Digital Commons Network^™

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Improving Pid Control With Bayesian Optimization For Adversarially Robust Federated Learning, Adrian Pena, Sergei Chuprov, Raman Zatsarenko, Leon Reznik

Computer Science Faculty Publications

Federated Learning (FL) often suffers from unstable convergence and reduced robustness under non-IID data and malicious attacks. In this paper, we present FedPIDAvg_tuned, a control-theoretic aggregation framework for improving stability and adversarial robustness in FL. In particular, our approach combines a server-side Proportional–Integral–Derivative (PID) controller with Bayesian optimization to tune controller gains for different data and attack conditions. The PID controller regulates global model updates through feedback on loss dynamics, providing adaptive scaling that improves stability and convergence. The tuned gains are applied within a trust-weighted trimmed-mean mechanism to remove adversarial or outlier updates. Using the Flower framework, we evaluate …

Redefining Educational Technology: A Critical Collaborative Inquiry, Aras Bozkurt, Helen Crompton, Robert Farrow, Agnes Kukulska-Hulme, Jon Dron, Richard West, Agnieszka (Aga) Palalas, Matth Bower, Junhong Xiao, Ahmed Tlili, Danah Henriksen, Angelica Pazurek, Henk Huijser, Thomas K. F. Chiu, Petar Jandrić, Katy Jordan, John Curry, Royce Kimmons, Mutlu Cukurova, Thomas Reeves, Gwo-Jen Hwang, Peter Shea, Jason Lodge, Martin Weller, Davy Ng, Tutaleni Iita Asino

STEMPS Faculty Publications

Educational technologists have not settled on a fixed definition of the field and likely never will. However, attempting to define the field helps to understand the epistemological meanings that shape what the field sees, values, and considers worth pursuing. Through a critical historical review spanning over a century, alongside theoretical engagement with the concepts of entanglement and distributed agency, this paper identifies three key insufficiencies in current educational technology frameworks. These are the persistence of an instrumental-facilitative paradigm that treats technology as a resource deployed by human agents; the theoretical dissolution of the pedagogy-technology dichotomy that existing definitions have not …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Multimodal Emotion Detection System, Shubhankar Sameer Munshi

Master's Projects

Trying to understand emotion from speech is a problem that is present in human computer interaction. Nevertheless, there are still some shortcomings in current SER methods. Text-based systems may miss vital vocal cues, such as sarcasm, tone changes, and delivery. On the other hand, purely audio-based systems are prone to noise and unstable acoustic features. The combination of linguistic and acoustic features in multimodal approaches partially solves this problem, but many existing approaches use inflexible multimodal fusion techniques that cannot adjust their behaviors according to the quality of input signals. In this work, we propose a multimodal approach based on …

Real-Time Isolated Asl Recognition: Evaluating Spatial-Temporal Networks And Multimodal Llms, Raga Mouni Batchu

West Chester University Graduate Theses, Dissertations, and Final Projects

This thesis investigates the deployment of high-accuracy Isolated ASL Recognition (ISLR) in resource-constrained edge environments. We train a lightweight Spatio-Temporal Attention Network (SSTAN,∼2.7 M parameters,∼10 MB) on the WLASL-100 benchmark, achieving 75.25% Top-1 and 88.24% Top-5 accuracy with 139 ms CPU-only inference. A systematic comparison against frontier multimodal LLMs (Gemini 3 Flash, Gemini 3.1 Pro, Qwen 3 VL) shows SSTAN outperforms the best LLM baseline by∼1.85×in accuracy while being 22–230×faster and up to 40×cheaper annually. The LLMs’ core limitation is a lack of fine-grained temporal perception; they impose English-language semantic priors rather than learning the articulatory distinctions that define ASL …

Polysemicolon; Novice Programmers And Java Keywords, Briana C. Bettin

Michigan Tech Publications

Most industrial programming languages leverage the English language for reserved keywords – words which a program compiler recognizes as specific execution commands. The divide between expert and novice programmers showcases an intriguing middle-ground by which the polysemy of many keywords becomes revealed. This essay explores a sampling of the multitude of keyword interpretations that a novice programmer may derive from the Java language’s syntactic style and keywords specifically, and how the polysemy of both “English” and “code” meanings to these terms affects the novice-expert programmer transition.The transition from novice to expert, and the mapping of the career of metaphor to …

The Devil Is In The Det[Ai]Ls: Ai Agents, Ghost Students, And The Crisis Of Verified Presence In An Agentic Ai World, Aras Bozkurt, Helen Crompton, Caroline Fell Kurban

STEMPS Faculty Publications

The transition from reactive Generative Artificial Intelligence (GenAI) to agentic AI systems marks a categorical shift in digital education, moving beyond simple content generation to goal-oriented, autonomous execution. This paper explores the emergence of the “ghost student”: a digital surrogate created by the coupling of Large Language Models (the “mind”) and agentic AI browsers (the “body”). These entities are capable of navigating Learning Management Systems (LMS), engaging with content, and completing assessments with human-like mimicry, often rendering the actual learner’s presence optional. We argue that this phenomenon creates a verification gap that traditional proctoring and detection tools are structurally unable …

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 …

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 …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

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.

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

Energy-Efficient Security For Narrowband Iot Using Blockchain And Ep-Cumac, Hafizullah Kakar

UNF Graduate Theses and Dissertations

The Narrowband Internet of Things (NB-IoT) continues to expand but faces challenges such as cryptographic overhead and energy consumption. Security frameworks such as blockchain and Energy-Performance Cumulative Message Authentication Codes (EP-CuMAC) rely heavily on SHA-256, which is not optimized for energy-limited devices.

This work unifies two complementary approaches, a hybrid blockchain-based NB-IoT framework and an EP-CuMAC-based framework, by engineering their cryptographic core with an Energy Complexity Model-optimized SHA-256 (ECM-SHA256). ECM applies parallel memory-bank mapping and block-level access optimization to reduce redundant power usage while preserving algorithmic integrity.

Experimental evaluation on identical Intel DDR3 systems using pyRAPL shows energy savings of …

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 …

Optimizing Markov Chain Monte Carlo Algorithms For Fairness, Scalability, And Interpretability In Electoral Redistricting, Madhukara Kekulandara

Open Access Dissertations

Gerrymandering undermines democratic representation by manipulating electoral district boundaries to dilute the political influence of targeted communities. While Markov Chain Monte Carlo (MCMC) based redistricting algorithms have become a standard computational tool for detecting partisan gerrymandering, their effectiveness in addressing racial gerrymandering and their scalability on modern computing systems remain limited. This dissertation advances the theory and practice of algorithmic redistricting by optimizing MCMC-based approaches across three dimensions: racial fairness, computational efficiency, and interpretability.

First, this work introduces the Partial Map MCMC algorithm, a novel redistricting method designed specifically to address racial gerrymandering under the legal framework of Section 2 …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Hallucination Detection In Scientific Writing: Quantification, Trend Analysis And Benchmarking, Adiba Ibnat Hossain

Graduate Research Theses & Dissertations

The landscape of scientific communication has undergone a significant transformation with the emergence and widespread adoption of Large Language Models (LLMs). LLMs have enhanced productivity and creativity in scientific writing, raising serious questions about the faithfulness and reliability of the generated content. A common problem in LLMs is hallucination, which occurs when models generate fluent but inaccurate or inconsistent content. When hallucinations enter scientific writing, the intended meaning may be distorted, coherence disrupted, and the study’s overall integrity threatened.

This thesis investigates hallucinations in scientific writing from both an analytical and dataset-driven approach. First, it offers a comprehensive analysis of …

Cybercrime, Vulnerability And Digital Guardianship: Opportunity Structures And Prevention In A Changing Online Landscape, Mike Toro-Alvarez, Amy Lim

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

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.

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 …

Key-Point-Based Sign Language Translation With Multi-Stream Motion Modeling And Cross-Lingual Transfer, Alex Muchiri Kagozi

Dissertations and Theses

Automatic sign language translation remains challenging because models must recover linguistic structure from complex visual motion while relying on limited annotated data. This thesis investigates whether compact skeletal key-points can serve as an effective alternative to RGB video for continuous sign language recognition and translation, motivated by the privacy, computational efficiency, and transferability of skeletal representations. To address this problem, a segmented key-point-based framework is evaluated in continuous sign language recognition (Sign2Gloss), gloss-mediated translation (Sign2Gloss2Text), gloss-free translation (Sign2Text), and transfer learning within a common modeling pipeline. The proposed framework is a motion-aware multi-stream transformer encoder. Rather than processing the signer …

Real-Time Immersive Wildfire Simulation: A Game Engine Approach, Jake Bova

Graduate Student Theses, Dissertations, & Professional Papers

Wildfires are growing in frequency, size, and severity, placing escalating demands on firefighting resources and putting lives, property, and ecosystems at risk. Accurate prediction of fire behavior is critical to suppression planning, evacuation decisions, and firefighter training, yet the operational models that fire managers rely upon face a persistent tradeoff. High-fidelity physics-based models capture fire–atmosphere interactions faithfully but are computationally expensive, often running far slower than real time on CPU hardware, while faster empirical models sacrifice the dynamic coupling between a fire and the wind field it generates. Furthermore, most existing tools produce abstract, two-dimensional outputs that offer limited visual …