Artificial Intelligence and Robotics Commons^™

Ai Foundations And Applications: Summary Of A Panel Discussion At Loyola University Chicago, George K. Thiruvathukal, Dmitry Dligach, Shilpika, Michael B. Burns, Joseph Vukov, Fraser Turner, Mary Usher

Computer Science: Faculty Publications and Other Works

This document summarizes the panel discussion titled "AI Foundations and Applications," held at Loyola University Chicago as part of the "Forum on Global Affairs: Artificial Intelligence in a Globalized World" series. The panel brought together interdisciplinary experts to discuss the foundational aspects of artificial intelligence (AI), its applications, ethical considerations, and implications for education and society.

Ai In Academia: Supportive Ally Or Cheating Accomplice?, Ian Offner

INSPIRE Student Research and Engagement Conference

• The purpose of this study was to investigate student attitudes toward the use of AI for college class work in a variety of domains.
• For some situations, the use of the AI was collaborative. The students would have to utilize their own abilities in conjunction with the AI as a co-intelligence. For some situations, the AI was a dominant agent, requiring little input from the students.

Ai Isn’T What We Should Be Worried About – It’S The Humans Controlling It, Billy J. Stratton

English and Literary Arts: Faculty Scholarship

Stratton examines depictions of AI in popular media and literature, drawing comparisons to real-world AI and humanity's capacity to harness technology for good or ill.

Evaluating Wrist Placement And Signal Processing Techniques For Real-World Hrv Monitoring Using Ppg, Andrew Murphy

College of Computing and Digital Media Dissertations

This thesis investigates trade-offs between signal quality and data coverage in photoplethysmographic (PPG) heart rate variability (HRV) monitoring using wrist-worn devices. The goal was to evaluate whether wrist placement and signal processing techniques can improve measurement reliability in real-world conditions. Data was collected from healthy participants wearing smartwatches on both wrists during rest and a structured math task introducing natural wrist movement. Three distinct processing methodologies were compared, including a proposed Rolling-Standardized Derivative (RSD) approach. Results showed that while HRV signals from both wrists were highly correlated at rest, motion caused a measurable drop in signal quality and inter-wrist agreement, …

Closing Remarks, Jay Yang

Value and Responsibility in AI Technologies

Closing remarks from director of Gonzaga's Institute for Informatics and Applied Technology, Dr. Jay Yang, with a reception to follow.

Reframing Information Seeking In The Age Of Generative Ai: A Critical And Humanistic Approach, Joseph Kevin Sebastian

Library Faculty Research

Information-seeking has long been the subject of theoretical modeling, often drawing from cognitive, behavioral, computational, and even evolutionary perspectives to explain how individuals navigate, filter, and utilize information. Several dominant frameworks—Carol Kuhlthau’s Information Search Process, Marcia Bates’ Berrypicking Model, Peter Pirolli & Stuart Card’s Information Foraging Theory, Kiyohiko Nakamura’s Information Criteria framework, and Ian Ruthven’s Information Shaping Theory —have provided structured ways of understanding how people interact with information environments. However, while these frameworks offer valuable insights, they often operate within mechanistic or efficiency-driven paradigms, which risk overlooking the complex, embodied, and socioculturally situated nature of human information behaviors. These …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Ceker: A Generalizable Llm Framework For Literature Analysis With A Case Study In Unikernel Security, Alex Wollman, John Hastings

Research & Publications

Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER's ability to generate novel …

Grounding Ai Use In Learning Science: A Conversation With Steven Miller, Steven Miller, Lieven Demeester

CASTLe: Collection of Articles on Scholarship for Teaching and Learning

In this insightful interview, SMU Associate Provost (Teaching and Learning Innovation) Lieven Demeester and Professor Emeritus of Information Systems Steven Miller discuss the integration of artificial intelligence (AI) in teaching and learning, emphasising the importance of grounding AI use in the fundamentals of learning science. They explore the evolving role of education in the context of AI advancements, highlighting the need for educators to focus on the cognitive aspects of learning, such as goal-directed practice and feedback. They also address the potential of AI as a collaborative agent in group projects and the importance of maintaining accountability and quality control …

Impact Of Data Snooping On Deep Learning Models For Locating Vulnerabilities In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validation. The results show that introducing data snooping did not significantly alter model performance, suggesting that data snooping had a minimal impact or that samples randomly dropped as part of the methodology contained hidden features critical to achieving optimal performance. In addition, …

Coding An Assignment Calculator Exclusively With Chatgpt, Andy Tincknell, Heather P. Vandyne, Lisa K. Bell

SACAD: Scholarly Activities

Large Language Models like ChatGPT are influencing higher education and society in broader ways, including the coding and programming of applications and websites (Silva et al., 2024). This poster will profile how Forsyth Library, with no coders on staff, used ChatGPT to program an Assignment Calculator LibGuide without human coding. It details the process, challenges, and outcomes while highlighting AI’s potential to enhance resources for academic success and considers its efficacy and ethical implications.

Ethical Work Cultures & Ai, Andrew Brei

Presentations - 2025

With the help of moral theories, several case studies, and insights from the world of behavioral ethics, my project aims to provide engineering professionals with the means to deal properly with moral issues that commonly arise in their chosen fields.

From Data To Decisions: Safeguarding Athletes In The Age Of Ai, Nathan Elmer

SLU Law Journal Online

Artificial intelligence (AI) and data analytics are transforming professional sports by enhancing player performance, injury prevention, and scouting. However, the rapid adoption of AI raises significant concerns about data privacy, ownership, and decision-making biases that affect athletes. While collective bargaining agreements in major sports leagues provide some protections, they fail to address the complexities of AI-driven data collection and processing. The United States should adopt a regulatory framework similar to the European Union’s General Data Protection Regulation (GDPR) to safeguard athletes’ personal data. Implementing explicit consent requirements, addressing power imbalances, and ensuring transparency in AI decision-making would protect athletes while …

Relationship Between Academic Influence And Institutional Cooperation In Specific Fields：Evidence From The Computer Science Domain, Yukai Yang, Yi Zhao, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/ significance]In scientific collaboration, institutions are the primary driving units of scientific research. Compared to intra-institutional collaboration, inter-institutional collaboration often has the potential to produce high-impact papers. Therefore, studying fine-grained collaboration at the institutional level holds significant importance.[Method/process]To explore the relationship between different types of institutional cooperation and academic influence, this paper classifies institutions and defines various types of cooperation. Using network analysis methods, it investigates the relationship between network indicators of different types of institutional cooperation and academic influence. [Result/conclusion]Taking the computer science domain as an example, the analysis of the relationship between network indicators of different types of …

Development And Evaluation Of The Da Vinci Ai Tutor: Enhancing Accessibility And Personalized Learning In Art History Education, James Hutson, Tiffani Barner

Faculty Scholarship

This study examines the implementation of the Da Vinci AI Tutor, an innovative artificial intelligence (AI)-based tutoring platform designed specifically for enhancing personalized and accessible learning in art history within higher education. Launched in Fall 2024 at a private liberal arts institution in the Midwest, the system integrates a conversational AI avatar modeled after Leonardo da Vinci, incorporating immersive virtual reality environments and multimodal interaction capabilities to engage students across undergraduate survey courses, advanced Renaissance classes, and graduate comprehensive exam preparations. Addressing significant gaps in existing humanities education research, the current study explores two primary research questions: (i) How AI-driven …

Cogprog: Utilizing Large Language Models To Forecast In-The-Moment Health Assessment, Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane Cook

Computer Science Faculty Scholarship

Forecasting future health status is beneficial for understanding health patterns and providing anticipatory support for cognitive and physical health difficulties. In recent years, generative Large Language Models (LLMs) have shown promise as forecasters. Though not traditionally considered strong candidates for numeric tasks, LLMs demonstrate emerging abilities to address various forecasting problems. They also provide the ability to incorporate unstructured information and explain their reasoning process. In this article, we explore whether LLMs can effectively forecast future self-reported health state. To do this, we utilized in-the-moment assessments of mental sharpness, fatigue, and stress from multiple studies, utilizing daily responses (N = …

Control Of Industrial Robots Based On Artificial Intelligence, Bryan Lara Medrano

Open Access Theses & Dissertations

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm …

Enhancing Metacognitive Competencies Through Human-Centered Ai: The Role Of Custom-Trained Intelligent Agents In Workforce Upskilling, James Hutson

Faculty Scholarship

This editorial examines the integration of human-computer intelligent interaction (HCII), specifically through human-centered artificial intelligence (AI) and custom-trained intelligent agents, to foster metacognitive competencies critical for workforce upskilling. With 59% of the workforce projected to require substantial upskilling by 2030, developing personalized AI models tailored to individual cognitive and learning profiles presents an innovative pathway. These custom-trained agents leverage human-computer interaction (HCI) technologies and machine learning methodologies to enhance understanding of one’s own learning processes-metacognition-thus empowering individuals to optimize their future learning and adaptability. This approach not only enhances the individual’s ability to engage effectively with complex tasks in the …

Extending Feature-Based Detection For Artificial Intelligence, Kayla Ahrndt

SPARK Symposium Presentations

AI text generation is rapidly developing, and, as a result, it is becoming increasingly difficult to differentiate it from human written text. Our base study by Leon Fröhling et al. proposed a feature-based detection model trained on GPT2, GPT3, and Grover data, as well as human-generated text. Our work extends their research by training a modified model with four neural networks on word embeddings, select features from the original study, as well as updated data (GPT3, GPT4, and Grover).

Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

David B. Smith Chats With Monday 1.0, David B. Smith

Publications and Research

This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …

Research On Quantitative Evaluation Of Artificial Intelligence Policy Texts In The Yangtze River Delta Region, Ying Zhou, Danjie Yang, Mei Jiang, Xiaochun Zhao

Journal of Scientific Information Research

[Purpose/significance]The quantitative evaluation of existing effective artificial intelligence (AI) policies aims to provide reference for government department to formulate scientific and reasonable AI policies and promote the development of AI. [Method/process]Taking 10 AI policies in the Yangtze River Delta region from 2015 to 2024 as the research samples, the text mining method is used to construct the evaluation index system of AI policies in the Yangtze River Delta region, and conduct quantitative evaluation by combining the PMC index model. [Result/conclusion]The study found that from a macro policy text perspective, the average PMC index of the 10 AI policy samples in …

Heartbeats And Algorithms: Black Pre- Med Students At The Crossroad Of Cardiology And Ai, Shuri Magdalene

Posters - 2025

The rapid integration of artificial intelligence (AI) is reshaping the healthcare landscape. • The underrepresentation of Black/African American (BAA) doctors is alarming with recent data of active physicians from the Association of Medical Colleges in 2021 has highlighted this underrepresentation of Black/ African American physicians in the U.S., with only about 5.7%1 of doctors belonging to this demographic, and cardiologists making up a mere 4.2% of this group1 .

Humanist Copyright, Jane C. Ginsburg

Faculty Scholarship

This exploration of the role of authorship in copyright law proceeds in three parts: historical, doctrinal, and predictive. First, I will review the development of author-focused property rights in the pre-copyright regimes of printing privileges and in early Anglo-American copyright law through the 1909 U.S. Copyright Act. Second, I will analyze the extent to which the present U.S. copyright law does (and does not) honor human authorship. Finally, I will consider the potential responses of copyright law to the claims of proprietary rights in AI-generated outputs. I will explain why the humanist orientation of U.S. copyright law validates the position …

Cutting-Edge Deep Learning Methods For Image-Based Object Detection In Autonomous Driving: In-Depth Survey, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed

Research outputs 2022 to 2026

Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image-based object detection methods, which offer several advantages over other modalities, such as cost-effectiveness and …

2025 Research Day Program, Lincoln Memorial University

Research Day

This program contains poster presentation summaries from LMU's 2025 Annual Research Day conference.

Navigating Generative Ai In Honors, Anne Dotter, Victoria M. Bryan

Journal of the National Collegiate Honors Council Online Archive

Considering the historical significance and pedagogical impact of educational technologies on teaching and learning in higher education, authors suggest that generative artificial intelligence creates more questions than solutions for honors practitioners. What might meaningful AI literacy look like throughout a multidisciplinary honors curriculum? To what extent will generative AI level educational inequities, or create new ones? If policing AI misuse is ultimately a losing battle, how might this understanding reshape assignment design, assessment practices, and even our definitions of academic dishonesty? Drawing on examples in current teaching practice, authors observe the two sides of generative AI—one holding powerful possibilities for …

Editor's Introduction, Amy Mecklenburg-Faenger

Journal of the National Collegiate Honors Council Online Archive

Editorial for Journal of the National Collegiate Honors Council (2025) 26(1), special issue on Forum on AI and Honors.

Ai Responsibilization: Shifting The Burden Of Academic Integrity, Daniel A. Cryer

Journal of the National Collegiate Honors Council Online Archive

Because AI text generators like ChatGPT give students unprece-dented power to outsource their work, concerns about academic integrity are escalating among instructors. This essay suggests that the proliferation of generative artificial intelligence in teaching and learning dramatically shifts the burden of academic integrity, typically shared between teachers and students, onto students. The concept of responsibilization, a defining feature of neoliberal societies in which individuals become responsible for costs and tasks once shouldered collectively, is a useful lens through which to view this new reality. Rather than policing students’ work, educators should recognize the new responsibilities conferred onto students by learning …