Digital Commons Network^™

Integrating Art And Ai: Evaluating The Educational Impact Of Ai Tools In Digital Art History Learning, James Hutson

Faculty Scholarship

This study delves into the burgeoning intersection of Artificial Intelligence (AI) and art history education, an area that has been relatively unexplored. The research focuses on how AI art generators impact learning outcomes in art history for both undergraduate and graduate students enrolled in Ancient Art courses, covering eras from ancient Mesopotamia to the fall of Rome. Utilizing a mixed-methods approach, the study analyzes AI-generated artworks, reflective essays, and survey responses to assess how these generative tools influence students’ comprehension, engagement, and creative interpretation of historical artworks. The study reveals that the use of AI tools in art history not …

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …

An Overview Of The Relationships Between The Food Industry And Nanotechnology, Mehdi Koushki, Nasrin Amiri-Dashatan, Hossein Pourghadamyari, Hadi Khodabandehloo, Fatemeh Bagheri, Masoumeh Farahani, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Background and Objective: Due to the growth of the global population, food demands are increasing. Hence, the need to develop more efficient methods for producing better quality, safer, and more sustainable food seems essential. In the past decades, the use of nanoscale materials has increased greatly due to the unique chemical, physical, and biological characteristics of nanomaterials compared to bulk materials. This research presents nanotechnology role in improving sensorial properties (taste, appearance, and texture) and safety aspects as well as processing and packaging of foods. The use of nano-omics-based technologies and artificial intelligence-nanotechnology-based technologies in the food industry is also …

Classification Of Instructional Activities In Classroom Videos Using Neural Networks, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos have become an integral part of classroom observation and instructional quality studies. Researchers encounter substantial financial and time-consuming costs gathering trained personnel to catalogue and analyze a large quantity (i.e., hundreds of hours) of classroom videos. In this chapter, we argue that deep-learning neural networks have potential to help with these tasks by detecting features of instruction in videos. We evaluated the performance of three neural networks in detecting instructional activities in classroom videos. The video dataset included a total of forty-six hours, which was evenly split between elementary mathematics and English language arts lesson recordings. Results showed …

Looking For The Answer: Exploring The Choice Between Ai And Search Engines, Joseph Driscoll

Honors Theses and Capstones

With the meteoric rise of artificial intelligence, consumers have found themselves faced with an abundance of options for information retrieval. This study is designed to explore when consumers choose to use an AI tool as opposed to a traditional search engine, as well as determining what the user segments look like for AI tools. The results of this study indicate that there are two AI user segments, along with a third segment of non-users that were not included in the study. One segment has fully embraced the use of AI, while the other has tested it and only uses it …

Evaluating If Ghana's Health Institutions And Facilities Act 2011 (Act 829) Sufficiently Addresses Medical Negligence Risks From Integration Of Artificial Intelligence Systems, George Benneh Mensah, Pushan Kumar Dutta

Mesopotamian Journal of Artificial Intelligence in Healthcare

With artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilistic nature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate …

Building A Human Digital Twin (Hdtwin) Using Large Language Models For Cognitive Diagnosis: Algorithm Development And Validation, Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

Computer Science Faculty Scholarship

Background: Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model. Objective: This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences. Methods: HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, …

Heuristic Machine Learning Approaches For Identifying Phishing Threats Across Web And Email Platforms, Ramprasath Jayaprakash, Krishnaraj Natarajan, J. Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik

Data Science Faculty Publications

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. …

The Educational Affordances And Challenges Of Chatgpt: State Of The Field, Helen Crompton, Diane Burke

STEMPS Faculty Publications

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub …

A Generative Approach For Document Enhancement With Small Unpaired Data, Mohammad Shahab Uddin, Wael Khallouli, Andres Sousa-Poza, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Shipbuilding drawings, crafted manually before the digital era, are vital for historical reference and technical insight. However, their digital versions, stored as scanned PDFs, often contain significant noise, making them unsuitable for use in modern CAD software like AutoCAD. Traditional denoising techniques struggle with the diverse and intense noise found in these documents, which also does not adhere to standard noise models. In this paper, we propose an innovative generative approach tailored for document enhancement, particularly focusing on shipbuilding drawings. For a small, unpaired dataset of clean and noisy shipbuilding drawing documents, we first learn to generate the noise in …

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …

A Comparison Of Lexical Tokenization Methods, Nathan Culmer

Williams Honors College, Honors Research Projects

The purpose of this project was to compare tokenization methods, or methods of breaking up a text into meaningful parts for use in natural language processing. The effectiveness of several commonly used tokenization methods were investigated, including morpheme tokenization, which takes into account the linguistic features of the language. In addition, I proposed and implemented a new technique to consider the capitalization pattern of a word in the tokenization process, in order to allow this process to include more natural language features. The effectiveness of these methods was compared by using them in a sentiment analysis model for various datasets, …

A Phenomenological Study Of Teachers' Perception Of Professional Development Sessions Focused On Artificial Intelligence: Utilizing The Tpack Framework To Address The Professional Development Needs Of Teachers, Xuanyi Chen

Theses and Dissertations

AI-enabled educational tools have great potential for enhancing learning and teaching, yet there is a notable lack of study on AI-focused professional development for K-12 teachers. This phenomenological study employed the TPACK framework to examine teachers' incorporation of AI and engagement in professional development activities. The use of a social constructivism framework explored teachers' views on knowledge, learning, and motivation. Data were collected from 12 K-12 teachers across various districts in a large metropolitan area who attended AI professional development sessions in their own context and from that experience created AI artifacts. Primary data included (a) semistructured interviews, (b) professional …

The Information Age For Education Via Artificial Intelligence And Machine Learning: A Bibliometric And Systematic Literature Analysis, Hassan Abuhassna, Fareed Awae, Mohamad A.B.M. Adnan, Mahyudin Daud, Abdulla S.B. Almheiri

All Works

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in education is a rapidly evolving field, yet the long-term implications and actual impacts on student learning outcomes require more in-depth study. Address this gap, our study offers a novel approach combining bibliometric analysis and a Systematic Literature Review (SLR), guided by the PRISMA methodology. The first phase, a comprehensive bibliometric analysis, identified key nations, educational institutions, journals, keywords, and influential authors in the realm of AI/ML in educational settings. This phase provided a macro-level understanding of the field’s landscape, showcasing the global and interdisciplinary nature of AI/ML research in …

Diagnostic Performance Of Ai-Based Models Versus Physicians Among Patients With Hepatocellular Carcinoma: A Systematic Review And Meta-Analysis, Feras Al-Obeidat, Wael Hafez, Muneir Gador, Nesma Ahmed, Marwa Muhammed Abdeljawad, Antesh Yadav, Asrar Rashed

All Works

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain. Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma. Methods: We searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. …

Online Learning For Adaptive Control : Stable Learning And Control For Aerospace And Robotics, Jacob G. Elkins

Dissertations

Aerospace and robotic systems perform various tasks in uncertain, dynamic environments. Further, the data that a system encounters in the real-world is often the most valuable. The most advanced aerospace and robotic systems of the future will be able to learn online, during operation, from this real-world data. Aerospace and robotic systems are often expensive and difficult to model, and the controllers of these systems thus require rigorous control proofs and safety guarantees. Recently, general research in artificial intelligence and machine learning (AI/ML) has made significant strides in developing learning-based systems and controllers. However, much of this research has focused …

Music Recommendation Using Exemplars And Contrastive Learning, Tina Tran

Honors Undergraduate Theses

The popularity of AI audio applications is growing, it is used in chatbots, automated voice translation, virtual assistants, and text-to-speech translation. Audio classification is crucial in today’s world with a growing need to sort and classify millions of existing audio data with increasing amounts of new data uploaded over time. In the area of classification lies the difficult and lucrative problem of music recommendation. Research in music recommendation has trended over time towards collaborative-based approaches utilizing large amounts of user data. These approaches tend to deal with the cold-start problem of insufficient data and are costly to train. We look …

Harnessing The Potential Of Artificial Intelligence In Managing Viral Hepatitis, Guma Ali, Maad M. Mijwil, Ioannis Adamopoulos, Bosco Apparatus Buruga, Murat Gök, Malik Sallam

Mesopotamian Journal of Big Data

Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Reflecting On Addressing Academic Integrity Through Authentic Assessment, Sharon Lehane, Angela Wright

Conference Materials

With the advancement of Artificial Intelligence and chatbot technologies such as ChatGPT, little else is discussed currently by colleagues in Higher Education. Why? – because their potential disruption for assessment practices in higher education are so uncertain. Advances in these artificial intelligence technologies threaten the legitimacy of assessment and academic integrity, calling conventional assessment methods into question. The widespread utilisation of ChatGPT emphasizes the need for sustainable educational practices such as authentic assessments that focus on higher-order critical thinking and problem-solving skills. In terms of the methodology for this study, a critical examination of the association between authentic assessment design …

An Assessment Of The Hotel Customers’ Robotic Service Quality Perceptions: Scale Development And Validation, Ahmet Hacikara

Graduate Thesis and Dissertation 2023-2024

"Hotel customers' interactions with service robots are becoming increasingly common, raising questions about how these interactions impact their experience. To address this gap in knowledge, this dissertation develops and validates a novel robotic service quality (ROBOQUAL) scale. Drawing on social exchange theory, the study examines the reciprocal relationship between hotel customers and artificially intelligent service robots, exploring how robotic service quality influences customers' perceptions of experience quality, satisfaction, and word-of-mouth intentions. The research employs focus-group studies, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and partial least squares equation modeling (PLS-SEM) to develop and validate the ROBOQUAL scale."

Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu

Engineering Technology Faculty Publications

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …

Integrating Artificial Intelligence In Public Relations And Media: A Bibliometric Analysis Of Emerging Trends And Influences, Akhmed Kaleel, Mohammed Shukri Alomari

Iraqi Journal for Computer Science and Mathematics

Integrating artificial intelligence (AI) techniques in public relations and media is an emerging interdisciplinary research domain warranting greater attention. This study presents the first bibliometric analysis of recent literature at the nexus of AI, public relations, and media. Publications from 2018-2023 were retrieved from Scopus and analyzed touncover productivity, impact, collaborations, and topics. Results showed rising annual outputs with over 2000 articles published in 2021, confirming intensifying research activity. Recent publications also demonstrated higher citation impact, indicating their contemporary influence. Prolific authors were predominantly China-based, while the US led overall production. China, Western nations, and India dominated but opportunities exist …

K-12 Teachers’ Ai Literacy: A Key Driver Of Ai Integration Into Teaching Practices, Sedide Gulsah Pekcan

Graduate Theses/Dissertations

This thesis examined the correlation between K-12 teachers' artificial intelligence (AI) literacy and their integration of AI tools and resources into their teaching practices. The research question asked, “What is the impact of K-12 teachers’ AI literacy on the integration of AI tools and resources into their teaching practices?” i.e., whether teachers with higher levels of AI knowledge and skills are more likely to incorporate AI-powered tools in their teaching methods. The study hypothesizes that educators who are more proficient in implementing AI will be more likely to adopt AI-driven resources in their teaching practices. The study uses a correlational …

Enhancing Control Room Operator Decision Making, Joseph Mietkiewicz, Ammar N. Abbas, Chidera W. Amazu, Gabriele Baldissone, Anders L. Madsen, Micaela Demichela, Maria Chiara Leva

Articles

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. …

"Ai's Pompous Vocabulary Sounds Like It's Trying To Impress Someone": Preservice Teachers' Respond To Ai Reviews Of Their Writing, Jennifer Kidd, Keri Parker, Samantha Myers

Teaching & Learning Faculty Publications

This study examines pre-service teachers’ (PSTs’) responses to using AI to review their writing. As part of a class activity, PSTs prompted an AI-chatbot for feedback and revisions on a short essay based on a provided assignment rubric. Afterward, they reflected on the pros and cons of using AI in this way. While some PSTs initially encountered functionality issues, all were able to obtain feedback and the majority reported finding it useful. In general, the PSTs appreciated AI’s speed and expertise and the potential to customize feedback based on their needs. However, many were skeptical of AI’s evaluation of their …

Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai

Dartmouth College Master’s Theses

Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, …

Chatgpt, Ai Large Language Models, And Law, Harry Surden

Publications

This Essay explores Artificial Intelligence (AI) Large Language Models (LLMs) like ChatGPT/GPT-4, detailing the advances and challenges in applying AI to law. It first explains how these AI technologies work at an understandable level. It then examines the significant evolution of LLMs since 2022 and their improved capabilities in understanding and generating complex documents, such as legal texts. Finally, this Essay discusses the limitations of these technologies, offering a balanced view of their potential role in legal work.

Ai Malpractice, Bryan H. Choi

Publications

Should AI modelers be held to a professional standard of care? Recent scholarship has argued that those who build AI systems owe special duties to the public to promote values such as safety, fairness, transparency, and accountability. Yet, there is little agreement as to what the content of those duties should be. Nor is there a framework for how conflicting views should be resolved as a matter of law.

This Article builds on prior work applying professional malpractice law to conventional software development work, and extends it to AI work. The malpractice doctrine establishes an alternate standard of care—the customary …

Leveraging Ai And Big Data In Low-Resource Healthcare Settings, Ahmed Hussein Ali, Saad Ahmed Dheyab, Abdullah Hussein Alamoodi, Aws Abed Al Raheem Magableh, Yuantong Gu

Mesopotamian Journal of Big Data

Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have …