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 …

Report 17—Revisions To The Articles Of Organization And Procedure Of The Commission On Stratigraphic Nomenclature Of The Americas, Lynn T. Dafoe, Robert B. Macnaughton, Marie-Perre Aubry, Carlton E. Brett, James R. Lehane, Fernando Núñez-Useche, Randall C. Orndorff, Frank Brunton, Lucy E. Edwards, Richard H. Fluegeman, Howard Harper, Robert Matthew Joeckel, Norman P. Lasca, B. H. Tew

Conservation and Survey Division: Faculty and Staff Publications

Several revisions to the Articles of Organization and Procedure for the North American Commission on Stratigraphic Nomenclature have been adopted following the 75th, 79th, and 80th annual meetings of the Commission in 2020, 2024 and 2025, respectively. Of these, the most substantial change was revision of Article III regarding membership composition of the Commission and the addition of new member organizations from Central and South America and the Caribbean region. As a result, the Commission also voted to change its name to be more inclusive of the expanding membership, with approval of the name: Commission on Stratigraphic Nomenclature of the …

Assessment Of Soybean Response To Irrigation Variability In Eastern Nebraska Using The Aquacrop Model, Anmol Singh, Saleh Taghvaeian, Yufeng Ge, Derek M. Heeren, Frank Bai

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a major irrigated crop in eastern Nebraska, where recent expansions in irrigated agriculture and projected trends in irrigation demand necessitate thorough investigations of approaches that can optimize its irrigation management. In this study, the AquaCrop model was calibrated and validated using 19 variable irrigation treatments from a five-year field experiment. The model accurately simulated canopy cover, soil water content, and grain yield, with validation normalized root mean square error (nRMSE) of 12%, 7%, and 7%, respectively. The model was subsequently applied to estimate soybean yield and water requirement during a 15-year period (2010–2024) …

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 …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

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 …

State Climate Superfunds, Rachel Rothschild

Articles

The harmful effects of climate change have already arrived in cities and states across America, with disasters increasing markedly in recent years along with more gradual environmental changes like sea-level rise and drought. To protect populations and natural resources, significant funding will be necessary for preventative measures as well as disaster response.

At present, it is states and ordinary taxpayers who must shoulder the enormous costs and planning for climate adaptation. A number of state legislators, however, have recently proposed enacting new laws that would require the companies who have most profited from fossil fuel usage to assist in funding …

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 …

The Classical Limit In Geometric Quantization By Group Extension, Paul Bracken

School of Mathematical & Statistical Sciences Faculty Publications

Physical systems as a rule are associated with a symmetry group. The group approach to geometric quantization makes use of this to introduce a quantization by means of group extension. This procedure is discussed and applied to a physical system whose group law has its origin with the Galilean group. The main intention is to investigate the classical limit and its relationship under this approach to geometric quantization. The classical quantization conditions are obtained based on a deeper foundation.

Implementation Of A 3-Dimensional Hydrodynamic Numerical Model On A Large Northern Lake, Kirin Lorente

Theses and Dissertations (Comprehensive)

Climate change is impacting high latitude regions disproportionately more than those at a lower latitude. With polar lakes being highly sensitive to changes in the climate, shifts in lake ice dynamics and hydrodynamics are indicators of such changes in regional and global climates. These changes impact future seasons in a positive feedback loop, where changes in winter ice cover impact open water nutrients levels and lake thermal structure. This highlights the importance for long-term monitoring. Despite the abundance of lakes distributed across Northern Canada, there has been limited research conducted on these sentinels of climate change.

Specifically, Great Bear Lake …

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, …

Permafrost Microtopography And Active Layer Hydrology In The Western Canadian Arctic, Tobias L. Leonhard

Theses and Dissertations (Comprehensive)

The growing interest in Arctic regions will likely increase the use of nuclear energy in the Arctic. While this energy source has a high potential to improve economic opportunities in the Arctic, it also increases the release of radioactive material (radionuclides). Radionuclides are already found in the Arctic and are traced back to natural as well as anthropogenic sources. Water is one of the dominant transport vectors for solutes such as radionuclides or other contaminants. Therefore, improving our understanding of Arctic hydrology is important for assessing future environmental risks such as the release of radionuclides. Topographic and microtopographic features are …

Memory Effects In Many-Body Systems, Jeffrey Beckstrand

Master's Projects

This thesis investigates memory effects in many-body systems through the MoriZwanzig Formalism for projected dynamics of a Hamiltonian System which yields the Generalized Langevin Equation (GLE). The GLE is a stochastic differential equation (SDE) that studies the dynamics of observables under the effects of many other observables in the system. Although satisfying, the GLE has a term called the Memory Kernel that encodes the past of the system and introduces a computational challenge by introducing a non-Markovian property to the equation. The kernel is often approximated by introducing a delta function, which simplifies the computation, but at the loss of …

Javatutor: An Integrated Llm Framework For Structured Programming Tutoring And Problem-Solving In Java, Amey Makarand Dhongade

Master's Projects

Large Language Models (LLMs) are being adopted more actively to assist in pro- gramming tasks, but much of the available AI code assistants devices are encouraging the rapid generation of solutions, as opposed to assisting students in developing an understanding. As a result, they can give the students correct code without teaching on how to judge a problem and how to build solutions or how to debug their programs themselves. This study describes JavaTutor, an LLM-based tutoring system that is used to assist learners in the learning process in a three-step process of understand- ing, generating a solution, and refining …

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

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 …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu

Master's Projects

Sudoku is a constraint satisfaction problem that serves as a testbed for studying reasoning and stepwise deduction. While many solvers can produce correct solutions, they often fail to generate human interpretable sequences of logically consistent steps. This study investigates whether a transformer trained on stepwise deduction traces can learn to solve Sudoku puzzles through sequential, logically deducible moves. Results show that solve accuracy improves significantly with more training data, reaching approximately 78% at 1500k samples. Stepwise analysis indicates that the model effectively learns simple strategies, achieving near-perfect performance. However, performance on more complex strategies remains limited. Overall, while the model …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

The Coral Microbiome Under Climate Change: Shifting Symbiotic Interactions, Katherine E. Parker, Kate M. Quigley

Biological Sciences Faculty Publications

Host–microbe symbioses are central to organismal health, yet these complex partnerships are often strongly shaped by the environment. Climate change is increasingly altering environmental conditions, disrupting the balance of host–microbe interactions and pushing them along a continuum from mutualism (both partners benefit) toward parasitism (one partner gains at the other’s expense). Reef-building corals provide a clear example of this vulnerability, as the coral holobiont, an ecological unit comprising diverse photosynthetic algal symbionts (Symbiodiniaceae) and other microbial partners, depends largely on these mutualistic interactions for nutrition, immunity, and stress tolerance. Increasingly, evidence suggests that under heat stress, the algal symbionts retain …

Evaluating Species At Risk In Data-Limited Fisheries: A Productivity-Susceptibility Analysis For Marine Aquarium Fish, Gabrielle A. Baillargeon, Alice A. Wynn, Jemelyn Grace P. Baldisimo, Michael F. Tlusty, Andrew L. Rhyne

Biological Sciences Faculty Publications

The marine aquarium trade (MAT) is a significant global industry harvesting millions of wild-caught, live coral reef fishes for public and private aquaria markets in the United States and Europe annually, while supporting fisher livelihoods in the Indo-Pacific. This diverse and species-rich trade is considered data-limited, creating barriers to quantifying the current and future socio-ecological sustainability of the fishery. We present a revised and expanded productivity–susceptibility analysis (PSA) that serves as a holistic risk assessment to estimate the vulnerability of marine aquarium fish to overfishing. Our global analysis includes 306 species that are actively in trade. Improvements to the PSA …

Connecting Classroom Physics To Real-World Research: A Citizen Science Approach In Ap Physics 1, Heather M. Landgarten

West Chester University Graduate Theses, Dissertations, and Final Projects

Advanced Placement (AP) Physics 1 emphasizes evidence-based reasoning, quantitative modeling, and scientific inquiry. However, secondary science instruction often remains focused on procedural problem solving and standardized assessment rather than authentic scientific practice. This study examined the integration of citizen science projects into an AP Physics 1 curriculum to provide students with authentic research experiences while reinforcing physics concepts during the post-AP examination instructional period.

Using a classroom-based mixed-methods pre–post design, students participated in citizen science activities through publicly available research platforms, including Zooniverse. Students collected and classified authentic scientific data, analyzed real-world datasets, developed research questions, compared observations with theoretical …

Tree-Based Graph Neura Networks For Natural Language Inference: From Structure-Only To Hybrid Architectures, Jason P. Lunder

EWU Masters Thesis Collection

Large transformer models achieve strong performance on natural language understanding tasks but require hundreds of millions of parameters and extensive pretraining. This thesis investigates whether graph neural networks operating on dependency parse trees can provide more parameter-efficient sentence representations for natural language inference, evaluated on two NLI tasks: entailment classification and semantic textual similarity.

Tree Matching Networks (TMN) adapt Graph Matching Networks to linguistic dependency trees with rich node and edge features, evaluated against a BERT baseline at matched parameter counts on identical training data. Tree Transformer Networks (TTN) extend TMN with transformer-based aggregation and tree-aware positional encodings, with component …

Assessing Forest Health And Small Mammals After A Limited Suppression Wildfire In Colville National Forest, Samantha J. Kennel

EWU Masters Thesis Collection

In the face of a wildfire crisis, there is a growing need to expand the pace and scale of forest treatments, including the strategic use of wildfire as a management tool. However, ecological outcomes remain uncertain in forests shaped by more than a century of fire suppression. The 2022 Thor Fire, a managed wildfire in the Colville National Forest, Washington, provides an opportunity to evaluate forest health in a relatively understudied system. Using a control–impact design, I established 100 monitoring plots in burned and adjacent unburned forests to assess how wildfire and fire severity influence forest structure, fuels, and tree …

Modeling The Effects Of Subsurface Tile Drainage On Atrazine And Nitrate Transport In Till Soils Of Iowa, Madison Hobbs

West Chester University Graduate Theses, Dissertations, and Final Projects

Tile drainage design influences groundwater flow pathways, residence time, and agrochemical transport in agricultural soils. This study evaluates how differences in drainage design affect subsurface flow behavior and contaminant transport in glacial till soils. A groundwater model was developed to compare conventional and controlled drainage systems with a shallow drainage system, and particle tracking was used to examine flow paths and residence times.

The results show that tile drainage creates distinct shallow and deeper flow pathways. In the conventional and controlled model, 88% of particles were captured by drains, while 12% reached the general head boundary. The shallow system drains …

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 …

Predicting Oil Contamination In Water Using Machine Learning On Microbial Compositions, Tong Gao, Isaac Bigcraft, Stephen Techtmann, Issei Nakamura

Michigan Tech Publications

We present a compact and generative machine-learning framework that predicts oil contamination based on microbial community compositions from experimental samples. Our method combines dimensionality reduction with data augmentation and generative modeling to address high-dimensional, non-linear, and sparse microbial data. To reduce the 503-dimensional bacterial composition dataset, we compared three dimensionality reduction techniques: feature importance from random forest, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). Feature importance outperformed PCA and t-SNE, improving predictive performance and identifying microbial species most strongly correlated with oil contamination. To mitigate data scarcity, we augmented the training data using an augmented data neural …

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 …