Ai Synergy For Resilient Supply Chains And Predictive Routing Under Tariff Shocks, Andrew Burnstine, Raouf Ghattas

Faculty and Staff Publications & Presentations

No abstract provided.

Review Of Ai Needs You: How We Can Change Ai’S Future And Save Our Own, Tracy A. Fernandez Rysavy

Feminist Pedagogy

AI Needs You: How We Can Change AI’s Future and Save Our Own urges citizens to band together now, while A.I. is still in its nascent stages, to head off its potentially destructive repercussions and ensure that the technology serves more than just a wealthy few. While such efforts might seem out of reach in our polarized society, author Verity Harding points to three cases from history where policy was heavily influenced by multistakeholder collaborations. This review encourages educators to use the book as a way to study business ethics; out-of-the-box thinking; and intersectional, inclusive consensus-building over a top-down approach.

Barriers To Machine Learning Adoption In Regulated Electric Utilities, Donald R. Shiflet Jr.

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, …

Interdisciplinary Narratives On Artificial Intelligence & Personnel Selection Systems, John Hunter, Melissa Intindola, Neil Boyd, Thiago Serra Azevedo Silva

Faculty Journal Articles

Artificial intelligence (AI) has quickly and persistently become a daily presence in our lives, and its omnipresence has eclipsed the speed with which scholars can fully assess its efficacy and pitfalls. AI’s ubiquity and the lack of a clear understanding of its implications for humanity has spurred scholars across disciplines to action, and scholars in the field of Human Resource Management (HR) have certainly joined the fray. As scholars with a variety of experience and scholarship across disciplines, we believe that the burgeoning conversation in the HR literature would significantly benefit from a greater presence of interdisciplinary knowledge.

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Trust And Ethics In Ai-Driven E-Commerce: Persuasion Vs. Privacy, Akriti Nepal

Student Publications

This study examines how AI-driven features in e-commerce influence user satisfaction and the role of trust in these interactions. Using a survey-based dataset of 100 consumers, we investigated whether trust moderates or mediates the impact of AI persuasiveness and perceptions of bias, intrusiveness, and preference understanding on satisfaction. Results indicate that AI’s perceived ability to understand user preferences strongly predicts satisfaction, while trust partially mediates the relationship between helpful AI features and urgency messages and user satisfaction. Conversely, trust did not significantly moderate these relationships, and concerns about bias and intrusiveness had minimal impact. Findings suggest that AI-driven satisfaction is …

Global Trends In Ai-Driven Product Development: A Cross-Country Analysis, Shilpa Dhananjayan

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

Artificial Intelligence (AI) is transforming industries and accelerating global innovation, yet its benefits remain unevenly distributed. A nation’s AI readiness—its capacity to adopt and implement AI technologies—plays a crucial role in economic growth and technological advancement. Key determinants of AI readiness include digital infrastructure, data accessibility, government policies, research and development (R&D) investment, and workforce development. This study examines the relationship between AI readiness, AI adoption, innovation, R&D investment of a nation, Digital Infrastructure Index (DII) and Human Capital Index (HCI) using a Random Forest regression model. Findings reveal a strong correlation between AI adoption …

Service With A Smile Or Salesperson Mirroring? Understanding The Flow Of Emotional Contagion In Sales Encounters, Vinh Quoc Trong Luong

Theses and Dissertations in Business Administration

This study examines the directionality of emotional contagion in sales interactions, addressing a critical gap in understanding whether emotions flow primarily from the salesperson to the customer, from the customer to the salesperson, or bidirectionally. While prior research emphasizes customer-driven emotional flow or bidirectional alignment, this study challenges these assumptions by employing categorical Cross-Recurrence Quantification Analysis (CRQA) to assess temporal emotional synchronization in sales dialogues. Leveraging automated sentiment analysis and multi-agent AI evaluation for performance metrics, the research analyzes 166 sales interactions to quantify emotional influence dynamics. Results reveal that salespeople predominantly lead emotional exchanges, exhibiting stronger and more stable …

Reviving The Lost Art: Historical Foundations And Future Pathways For Bespoke Service In Luxury Retail, Andrew Burnstine

Faculty and Staff Publications & Presentations

The mid-20th century witnessed a zenith of deeply personalized, bespoke service within iconic luxury specialty retailers. This paper critically analyzes the foundational principles of this historical bespoke service model, extending Service-Dominant (S-D) Logic and retail evolution theory, to propose a robust framework for its modern revival. Employing a rigorous qualitative, multiple-case study approach, grounded in extensive historical and media analysis of four archetypal American and British luxury boutiques, and uniquely informed by an insider-ethnographic perspective on the central case exemplar ("Martha's"), the study distills six core principles: Profound Client Knowledge, Visionary Curation & Styling, Anticipatory & Proactive Service, The Exclusive …