Artificial Intelligence and Robotics Commons^™

Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson

Faculty Scholarship

Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where …

Satirical Deepfakes, Surreal Dreamscapes & Nostalgic Pixels: The Rapid Evolution And Cultural Commentary Of Ai-Aesthetics, Andrew Smith, James Hutson

Faculty Scholarship

The rapid evolution of visual aesthetics driven by AI, shared globally through the internet and social media, has dramatically accelerated what once took centuries to develop. This article explores the unique visual tropes emerging from AI-generated content, characterized by surreal, uncanny, and often unsettling imagery. Examples range from the Dor Brothers' stylized narrative videos to horrifying depictions of transformations, such as people morphing into motorcycles. The article contextualizes this aesthetic within historical developments in creative experimentation, drawing parallels with David Bowie's unconventional approach to sound creation in the 1970s. It also considers how AI-driven art, free from copyright constraints in …

Ai Satire And Digital Dystopia: The Dor Brothers Crafting Imperfection And Political Commentary In Contemporary Video Art, James Hutson, Andrew Smith

Faculty Scholarship

The Dor Brothers' AI-generated video content exemplifies an inflection point in digital creativity, where technological limitations are repurposed as aesthetic tools. Drawing on recent interviews with Yonatan Dor, this article explores the innovative techniques of the brothers, such as masking visual imperfections with retro filters and embracing the unpredictability of AI outputs. Through generating numerous clips and meticulously editing selections, they create a unique aesthetic that juxtaposes surrealism with a gritty realism, often reminiscent of early CCTV or VHS footage. Their work not only transcends the typical "morphing face" trope of AI videos but also engages in satire, using deepfake-like …

Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …

Enhancing History Education With Google Notebooklm: Case Study Of Mary Easton Sibley’S Diary For Multimedia Content And Podcast Creation, Paul Huffman, James Hutson

Faculty Scholarship

This article explores new features of Google’s NotebookLM, an AI-powered tool designed for advanced document analysis and educational content generation. Tested on the 92-page transcribed diary of Mary Easton Sibley, the founder of Lindenwood University, NotebookLM effectively generated FAQs, a study guide, a table of contents, a briefing document, and an audio overview in podcast format. By transforming static historical documents into dynamic learning materials, the document-based AI model provides a user-friendly interface for educators and students, especially those without experience in audio editing or podcasting. While successful in creating study guides and audio formats, the tool faced challenges in …

Developing Empathetic Ai: Exploring The Potential Of Artificial Intelligence To Understand And Simulate Family Dynamics And Cultural Identity, Emily Barnes, James Hutson

Faculty Scholarship

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing …

Technoculture And Language Models In Archaeology: Reconstructing And Preserving Cultural Narratives Through Digital Humanities, James Hutson

Faculty Scholarship

Technoculture, which examines the intersection of culture and technology, has increasingly permeated archaeological practice, transforming both scholarly research and public engagement [1-3]. The introduction of digital tools such as virtual reality (VR), geographic information systems (GIS), and large language models (LLMs) has democratized access to archaeological knowledge, enabling communities to engage more actively with their cultural heritage [4-6]. This short article explores the mutual influence of technocultural studies and AI technologies on archaeology, with a focus on the preservation and reconstruction of cultural narratives through digital means.

The first aspect of this intersection lies in how technocultural tools are creating …

Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …

Beyond Automation: Ai As A Catalyst For New Job Creation In Software Development, Jill Willard, James Hutson

Faculty Scholarship

As artificial intelligence (AI) continues to evolve, its impact on software development and programming is profound, drawing parallels to the shift from assembler to object-oriented programming. This article explores how AI is reshaping the landscape of software jobs, creating new opportunities rather than diminishing them. By simplifying complex tasks and lowering barriers to coding, AI is expanding the technology "pie," introducing new use cases, and enhancing efficiency. The transition from monolithic services to microservices has reduced risks and accelerated deployment processes, and AI is poised to further this evolution by managing the complexities of service interactions through advanced orchestration layers. …

A Framework For The Foundation Of The Philosophy Of Artificial Intelligence, Emily Barnes, James Hutson

Faculty Scholarship

In recent years, the rapid advancement of artificial intelligence (AI) technology has sparked profound questions about the nature of machine intelligence and the possibility of AI consciousness. As AI systems become increasingly sophisticated, examining their philosophical foundations has become imperative. This article investigates the intricate relationship between AI and existential thought, aiming to establish a comprehensive framework for understanding AI's philosophical underpinnings. The historical development of AI, from symbolic AI to contemporary machine learning paradigms, highlights the increasing complexity and sophistication of AI systems, prompting significant philosophical debates about machine consciousness. Theoretical models such as the Independent Core Observer Model …

Bridging The Gap: Ai And The Hidden Structure Of Consciousness, Emily Barnes, James Hutson

Faculty Scholarship

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking …

A Qualitative Study On The Integration Of Artificial Intelligence In Cultural Heritage Conservation, Kholoud Ghaith, James Hutson

Faculty Scholarship

The widespread adoption of generative artificial intelligence (GAI) technologies heralds an era of expanding possibilities in the domain of cultural heritage conservation. This paradigm shift is marked by a confluence of innovative methodologies, including digital twin mapping, digital archiving, and enhanced preservation strategies, aimed at safeguarding the vestiges of our shared past. The application of AI within this field represents a frontier where technology and tradition intersect, offering new vistas for the preservation of historical structures and artifacts that are at risk of deterioration or oblivion. This article endeavors to elucidate the perspectives of professionals within the conservation domain on …

Empirical Insights Into Ai-Assisted Game Development: A Case Study On The Integration Of Generative Ai Tools In Creative Pipelines, Andrew Begemann, James Hutson

Student Scholarship

This study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Simulacra And Historical Fidelity In Digital Recreation Of Lost Cultural Heritage: Reconstituting Period Materialities For The Period Eye, Trent Olsen, James Hutson, Charles O'Brien, Jeremiah Ratican

Faculty Scholarship

The advancement of digital technologies in art history has opened avenues for reconstructing lost or damaged cultural heritage, a need highlighted by the deteriorated state of many artworks from the 1785 Salon. Grounded in the concept of the “Period Eye” by art historian Michael Baxandall, which emphasizes understanding artworks within their original historical and cultural contexts, this study proposes a subfield focused on Reconstituting Period Materialities for the Period Eye. This methodology bridges comprehensive historical research with generative visual artificial intelligence (AI) technologies, facilitating the creation and immersive virtual reality viewing of artworks. Beyond mere visual replication, the approach aims …

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining In Large Language Models (Llms), Jay Ratican, James Hutson

Faculty Scholarship

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

The Impact Of Ai On Ux: Challenges And Opportunities, Susan Stephanie Wells

Theses

Integrating artificial intelligence (AI) in user experience (UX) design is reshaping the field of UX, offering new opportunities and challenges for designers. This thesis project explores the multifaceted relationship between AI and UX design, focusing on the challenges, opportunities, and skills demanded of UX designers in the age of AI. Through a review of academic research and real-world experiences, this project studies the impact of AI on web design processes, UX testing, and data analysis. Key findings highlight the transformative potential of AI in enhancing user experiences, from suggesting website structures to facilitating UX testing and data analysis.

Comparative analysis …

Ethical Imperatives And Challenges: Review Of The Use Of Machine Learning For Predictive Analytics In Higher Education, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal …

Optimizing Adult Learner Success: Applying Random Forest Classifier In Higher Education Predictive Analytics, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This study examines the application of the Random Forest Classifier (RF) model in predicting academic success among adult learners in higher education. It focuses on evaluating the model's effectiveness using key statistical measures like accuracy, precision, recall, and F1 score across a comprehensive dataset from 2013–14 to 2021–22, which includes variables such as age, ethnicity, gender, Pell Grant eligibility, and academic performance metrics. The research highlights the RF model's capability to handle large datasets with varying data types and demonstrates its superiority over traditional regression models in predictive accuracy. Through an iterative process, the study refines the RF model to …

An Exploration Of Companion Robots, Annelyse Lockhart

2024 Student Academic Showcase

Japan and the United States have a drastically different view towards artificial intelligence and smart machines. Within my project, I did an exploratory analysis of robotics within the United States and Japan, and posed the question as to why Japan has substantially more robotics within their day-to-day life. I took an in-depth look at Japanese robotics that do not exist within the United States, as well as explored the biases behind smart machines in both cultures. Judging Category: Exploratory

Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the complex interplay between pre-enrollment socio-cultural and institutional factors and their impact on the higher education landscape. It challenges traditional metrics of academic achievement, presenting a nuanced perspective on student success that emphasizes the importance of socio-economic backgrounds, cultural capital, and K-12 education quality. The analysis extends to the significant role of institutional attributes in shaping student readiness and decision-making processes. The study advocates for the integration of artificial intelligence (AI)-driven assessments by higher education institutions to cater to the diverse needs of the student body, promoting an inclusive and supportive learning environment. Anchored in an extensive …