Digital Commons Network^™

Instructional Designers' Reflections On Generative Ai Use For Scenario-Based And Performance-Centered Learning: A Collective Autoethnography, Nour El Houda Maache, Dan V. Dao, Jiyoon Jung, Jayanth Nadheri, Nari Kim, Chikezie Ozuzu, Xinyue Ren

STEMPS Faculty Publications

This study examines how instructional designer-instructors (IDIs) use and evaluate generative artificial intelligence (GenAI) when designing scenario-based and performance-centered authentic learning in higher education. Using a collective autoethnography (CAE) approach, the study draws on semi-structured interviews and reflective narratives from five IDIs with varied professional experience. Findings indicate that GenAI enhanced design capacity by accelerating scenario development, translating complex content, and supporting scenario-based and performance-based task construction. At the same time, participants reported limitations related to contextual misalignment, output unreliability, and the cognitive demands of prompt refinement. Across cases, effective integration depended on sustained human oversight, disciplinary judgment, and ethical …

Chatgpt In Secondary English Language Learning: Educators' And Students' Literacy, Perceptions, And Experiences, Victoria Brenes, Sierra Outerbridge, Xinyue Ren

STEMPS Faculty Publications

The increasing use of generative artificial intelligence (GenAI) has shown the potential of transforming teaching and learning practices in various educational settings, such as in English language learning (ELL). As English language learners (ELLs) often experience many challenges and barriers in schools in the United States, it is urgent to leverage the educational affordances of GenAI in fostering the effectiveness of ELL. Given the limited research investigating GenAI adoption, especially ChatGPT literacy within K-12 ELL, this convergent mixed methods research aims to investigate students' and teachers’ perceptions of using ChatGPT and their ChatGPT literacy in secondary ELL contexts. We will …

A Comparison Of Conversational Chatbots And The Internet For Consumer Information Search, Wondwesen Tafesse, Yoseph Mamo

STEMPS Faculty Publications

This study compares consumer perceptions of conversational chatbots and the internet for information search. While the internet is a mature platform, conversational chatbots represent an emerging technology, and insight into how consumers view them in relation to the internet for information search is lacking. Drawing on the information source utility perspective, the study builds a comparative model based on four key dimensions: information currency, information customisation, information trustworthiness, and media richness. Additionally, the study investigates consumers’ prior experience with conversational chatbots as a moderating factor. Data was collected from 191 respondents recruited through MTurk. Paired sample t-tests assessed mean differences …

Design Principles For Customer-Engaging Digital Service Systems: An Action Research Study, Keng Leng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

Operationalizing Cyber-Routine Activities Theory For Senior Cybercrime Prevention: An Evaluation Of The Shield Training-The-Trainer Program, Kyung-Shick Choi, Insun Park, Mijin Kim, Ji Yae Bong

International Journal of Cybersecurity Intelligence & Cybercrime

Older adults face growing risks of cyber-enabled fraud, yet scalable, evidence-based prevention programs remain limited. Guided by Cyber-Routine Activities Theory (Cyber-RAT), this study evaluates the pilot implementation of the Seniors Harnessing Internet Education for Lasting Defense (SHIELD) Training-the-Trainer program, designed to prepare law enforcement officers and community leaders to deliver cybercrime prevention education to older adults. A key innovation of SHIELD is its integration of interactive game-based simulations, which allow participants to practice verification, refusal, and reporting behaviors in realistic cybercrime scenarios. Following the December 2025 pilot session in Boston, semi-structured interviews were conducted with 12 participants from law enforcement, …

From Authorization To Loss: A Blockchain Forensic Analysis Of Transaction-Level Mechanisms In Cryptocurrency Airdrop Scams, Chanwoo Shin, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

Cryptocurrency airdrop scams have emerged as a rapidly growing form of cyber-enabled financial crime, yet remain underexplored in empirical research. This study examines how transaction-level mechanisms and offender strategies influence variation in monetary loss in airdrop scam incidents. Grounded in Cyber-Routine Activities Theory (Cyber-RAT), the study conceptualizes financial harm as occurring within decentralized environments where users’ online behaviors, particularly transaction authorization, intersect with limited digital capable guardianship. Data were drawn from 112 validated airdrop scam cases reported on Chainabuse.com between January and December 2025. Blockchain forensic analysis using Breadcrumbs was conducted to reconstruct transaction pathways, identify exchange interactions, and detect …

Unsafe2safe: Controllable Image Anonymization For Downstream Utility, Minh Dinh

Dartmouth College Master’s Theses

Large-scale image datasets frequently contain identifiable or sensitive content, raising privacy risks when training models that may memorize and leak such information. We present Unsafe2Safe, a fully automated pipeline that detects privacy-prone images and rewrites only their sensitive regions using multimodally guided diffusion editing. Unsafe2Safe operates in two stages. Stage 1 uses a vision--language model to (i) inspect images for privacy risks, (ii) generate paired private and public captions that respectively include and omit sensitive attributes, and (iii) prompt a large language model to produce structured, identity-neutral edit instructions conditioned on the public caption. Stage 2 employs instruction-driven diffusion editors …

From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality, Jemely Robles

Dartmouth College Master’s Theses

Virtual reality is increasingly explored as a tool for cultivating empathy, and haptic feedback as a tool for enhancing immersion. This paper investigates the effects of combining the two.  Fifty-two participants experienced a custom-built VR scene in which a character was shown packing up a room. Participants were assigned to either a haptic condition, receiving passive haptic feedback corresponding to the character's actions, or a non-haptic control condition that did not receive any haptic input. Trait empathy was measured beforehand, and state empathy and engagement were measured after the experience. Thematic analysis was conducted on post-study interviews, and headset recordings …

Measuring The Efficiency Of The Arts And Culture Sector Relative To Gdp: A State-Level Analysis Of The United States, Ameerkhan Jaffar Khan Khader Khan

Selected Full-Text Master Theses 2021-

Arts and cultural production contributed $1.17 trillion to United States gross domestic product in 2023, 4.2% of the national total, but that contribution is spread very unevenly across states, and the official statistics describe how large the sector is rather than how efficiently it operates (Bureau of Economic Analysis, 2024). This study asks how efficiently each state convert growth in arts and culture employment into growth in value added, how many inputs the efficiency model can carry before it stops distinguishing 51 observations, and whether observable state characteristics explain the differences found. The analysis uses 2023 state-level data for all …

Analyzing Modern Scam Typologies: From Pig-Butchering And Nigerian Advance-Fee Fraud To Crypto Airdrop Schemes, Katalin Parti, Sinyong Choi, Thomas Dearden

International Journal of Cybersecurity Intelligence & Cybercrime

Rapid advancements in digital infrastructure and decentralized networks have fundamentally altered the nature of contemporary cybercrime, making comprehensive empirical and technical analysis more crucial than ever. To address these challenges, this editorial summarizes the research contributions featured in this issue of the International Jour nal of Cybersecurity Intelligence and Cybercrime. The included papers examine the structural on-chain dynamics of sanctioned pig-butchering operations, the representational production and AI-driven evolution of the “Nigerian scam” label, the critical transaction-authorization factors driving losses in crypto airdrop schemes, and the optimization of sentence-transformer models for automated Host Intrusion Detection System (HIDS) alert enrichment. Together, these …

Three-Tier On-Chain Transaction Architecture In A Sanctions-Linked Pig-Butchering Network: A Blockchain-Forensics Case Study, Matthew Stern, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

n October 2025, the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) sanctioned 29 Bitcoin addresses asso ciated with the Prince Group and Chen Zhi, providing an opportunity to examine the internal on-chain structure of a sanctions-linked pig-butchering network. This blockchain-forensics case study analyzes the group of 29 addresses using blockchain tracing, cross-plat form attribution checks across multiple commercial analytics platforms, exposure screening, and thematic analysis of transaction be havior. Because the case rests on OFAC designations and DOJ allegations, the traced flows are interpreted as patterns consistent with suspected laundering rather than adjudicated crimes; no fiat …

Attribution Post ‘Aura’ Loss: A Qualitative Discourse Analysis Of The Nigerian Scam Label And Fraud Attribution, Emaediong Akpan

International Journal of Cybersecurity Intelligence & Cybercrime

In this essay, I employ qualitative discourse analysis of eleven purposively selected literature to examine how “Nigerian scam” became an established yet detached signifier of advance-fee fraud in global discourse. I do not treat this association as a reflection of offender identity or national inclination. Instead, the analysis traces how attribution is produced through representational repetition, institu tional uptake, and the circulation of familiar narrative cues. Using qualitative discourse analysis and an interpretive approach, this paper distinguishes among the socio-technical conditions that enable fraud, the institutional processes that stabilize attribution, and the semiotic dynamics through which national categories acquire durability. …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

Critical Success Factors For An Effective Security Risk Management Program: An Exploratory Case Study, Jason A. Williams, Humayun Zafar, Saurabh Gupta

Faculty Articles

This paper evaluates the perceived effectiveness of the security risk management (SRM) programs at a Fortune 500 firm. Layers of management and staff participated in the study. Perceived effectiveness of their SRM programs was based on nine critical success factors (CSFs). Interviews confirmed six initial CSFs (Executive Management Support, Organizational Maturity, Open Communication, Risk Management Stakeholders, Team Member Empowerment, and Holistic View of an Organization) that were extracted from the literature. They were confirmed and synthesized with three additional CSFs (Security Maintenance, Corporate Security Strategy, and Human Resource Development). Implications for SRM are discussed.

The Future Of Monetary Federalism: Rethinking Supremacy In The Stablecoin Era, Richard H. Fair

American University Business Law Review

[INTRODUCTION] In the summer of 2023, the State of Wyoming enacted a law authorizing its state treasurer to issue a blockchain-based, state-backed digital stablecoin known as the Wyoming Stable Token (“WYST”). Two years later, Congress passed the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act, GENIUS, or the Act), moving to establish a comprehensive federal regulatory regime for stablecoins. These dueling initiatives have sparked more than regulatory confusion; they have set the stage for a structural clash between state financial innovation and federal monetary supremacy. At the heart of this confrontation lies a question that the Constitution …

Lessons On Generative Artificial Intelligence From The American Association Of Dental Editors And Journalists (Aadej), Christopher J. Smiley

Journal of the American College of Dentists

The American Association of Dental Editors and Journalists (AADEJ) recently released "Guidance for Authors, Editors and Publishers on the Use of Generative AI". Developed by an 11-member stakeholder panel, this guidance paper serves a dual purpose: It provides practical strategies for mitigating the risks that generative artificial intelligence (GAI) poses to professional writing and explains how GAI creates these risks. Understanding both the underlying vulnerabilities of GAI and how to address them is critical for users at all levels, from authors and reviewers to editors, publishers, and general users, to maintain the validity and reliability of their written work. This …

Provoking Generative Ai Futures: Merging Theory And Praxis, Regina M. Luttrell Ph.D., Nick Bowman

Media Studies - All Scholarship

An accessible exploration of the myriad applications and challenges of generative AI for media and communication students, scholars, and practitioners alike.

The latest emergence of increasingly low-cost and scalable AI technologies presents a point of both celebration and concern for the contemporary media and information ecosystem. To this end, this edited volume gathers media and communications scholars and practitioners to engage in discussions and exchange ideas about current trends and developments in the field. Questions this volume asks include: What are the essentials of generative AI from a media and communication perspective? How has generative AI influenced research and scholarship? …

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 …

Advancing Multi-Modal Learning, Swetha Sirnam

Graduate Studies Theses and Dissertations 2026

Multimodal learning aims to develop systems that understand and reason across multiple sources of information such as images, videos, audio, and text. These capabilities are central to applications including visual search, digital assistants, robotics, and visual question answering. Despite recent progress in multimodal large language models, current approaches struggle to capture the structured complexity of real-world visual data. Fine-grained visual details are often lost during multimodal alignment, models frequently fail to capture nuanced human intent, and existing methods remain limited in understanding long-form videos with complex temporal dynamics. Furthermore, many evaluation benchmarks rely heavily on explicit visual cues, which can …

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 …

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. …

Ai Tips And Traps, Patrick Barry

Books

Based on a series of popular courses and workshops that Professor Patrick Barry has created for students, professionals, and anyone else interested in taking a skills-based approach to artificial intelligence, this book gives you a chance to engage with important AI concepts, experiment with exploratory AI exercises, and then ultimately develop your own customized list of AI traps to try as well as AI traps to avoid.

Use Of Generative Ai In Scholarly Research: Challenges And Opportunities, Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar

Computer Information Systems Faculty Publications

The focus of this panel report is to present evidence of a panel discussion that took place in October 2025 on the use of generative artificial intelligence (GAI) in academic research and publishing. The panel included publishers (Elsevier, Emerald, Gailot Press, MIT Press, Nature/Springer, Wiley) and editors in chief (representing the Information Systems Journal, Journal of the AIS, Journal of Data Science and ICES Journal of Marine Science). Key outcomes of the panel are the following: 1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …

Evaluating Llms For Cpe Identification In Iot Reconnaissance, Christopher Davisson

EWU Masters Thesis Collection

Vulnerability identification during penetration testing relies on rigid string-matching to map network scan data to Common Platform Enumeration (CPE) identifiers and downstream Common Vulnerabilities and Exposures (CVEs). The approach frequently fails on physical Internet of Things (IoT) devices, which produce non-standard, irregular service banners that resist deterministic parsing. Large Language Models can reason through these fuzzy associations, but cloud-hosted models introduce cost, latency, and operational security concerns when processing reconnaissance data from live networks. This thesis asks whether locally-hosted open-weight Large Language Models (LLMs) can perform this task well enough to be useful, and how performance varies with model scale, …

Visual Cues Of Human-Likeness, Not Salience, Impact Trust-Related Human-Computer Interaction, Jordan Schotz

Graduate Studies Theses and Dissertations 2026

As interactions with digital agents become increasingly integrated into daily life, understanding how visual representations influence social decision-making is critical. Previous research in human-computer interaction has frequently confounded the psychological effects of an agent's perceived human-likeness with the underlying visual salience of the stimuli. To address these persistent gaps, the present study systematically isolated the effects of human-likeness and visual cue trustworthiness on trust behavior while controlling for objective image properties. The present study expanded on and normed the Virtual Avatar Facial Stimuli Set (VAFSS), a comprehensive database comprising hundreds of identity-matched photographs and computer-generated avatars varying across a spectrum …

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …