Digital Commons Network^™

Ai-Generated Musical Educational Videos: A Creative Learning Strategy For Any Discipline, Gulfem I. Yucelen

Teaching Repository of AI-Infused Learning

In this activity, students transform course concepts into original songs by writing lyrics, refining them with AI, and generating music and video content. Generative AI tools support the creative process, while students provide oversight to ensure accuracy and clarity. The final product is a musical educational video that deepens understanding and can also be shared with peers or younger learners.

Ai Role-Play Interview Simulation In A Graduate-Level Research Methods Course, Julie Siddique

Teaching Repository of AI-Infused Learning

To enhance qualitative research skills and deepen student engagement, I introduced an AI-driven role-play strategy in a graduate-level research methods course. Students were asked to practice interview skills using an AI bot that responded as a simulated persona, allowing students to practice semi-structured interviewing and navigate ethical considerations in a simulated context.

Ai In Medicine: A Game-Changer In Education And Practice, Katia Ferdowsi

Teaching Repository of AI-Infused Learning

This entry describes the integration of AI-powered platforms such as Osmosis, Khan Academy, and ChatGPT into undergraduate medical education. These tools support student engagement, deepen understanding of complex topics, and prepare learners for a tech-driven medical landscape. By embedding AI into course design, educators can address challenges such as limited clinical exposure, diverse learning needs, and time constraints.

2026 Ucf Book Arts Competition Official Entry Form, Special Collections & University Archives

Libraries' Documents

No abstract provided.

The Scoop, Vol. 12 Issue 11, February 2026, Health Sciences Library

Volume 12

Latest news and updates from the Health Sciences Library in our monthly newsletter for February 2026. Please see page 2 for a text-only version of this issue!

Rethinking The Mindfulness And Mindlessness In Media Evocation Of Human-Machine Communication: A Case Study On The In-Car Robot “Nomi”, Bing Wang, Longxiang Luo, Weizi Liu

Human-Machine Communication

The commonly used Media Equation framework in human-machine communication research exhibits limitations when explaining human-machine relationships in the AI era, giving rise to the Media Evocation paradigm. Based on the Media Evocation framework, this study employs in-depth interviews to investigate whether interactions between humans and the in-car robot Nomi are mindful or mindless, while also examining whether these interactions prompt reflections on the machine’s ontology. The findings indicate that most participants perceive Nomi as a human-like actor and interact with it mindfully. Highquality social cues, such as voice and facial expressions, can trigger mindful anthropomorphic responses in human-machine interaction, eliciting …

Human-Machine Communication: Complete Volume. Volume 12

Human-Machine Communication

This is the complete volume of HMC Volume 12.

Normalizing Ai: The Evanishing Effect And Rethinking The Machine Heuristic, Bumju Jung, Cameron W. Piercy, Patric R. Spence

Human-Machine Communication

This study examines how people evaluate factual claims attributed to human journalists versus artificial intelligence (AI) news bots, and how these evaluations are shaped by individual endorsement of the machine heuristic. An online experiment (N = 233) used a 2 (Agent: human vs. AI) × 2 (Information Veracity: accurate vs. inaccurate) design in which participants viewed a short tweet embedded in either a human or AI profile and then rated perceptions of source credibility, objectivity, bias, and accuracy. Endorsement of an AI-focused machine heuristic was significantly associated with higher source credibility, perceived objectivity, and perceived accuracy, and lower perceived bias, …

Ai Humanizers, Pragmatists, Skeptics: A Cluster Analysis Of Normative Attitudes For Ai’S Capabilities And Roles, Kate K. Mays, Ekaterina Novozhilova

Human-Machine Communication

This study explores people’s attitudes about artificial intelligence’s (AI) capabilities through a normative lens. Drawing from literature on phenomenological, ontological, and heuristic judgments about machines, we aim to better understand what capabilities people think AI should be able to have and how that relates to enthusiasm about having AI take certain roles in their lives. Survey participants (N = 601) were grouped via two-step clustering based on their attitudes, revealing three clusters: AI humanizers, who expect empathy and social interaction from AI; AI pragmatists, who expect cognitive capabilities; and AI skeptics, who prefer AI with limited human-like traits and emphasize …

Explain. Write. Edit. Summarise: An Exploratory Study On Agency Negotiations In Student-Chatbot Conversations, Árni Már Einarsson Mr, Ekaterina Pashevich

Human-Machine Communication

LLM-based chatbots such as ChatGPT have given technologies that traditionally operated on the back end a user-friendly, conversational interface. Their rapid adoption among students has prompted universities worldwide to issue guidelines and re-examine existing practices. Supplementing prior research based on discourse analysis and self-reported measures (e.g., surveys and interviews), we propose an approach for analyzing naturally occurring student–chatbot interactions, rooted in conversation analysis of chats (n = 503) donated by eight current Danish university students. The analysis identifies conversational patterns across three main types of activities and examines how agency is negotiated across the structural dimensions of signification, domination, and …

Two Years To Madness: A 30-Month Journal Of Human–Machine Communication And Second-Hand Reality, Andrew Prahl

Human-Machine Communication

Drawing on a 30-month analytic-autoethnography of the first author’s journal as an earlyaccess OpenAI user, this study examines how sustained communication with generative AI affects everyday lived experience and identity. Inductive content analysis of 166 entries by independent coders is followed by theory-driven coding with the extended-mind and sensemaking frameworks. Three interlocking themes emerge. Enabled and Degraded captures the tension between new capabilities and efficiency versus creeping skill atrophy. Scary New World charts rising anxiety as productivity gains erode competitive and relational distinctiveness. The findings also show a growing, pervasive doubt about the human authorship of texts, images, and other …

The Material Condition: A Practice Theory-Oriented Infrastructural Turn To The Ethics Of Human-Ai Communication, Anne Mollen, Sigrid Kannengießer

Human-Machine Communication

This paper calls for an infrastructural turn in human–AI communication, centered on the materiality of generative AI. We argue that a nuanced understanding of ethical implications of generative AI necessitates examining the socio-technical infrastructures that constitute these systems. These infrastructures encompass not only technological artifacts but also human actors (developers, users, data subjects, etc.) and their practices as well as social contexts in designing, developing, implementing, and maintaining AI. Therefore, we propose to adopt a practice theory approach, to analyze how ethical concerns materialize within these infrastructures. This perspective reveals how infrastructures are contested spaces in global production chains, highlighting …

The Epistemic Power Of Human-Machine Communication, Katrin Etzrodt, Autumn P. Edwards

Human-Machine Communication

This editorial introduces Volume 12 of Human–Machine Communication by positioning communicative machines as epistemic provocateurs that unsettle inherited distinctions among agency, meaning, and relation. Rather than treating these disruptions as anomalies to be resolved, the editorial argues that Human–Machine Communication (HMC) is distinctively equipped to register, analyze, and sustain such epistemological tensions. Drawing on philosophical traditions from Aristotle to Marx and Heidegger, the authors show how contemporary communicative machines simultaneously strain multiple historical settlements that once stabilized human–machine boundaries. The volume frames HMC as a “seismograph” for and as a “practice of” epistemological change, tracing how agency and meaning are …

Whole Earth Machines: Human-Machine Communication For A Green Transition, Klaus Bruhn Jensen

Human-Machine Communication

The climate crisis of the 21st century represents an existential risk to humanity and biodiversity, posing essential questions of how communication may serve to coordinate mitigation of and adaptation to climate change. One recent response has been massive investments by governments and corporations in systems providing feedback on the state of Earth— Whole Earth Machines (WEMs). For human-machine communication (HMC) studies, WEMs invite sustained engagement with communication infrastructures as a key constituent of research agendas, beyond the interface encounters at the center of many HMC studies to date. The article presents a conceptualization and operationalization of WEMs as critical infrastructures …

Interpretable Linear Models For Heart Disease Prediction: A Comparative Study, Dipok Deb, Emran Hossain

Data Science and Data Mining

Heart disease remains a leading cause of mortality worldwide, underscoring the importance of accurate and transparent methods for early diagnosis. While many machine learning and artificial intelligence models have demonstrated strong predictive performance, their limited interpretability poses challenges for clinical adoption. In this study, we evaluate three interpretable linear classification models—Generalized Linear Model (GLM) logistic regression, L1-regularized (Lasso) logistic regression, and Linear Discriminant Analysis (LDA)—for heart disease prediction using the Cleveland Heart Disease dataset. Following comprehensive data preprocessing, the models are assessed on a held-out test set using standard evaluation metrics, including accuracy, precision, recall, F1-score, and the area under …

Predicting Male Flowering Time In Maize Using Machine Learning Technique, Dipok Deb

Data Science and Data Mining

This study compares three machine learning approaches—Elastic Net, Principal Component Regression (PCR), and Partial Least Squares (PLS)—for variable selection and prediction within a high-dimensional Maize-GWAS framework. The goal was to accurately predict the complex polygenic trait of time to male flowering while managing the challenges of numerous, highly correlated genetic markers. The ENET model, which combines l1 and l2 penalties, delivered the highest predictive accuracy and successfully identified a select subset of the most influential genetic variants. In contrast, PCR and PLS, both utilizing dimension reduction, offered a significant advantage in computational speed and model stability. The findings confirm that …

Meeting Minutes 01-21-2026, Staff Advisory Council

Staff Advisory Council Meeting Documents

No abstract provided.

Covid-19 Infection And Hypertension In Correlation With Race, Elizabeth Durkin

The Pegasus Review: UCF Undergraduate Research Journal

Healthcare disparities exist in the U.S. between different races. This study investigated the frequency of COVID-19 infections and hypertension cases among five different racial groups (White, Black, Asian, Native American, and Native Hawaiian). The study also examined the correlation between COVID-19 infections and hypertension with the hypothesis that because of disease predisposition and socioeconomic barriers, Black populations would have the highest rates of COVID-19 infections and hypertension. We used data from the Kaiser Family Foundation regarding COVID-19 cases and race in conjunction with Census population data to determine whether COVID-19 case frequency means differed by race. Data on hypertension and …

Data-Driven Prediction Of Superconducting Critical Temperature: A Linear And Regularized Linear Modeling Approach, Dipok Deb

Data Science and Data Mining

This study adopts a data-driven approach to estimate the critical temperature of superconducting materials using linear machine learning models. A comprehensive dataset derived from material physico-chemical properties was analyzed after systematic preprocessing and standardization. Three linear modeling strategies—Linear Regression, Ridge Regression, and Linear Regression with Subset Selection—were developed and evaluated using standard regression performance metrics. The findings demonstrate that both basic and regularized linear models can effectively capture the relationship between material features and superconducting behavior, offering robust and interpretable predictions. While feature selection enhances model transparency, it comes with a modest reduction in predictive capability. Overall, this work emphasizes …

Digital Redlining In The Smart City: Artificial Intelligence, Housing Law, And Structural Urban Inequality, Spurthi Nrusimhadevara

Undergraduate Scholarship and Creative Works

Artificial intelligence is increasingly used in urban housing systems, where it shapes decisions about tenant screening, rent pricing, lending, zoning, and neighborhood investment. Although these tools are often promoted as efficient and impartial, they frequently rely on historical data that reflect racial, economic, and spatial inequality. As a result, AI systems can reproduce discriminatory outcomes even when protected characteristics are not directly used. This paper examines digital redlining in the smart city and argues that algorithmic housing tools mirror long standing structural inequities that raise significant concerns under fair housing and civil rights law. It evaluates how automated screening, predictive …

The Cypress Dome 2026, Issue 37, The Cypress Dome Society

The Cypress Dome

The Cypress Dome Society is the University of Central Florida’s literary organization staffed by undergraduate students. Named after a feature of the landscape on the UCF campus, we are dedicated to fostering the literary arts and promoting a sense of community among writers and artists through events and our annual publication, The Cypress Dome, UCF’s undergraduate literary journal.

Wormfood, Susan Mesler-Evans

Graduate Studies Theses and Dissertations 2026

A collection of seven fiction short stories focused on death and mortality, with genres represented including horror, fantasy, science fiction, and literary. The stories include an ecological epistolary narrative set in the near-future, a modern retelling of Hamlet, a Biblical flood narrative, a polyphonic retelling of the Bluebeard myth, a realist drama about abuse and healing, a darkly comic story of a woman planning for her own death, and a sci-fi story about two robots defying their programming to save a flower from destruction.

Sensitivity Analysis Of Tuned And Intentionally Mistuned Blisks To Random Mistuning, Tate A. Myers

Graduate Studies Theses and Dissertations 2026

The vibrational behavior of bladed disks, or blisks, is highly sensitive to variations in its cyclic symmetry due to variations in mass, stiffness, damping, or a combination of these factors. These inconsistencies can arise from manufacturing tolerance inconsistencies, operational wear, impacts during usage, and intentional mistuning. This thesis develops an analysis of the sensitivity of both tuned and intentionally mistuned blisks to random mistuning in their structural properties. Sobol sampling combined with state-space eigen-analysis is utilized to develop a set of randomly mistuned systems. Modal Assurance Criterion is used to track the mode shapes of each randomly mistuned system back …

Show Your Work: Assessment In The Age Of Ai, Kevin Yee, Laurie Uttich, Elizabeth Giltner, Anastasia Bojanowski

UCF Created OER Works

The fourth book from a team of college instructors who have been in the trenches of AI and higher education since the beginning, Show Your Work: Assessment in the Age of AI is a practical, discipline-spanning guide for educators who are ready to redesign their assignments for a world where AI is a given. Organized around three approaches—co-creating with AI, writing without it, and finding alternative AI-resistant assessments—this book offers more than 50 concrete strategies that faculty can mix, match, and adapt to their own courses and contexts. Whether you want to integrate AI as a learning tool, build assignments …

Designing Presence Together: A Coi-Guided Summit And Accountability Approach, Latisha D. Haag, Rachel Dolechek, Janet Stramel, Jessica Heronemus-Claiborn

Teaching Online Pedagogical Repository

This strategy, developed as part of a Digital Teaching Champions initiative at a regional comprehensive university, describes an Engage & Elevate summit and accountability model that helps faculty implement small, presence‑focused changes in their online courses using the Community of Inquiry (CoI) framework. In two synchronous Zoom summits, faculty cohorts completed a brief CoI self‑assessment, explored discipline‑neutral examples of social, cognitive, and teaching presence, and analyzed low‑, moderate‑, and high‑presence versions of common online activities. Guided by summit worksheets and templates, each instructor selected one existing activity (such as a discussion, peer review, or feedback workflow), identified one “small teaching” …

Raining Plastics: Quantification Of Atmospheric Deposition Of Plastic And Anthropogenic Particles Into An Estuary Of National Significance With The Assistance Of Citizen Scientists, Linda Walters, Madison Serrate, Tara Blanchard, Paul Sacks, Fnu Joshua, Lei Zhai

Research Data and Datasets

Globally, little is known about the dispersal of microplastics (MP) and anthropogenic particles (AP) via atmospheric deposition (AD) into water bodies. Correlating AD to the large number of MP in estuaries is challenging but an important first step toward reducing this form of pollution. A previously published model of the surface waters of the Indian River Lagoon (IRL, east central coast of Florida, USA) estimated it contained 1.4 trillion microplastics. To determine if AD could produce this much plastic deposition, we deployed passive AD collectors throughout a 145 km2 area at three site types with assistance from citizen scientists. We …

University Of Central Florida Graduate Catalog, 2025-2026, University Of Central Florida

UCF Catalogs

No abstract provided.

Observation Of Altermagnetic Spin-Splitting In An Intercalated Transition Metal Dichalcogenide, Milo X. Sprague, Madhab Neupane

Research Data and Datasets

Altermagnetism is a novel magnetic phase combining characteristics of both antiferromagnetism and ferromagnetic ordering. Despite growing theoretical interest in altermagnetic materials, reports of experimentally verified high-Néel-temperature layered compounds are limited or remain to be firmly established. In our manuscript, Observation of Altermagnetic Spin-Splitting in an Intercalated Transition Metal Dichalcogenide by M. Sprague et al, we present an angle-resolved photoemission spectroscopy and density functional theory study of Co1/4TaSe2, a compound we identify as a layered altermagnetic material. Our spin-resolved and spin-integrated angle-resolved photoemission spectroscopy measurements reveal an electronic band structure in excellent agreement with density functional theory calculations, demonstrating clear signatures …

Student Performance And Study Habits: A Multiple Regression Analysis, Alyssa A. Zichichi, Zaineb Hedhili Razki

High Impact Practices Student Showcase Spring 2026

The purpose of this project is to study which academic and lifestyle variables are related to a student's performance index. In this dataset, the response variable is Performance Index, and the predictors are Hours Studied, Previous Scores, Extracurricular Activities, Sleep Hours, and Sample Question Papers Practiced. The question is whether these variables help explain differences in student performance and which predictors appear to be the strongest.

This study gave us the opportunity to learn how to approach analysis after real-world data collection, and what the expected relationships between our variables should be. We also learned how to find limitations in …

Community Outreach At Healthy Start Coalition Of Osceola County, Ben John

High Impact Practices Student Showcase Spring 2026

The Healthy Start Coalition of Osceola County (HSCOC) is a state and federally funded non-profit organization dedicated to providing comprehensive prenatal and postnatal care to mothers and families. This abstract details a professional internship focused on enhancing community outreach and systemic efficiency. HSCOC manages diverse programs, including care coordination, home visiting, risk screening, and Fetal and Infant Mortality Review (FIMR), working closely with regional OBGYN providers to identify and support at-risk participants through evidence-based interventions.

A central component of this internship involved addressing a critical gap in the local system of care: the tendency for uninsured pregnant women to utilize …