Digital Commons Network^™

The Future Is Now: Empowering Society Through Ai Literacy, Jason S. Wrench, Sanae Elmoudden

Milne Open Textbooks

Artificial Intelligence (AI) is no longer a futuristic concept—it is the reality of the present. From the algorithms shaping our social media feeds to the generative tools transforming our workplaces, AI has permeated every aspect of modern life. The Future is Now moves beyond the hype to provide a comprehensive roadmap for understanding, navigating, and shaping this technological revolution.

Demystifying the Machine

This textbook serves as a user-friendly guide to the “black box” of AI. It breaks down complex technical concepts—from machine learning and neural networks to large language models—making them accessible to students across all disciplines. By establishing a …

Information Literacy In The Artificial Intelligence Era: A Proposed Framework, Radwan Ali, Matthew L. Wilson

Communications in Information Literacy

Artificial intelligence (AI) systems and applications have become ubiquitous across daily life. While AI offers numerous positive opportunities, it also presents numerous challenges. Information literacy (IL) advocates are concerned about the risks that accompany AI systems. Given the universal reach of AI with its potential pitfalls and perils, researchers and practitioners from various fields and disciplines should inform the dialogue on AI’s development and application. A critical aspect of this dialogue comes in the form of information literacy. Our paper aims to answer the question “How can information literacy guide users in navigating and shaping artificial intelligence technologies?” by establishing …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Uncertainty Estimation For Graph-Based Learning In Digital Pathology, Saba Heidari Gheshlaghi, Nasim Yahyasoltani, Masoud Ganji

Computer Science Faculty Research and Publications

High-resolution digital scans of pathology slides, known as whole slide images (WSIs), have detailed spatial and contextual information for diagnosing cancer. However, the classification performance of WSIs by deep learning models is typically compromised by data with a different distribution, known as out-of-distribution (OOD), resulting in unreliable predictions. Therefore, having a reliable predictive uncertainty estimation is crucial for clinical adoption. This article comprehensively studies graph-based uncertainty estimation for WSI classification using two cutting-edge graph neural network (GNN) architectures: 1) graph attention networks (GAT); and 2) GraphSAGE. In this work, we introduce the first unified multihead GNN framework that leverages GraphSAGE …

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Exploring 2d Geometric Shape Classification Using Ai-Driven Feature Tables In Mathematics, Yasemin Gunpinar, Woonhee Sung

Education Faculty Publications and Presentations

This study explored the effectiveness of an AI-integrated instructional task designed to enhance preservice teachers' understanding of the features and hierarchical relationships of 2D geometric shapes. Originally developed and tested in online K-12 professional development settings, this intervention was adapted for in-person preservice teacher education context in this study. Data were collected from 17 preservice teachers through demographic surveys, pre- and posttests using the Van Hiele geometry framework, hierarchical diagram tasks, feature table creation during the intervention, and postintervention reflections. Findings indicated a statistically significant improvement in the accuracy and complexity of postintervention hierarchical diagrams, along with a descriptively higher …

Attachment To Artificial Intelligence: Development Of The Ai Attachment Scale, Construct Validation, And The Psychological Mechanisms Of Human-Ai Attachment, K Tennakoon Appuhamillage Sandeeshwara Kasturiratna, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) systems are increasingly integrated into daily life, not only as tools but also as social partners that people may turn to for interaction and support. This raises important questions about whether, how, and why individuals form attachment-like bonds with AI, and the psychological implications of such attachments. Across five studies involving 1259 unique participants from Singapore and the U.S., the current work developed and validated the 15-item AI Attachment Scale and investigated the dispositional and motivational factors associated with attachment to AI, as well as its emotional and social outcomes. The AI Attachment Scale displayed strong psychometric …

Is Artificial Intelligence Really Taking Our Jobs?, Francesca A. Strom

Undergraduate Theses and Capstone Projects

This paper studies how artificial intelligence has reshaped occupational opportunities in the United States by analyzing changes in employment, median wages, and wage inequality across all occupations from 2014 through 2024. Using a difference-in-differences framework, I compare occupations associated with AI-related tasks to those not directly exposed to generative AI, with particular attention to the structural break introduced by the mainstream release of generative AI tools in 2022. The results show no statistically significant decline in employment among AI-exposed occupations, indicating that early adoption did not lead to measurable job displacement. Instead, the strongest effects appear in wage inequality. Among …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

How Human Personality Will Change With The Use Of Artificial Intelligence, John Mayer

UNH Personality Lab

INTRODUCTION: Human personality changes in response to new habits, situations, and maturation, among other causes. As people interact with artificial intelligence (AI), their personalities are likely to change, from their emotional responses to their self-concept.

METHODOLOGY: This theoretical account draws together empirical studies of how personality changed in response to past technological innovations, changes in personal interactions observed in people’s AI usage, changes that arise from the use and disuse of cognitive skills, and works on the self-concept. Research reports in the review were selected according to their relevance and quality.

THEORETICAL STATEMENT: While AI becomes increasingly human-like, humans increasingly …

Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita

The Brown Foundation: Institute of Molecular Medicine

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Exploring Professors’ Lived Experiences Of Interacting With Artificial Intelligence In The Classroom: A Phenomenological Study, Joshua A. Baltzell

Doctoral Dissertations and Projects

The purpose of this hermeneutic phenomenological study was to explore the lived experiences of higher education professors’ interactions with AI in their teaching practices. This study was guided by Ajzen’s theory of planned behavior, which provided a contextual understanding of the concepts that influence higher education professors’ perceptions of AI in their classrooms. A qualitative hermeneutical phenomenological design was employed, utilizing a combination of criterion and snowball sampling. There were 13 participants recruited from higher education institutions from around the United States. Semi-structured individual interviews, a letter-writing exercise, and a qualitative survey were used to collect data from participants. Thematic …

Accuracy Matters: Evaluating The Value Of Ai Tools For Summaries In Metadata, Emily Baldoni, Angela Yon

Faculty and Staff Publications – Milner Library

Since the introduction of ChatGPT in 2022, the potential impact of Artificial Intelligence on library workflows has been a topic of immense interest—and sometimes anxiety—in the library profession. In the area of cataloging and metadata, one frequently cited potential use for AI (particularly Large Language Models (LLMs)) is constructing summaries and abstracts of information resources. However, very little systematic assessment has been done of the characteristics, quality, and utility of AI-generated abstracts for library materials, leaving information professionals with little evidence on which to make data-driven decisions as to how, if at all, to implement AI-assisted workflows for this aspect …

Software-Defined Networking Powered By Ai-Driven Anomaly Detection, Dina Moloja, Vusumuzi Malele Prof

Journal of Cybersecurity Education, Research and Practice

Software Defined Networking (SDN) revolutionizes network control by separating the control plane from the data plane. Although the latter improves SDN agility and scalability, it creates a security hole, particularly in a central control plane, leading to SDN environments becoming high-profile targets for advanced cybersecurity threats. Due to static and signature-based point-in-time behavior, traditional security methods are unable to keep up with modern attacks that are an anomaly to SDNs. Artificial Intelligence (AI) with its different applications and techniques, has the capability of detecting SDN cyber threats’ anomalies. This paper presents the results of a literature scoping exercise that used …

Artificial Intelligence (Ai) In Pharmaceutical Formulation And Dosage Calculations., Sameer Joshi, Sandeep Sheth

PCOM Scholarly Works

Artificial intelligence (AI) is reforming pharmaceutical sciences by renovating traditional drug formulation and dosage calculation approaches. This review provides a comprehensive overview of how AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), are currently being used in pharmaceutical calculations to improve accuracy, efficiency, and personalization. We have explored the role of AI in predicting drug properties, excipient optimization, and formulation design, as well as its applications in pharmacokinetic/pharmacodynamic (PK/PD) modeling, real-time dose adjustment, and precision medicine. Despite significant progress, data quality, interpretability, regulatory acceptance, and ethical considerations persist. Therefore, this review examines the …

Engaging With Ai In A Technical Writing Course: A Collaboration Between A Writing Instructor And A Librarian, Isabel Baca, Joy Urbina

HIIT 2025

After describing our collaboration (a Technical Writing Instructor and a Librarian) on teaching students how to use artificial intelligence (AI) to strengthen their writing, we will engage attendees by having them reflect and practice with AI. For our workshop presentation, attendees will:

• Learn how a librarian and a writing instructor collaborated to teach students to use AI effectively and ethically in their writing.
• Reflect on how they can incorporate AI in their classroom or workplace.
• Learn how a librarian can help them incorporate AI into their courses.
• Practice using AI and developing their prompt engineering skills.

Our workshop presentation will …

Development Of Fire Prediction And Prevention Digital System Algorithms, Oybek Zokirovich Koraboshev

Chemical Technology, Control and Management

This research work is devoted to the development of algorithms for a digital system aimed at early detection, prediction and prevention of fire hazards. In the work, the process of fire hazard assessment is modeled on the basis of modern information technologies and artificial intelligence tools. The main focus is on collecting data in real time, analyzing it and creating algorithms that determine the level of danger. In the process of research, methods of data cleaning, normalization and determination of correlation between variables were used to process multidimensional data streams obtained from various sensors (temperature, smoke, gas concentration and humidity …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

From Prohibition To Preparation: Reframing Academic Integrity In The Age Of Ai, James Hutson

Faculty Scholarship

This study analyzes how U.S. universities reconfigure academic integrity during the 2024–2025 cycle in response to widespread generative AI adoption. The analysis foregrounds three loci: student ignorance and metacognitive blind spots; the expanded remit of Academic Integrity Officers prioritizing education over punishment; and deliberate AI-enabled misconduct that exposes the evidentiary limits of detection technologies. A mixed-methods design integrates a multi-site review at Arizona State University, Montclair State University, and Cornell University with synthesis of surveys, policies, and faculty development guidance. Findings show that detector outputs function as conversational prompts rather than adjudicative proof, necessitating dialogic resolution standards, process evidence, and …

The Ai-Powered Learning Loop In Higher Education, Oualid Abidi, Vladimir Dzenopoljac, Aleksandra Dzenopoljac

All Works

Purpose – This study examines how generative AI tools affect business students’ academic performance by investigating whether flexible AI policies promote deeper learning, enhance self-efficacy and facilitate tacit knowledge acquisition in a Middle Eastern context, while ensuring efficiency and academic integrity. Design/methodology/approach – A qualitative, exploratory study observed 20 final-year business students in Kuwait during five in-class activities using generative AI tools. Semi-structured interviews complemented the researcher’s observations. Thematic analysis revealed patterns in benefits, challenges and learning processes, leading to the development of the AI-powered learning loop framework to explain academic performance outcomes. Findings – The study indicates that generative …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Clinician-Led Development And Feasibility Of A Neural Network For Assessing 3d Dental Cavity Preparations Assisted By Conversational Ai, Mohammed El-Hakim, Haitham Khaled, Amr Fawzy, Robert Anthonappa

Research outputs 2022 to 2026

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and …

Persepsi Mahasiswa Ilmu Perpustakaan Terhadap Penggunaan Perangkat Ai Llm Dalam Pencarian Informasi, Danisya Laila Zahra, Muhamad Prabu Wibowo

Jurnal Ilmu Informasi, Perpustakaan, dan Kearsipan

The increasing use of generative artificial intelligence (AI) among university students is driving changes in the way they seek and manage information, including in academic contexts. ChatGPT and DeepSeek AI are two AI platforms based on Large Language Models (LLMs) that are increasingly utilized as tools to support information seeking processes. This study aims to analyze the preferences of students from the Library and Information Science Program, Faculty of Humanities, Universitas Indonesia (FIB UI), in using these two platforms. The research employs a case study method with a qualitative approach, involving in-depth interviews with ten students. This study explores their …

Face Recognition Using Optimized Multi-Task Cascaded Cnn In Artificial Intelligence Systems, Sami Abduljabbar Rashid, Mustafa Maad Hamdi, Salah Ayad Jassim, Lukman Audah, Baraa Saad Abdulhakeem, Mohammed Salah Abood, Ahmed Adil Nafea

Baghdad Science Journal

Improving the accuracy level of face recognition system is still an open research area where it consists of certain limitations to attain maximum accuracy. In earlier days, several algorithms are developed using machine learning and deep learning models, but still, they need improvement in terms of accuracy. For that purpose, in this article a novel CNN model called the optimized multi cascaded CNN (OPT-MTC-CNN) in the artificial intelligence system is developed, which helps to overcome the accuracy issues in face recognition system. The algorithms that are incorporated in this model are Fully Convolutional Network (FCN), Convolutional Neural Network (CNN), and …

Lung Cancer Classification With High Accuracy Based On Enhanced K- Nearest Neighbor Algorithm, Ahmed Subhi Abdalkafor, Ali Azawii Abdul Lateef

Baghdad Science Journal

Lung diseases have newly increased, with lung cancer being the most serious, as a result of the widespread use of electronic cigarettes and the high demand for them among youth. Misdiagnosis of this disease has resulted in a high death rate around the world, which further complicates matters. So the researchers have tended to concentrate on and study this illness. After extracting the crucial characteristics for this cancer's classification, several studies implemented multiple artificial intelligence algorithms with different techniques, nonetheless, the primary difference between those studies persisted in the classification accuracy. In this paper, after selecting the methods of pre-processing …

The Future Of Nursing Leadership: Incorporating E-Learned Artificial Intelligence (Ai) Pathways With A Precautionary Focus Of Patient-Centered-Care, Jamie Anne Marcus Dr, Bonnette Villalba Webb

FDLA Journal

Artificial Intelligence (AI) is a data-driven mathematical process that incorporates machine-based-logic, usually in the form of algorithms. Education, training, and competencies are now conducted through virtual reality, robotics, simulation, and technology learning-based-platforms by healthcare organizations. This represents a significant change in the future of nursing practice. The adaptability of technology-based-learning platforms can impact the quality and efficiency of learning for some of the workforce population. Nurses' perception of technology and AI-driven nursing practice may vary based on generational orientation and can be a potential barrier to learning, practicing, and adaptability of this framework. The forging of well-trained resilient nurse leaders …

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

Feminist Pedagogy

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

Insights On Ai-Supported Uncrewed And Autonomous Systems Education, Brent A. Terwilliger Ph. D, John Faraca

Publications

Artificial Intelligence (AI) related technology is reshaping the educational experience in programs focused on uncrewed and autonomous systems, aviation, robotics, and aerospace, with growing implications for workforce readiness and cross-sector innovation. Early survey data, capturing student, educator, and employer perspectives, reveals that AI-supported tools are notably changing student engagement, communication, and skills development. Initial indications underscores the importance of AI proficiency and technological familiarity in hiring and workforce development, particularly in technical and operational roles. Key areas of focus include the use of AI to strengthen outreach and interactivity; enrich instruction through intelligent simulations; inform curricular improvements using data analytics; …

Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma

All Works

Background: Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored. Objective: This study aimed to investigate whether retrieval-augmented generation techniques, when combined with knowledge graphs (KGs), could improve the contextual relevance and accuracy of AI-driven clinical decision support. For this, we developed and validated a graph-based retrieval-augmented generation (GraphRAG)–enabled local LLM as a clinical support tool for GDM management, assessing its performance against open-source LLM …