Across UMKC, faculty are using artificial intelligence to tackle challenges in healthcare, business, law, education and beyond
Henry W. Bloch School of Management Professor Nathan Mauck, Ph.D. is helping business professionals rethink and improve how they use artificial intelligence. His new book with co-author Robert Greene, "AI Co-Thinking: A Framework for Working with AI" introduces a framework for combining human intelligence with artificial intelligence to strengthen reasoning, ask better questions and solve complex problems. Mauck and Greene brought this framework to the Bloch Executive Education’s Business Innovation & Artificial Intelligence Series, where more than 30 participants applied it to real-world challenges. Through interactive exercises, participants practiced using AI as a thought partner. The school also launched a Master of Business Administration in Business Analytics program to teach students how to leverage artificial intelligence, including large language models and emerging technology, to analyze complex data sets and inform strategy. School of Dentistry Rose Wang, Ph.D., is combining artificial intelligence with infrared technology to identify precancerous oral lesions that are at high risk of developing into cancer. Wang uses machine learning to analyze biochemical data from tissue samples collected through infrared spectroscopic imaging. The technology produces thousands of variables at each pixel, making manual analysis nearly impossible. Machine learning models help extract the most important information and provide an objective way to assess risk. The goal is to identify high-risk precancers earlier, when intervention can be more effective. Wang received a $430,000 developmental research grant from the National Institutes of Health to advance the research. School of Education, Social Work and Psychological Sciences Michael Wei, Ph.D. is studying how generative AI can support student learning and professional skill development, and how to help students use it responsibly. He is the external independent evaluator for the “Building AI-Powered Responsible Workforce by Integrating Large Language Models into Computer Science Curriculum” project. The $750,000 National Science Foundation project runs from 2024 to 2027. It brings large language models such as ChatGPT into core computer science courses. As students build programming skills, they also learn prompt engineering, AI-assisted debugging and responsible AI use. Wei’s job is to assess how the project is carried out and what students gain from it. School of Humanities and Social Sciences Associate professor Antonio Byrd, Ph.D. is helping faculty across campus figure out where generative AI fits in teaching and learning. In 2026, Byrd was named UMKC’s first Endowed Provost Teaching Professor. During his two-year term, he will visit classes and lead workshops for faculty and students to implement the recommendations from the UMKC AI Task Force report. His workshops, offered through the Diane Filion Center for Advancing Faculty Excellence (CAFE), range from a hands-on introduction to generative AI to using it in research and writing courses while promoting academic integrity. Byrd also previously had CAFE faculty fellowships focused on writing instruction and AI. Byrd’s research interests include how students’ identities as writers change when they encounter generative AI. Nationally, he co-chairs the Modern Language Association’s Task Force on Generative AI Initiatives and serves on the Conference on College Composition and Communication’s Special Committee on Generative AI. School of Law Ryan Copus, J.D., Ph.D., associate professor of law, is testing whether AI can help predict who will be rearrested. Parole decision-makers rely on that judgment when they weigh public safety against a person’s readiness to return to the community. Copus and UC Davis researcher Hannah Laqueur, Ph.D., asked six large language models to assess rearrest risk using 141 parole hearing transcripts. GPT-5 outperformed a statistical model trained on more than 4,000 cases. Its predictions came closer to the actual outcome in 59% of the transcripts. Claude Fable 5’s predictions closely matched GPT-5’s. Most of the models’ advantage came from correctly identifying lower-risk people whom the statistical model had rated as higher risk. The study also raised a red flag. GPT-5’s written explanations pointed to factors such as remorse and insight. Its predictions, however, more closely tracked criminal history and the severity of the original offense. Copus also teaches courses such as “Data, Decisions and Justice” for students to be able to learn how AI can aid their decisions and how it can fail. School of Medicine Saman Zeeshan, Ph.D., research assistant professor of biomedical and health informatics, is using artificial intelligence to find early warning signs of cancer. Zeeshan’s lab, UMKC LIONS (Lab for Investigating Oncology Networks and Systems), performs cancer genomic and transcriptomic data analysis and AI/ML algorithm development. They have conducted multiple studies examining the application of AI in precision medicine and have built an AI framework integrating multi-omics and phenotypic data through machine learning into a unified framework for disease prediction and patient profiling. The goal is to flag people at a higher risk of disease earlier, which could lead to more personalized treatment and possibly prevention. The lab is applying the models to disparities in lung cancer and subtyping in breast cancer to look for genetic factors and patterns that are missed by traditional analyses. School of Nursing and Health Studies Mei R. Fu, Ph.D.; Michelle Birdashaw, DNP; Christine M. Zimmerman, Ph.D.; Victoria Blackwell; James K. Spence, DNP; and Joy Roberts, JD, MSN are preparing nursing students to be equipped with a skill many nurses only learn once they are in the hospital, which is narrating their care to AI. Many hospitals use AI documentation tools such as Suki, which listens as nurses work and drafts clinical notes automatically for them. The tools work best when nurses clearly say aloud what they are doing and why. Researchers behind the framework described it in the September 2026 editorial in the Journal of Transcultural Nursing. Students first build the nursing knowledge they need to narrate care accurately, then practice narrating clearly in simulations. Finally, they review the notes the AI generate and learn to catch errors, hallucinations and ethical problems. The School of Nursing and Health Studies is running a pilot project to build this clinical narration skill in students before they reach the bedside. School of Science and Engineering Associate professor Mostafizur Rahman, Ph.D. leads a $3 million National Science Foundation Research Traineeship program that trains graduate students to use artificial intelligence to mitigate alcohol and drug abuse. Master’s and doctoral students in the AI-MADA (Mitigating Alcohol and Drug Abuse Using Artificial Intelligence and Secure Networked Sensing) program learn to securely collect, store and analyze data on alcohol and drug abuse through the utilization of noninvasive sensing technologies, widespread wireless technologies and cloud-based cyber infrastructure. They also help build an AI reasoning engine that turns that data into insights about addiction. Professors Yugyung Lee, Ph.D. and Dianxiang Xu, Ph.D. are co-principal investigators on the project. The school also launched a Master of Science in Artificial Intelligence program to lead AI-driven innovation in healthcare, cybersecurity and intelligent systems.
Henry W. Bloch School of Management
Professor Nathan Mauck, Ph.D. is helping business professionals rethink and improve how they use artificial intelligence. His new book with co-author Robert Greene, "AI Co-Thinking: A Framework for Working with AI" introduces a framework for combining human intelligence with artificial intelligence to strengthen reasoning, ask better questions and solve complex problems. Mauck and Greene brought this framework to the Bloch Executive Education’s Business Innovation & Artificial Intelligence Series, where more than 30 participants applied it to real-world challenges. Through interactive exercises, participants practiced using AI as a thought partner. The school also launched a Master of Business Administration in Business Analytics program to teach students how to leverage artificial intelligence, including large language models and emerging technology, to analyze complex data sets and inform strategy.
School of Dentistry
Rose Wang, Ph.D., is combining artificial intelligence with infrared technology to identify precancerous oral lesions that are at high risk of developing into cancer. Wang uses machine learning to analyze biochemical data from tissue samples collected through infrared spectroscopic imaging. The technology produces thousands of variables at each pixel, making manual analysis nearly impossible. Machine learning models help extract the most important information and provide an objective way to assess risk. The goal is to identify high-risk precancers earlier, when intervention can be more effective. Wang received a $430,000 developmental research grant from the National Institutes of Health to advance the research.
School of Education, Social Work and Psychological Sciences
Michael Wei, Ph.D. is studying how generative AI can support student learning and professional skill development, and how to help students use it responsibly. He is the external independent evaluator for the “Building AI-Powered Responsible Workforce by Integrating Large Language Models into Computer Science Curriculum” project. The $750,000 National Science Foundation project runs from 2024 to 2027. It brings large language models such as ChatGPT into core computer science courses. As students build programming skills, they also learn prompt engineering, AI-assisted debugging and responsible AI use. Wei’s job is to assess how the project is carried out and what students gain from it.
School of Humanities and Social Sciences
Associate professor Antonio Byrd, Ph.D. is helping faculty across campus figure out where generative AI fits in teaching and learning. In 2026, Byrd was named UMKC’s first Endowed Provost Teaching Professor. During his two-year term, he will visit classes and lead workshops for faculty and students to implement the recommendations from the UMKC AI Task Force report. His workshops, offered through the Diane Filion Center for Advancing Faculty Excellence (CAFE), range from a hands-on introduction to generative AI to using it in research and writing courses while promoting academic integrity. Byrd also previously had CAFE faculty fellowships focused on writing instruction and AI. Byrd’s research interests include how students’ identities as writers change when they encounter generative AI. Nationally, he co-chairs the Modern Language Association’s Task Force on Generative AI Initiatives and serves on the Conference on College Composition and Communication’s Special Committee on Generative AI.
School of Law
Ryan Copus, J.D., Ph.D., associate professor of law, is testing whether AI can help predict who will be rearrested. Parole decision-makers rely on that judgment when they weigh public safety against a person’s readiness to return to the community. Copus and UC Davis researcher Hannah Laqueur, Ph.D., asked six large language models to assess rearrest risk using 141 parole hearing transcripts. GPT-5 outperformed a statistical model trained on more than 4,000 cases. Its predictions came closer to the actual outcome in 59% of the transcripts. Claude Fable 5’s predictions closely matched GPT-5’s. Most of the models’ advantage came from correctly identifying lower-risk people whom the statistical model had rated as higher risk. The study also raised a red flag. GPT-5’s written explanations pointed to factors such as remorse and insight. Its predictions, however, more closely tracked criminal history and the severity of the original offense. Copus also teaches courses such as “Data, Decisions and Justice” for students to be able to learn how AI can aid their decisions and how it can fail.
School of Medicine
Saman Zeeshan, Ph.D., research assistant professor of biomedical and health informatics, is using artificial intelligence to find early warning signs of cancer. Zeeshan’s lab, UMKC LIONS (Lab for Investigating Oncology Networks and Systems), performs cancer genomic and transcriptomic data analysis and AI/ML algorithm development. They have conducted multiple studies examining the application of AI in precision medicine and have built an AI framework integrating multi-omics and phenotypic data through machine learning into a unified framework for disease prediction and patient profiling. The goal is to flag people at a higher risk of disease earlier, which could lead to more personalized treatment and possibly prevention. The lab is applying the models to disparities in lung cancer and subtyping in breast cancer to look for genetic factors and patterns that are missed by traditional analyses.
School of Nursing and Health Studies
Mei R. Fu, Ph.D.; Michelle Birdashaw, DNP; Christine M. Zimmerman, Ph.D.; Victoria Blackwell; James K. Spence, DNP; and Joy Roberts, JD, MSN are preparing nursing students to be equipped with a skill many nurses only learn once they are in the hospital, which is narrating their care to AI. Many hospitals use AI documentation tools such as Suki, which listens as nurses work and drafts clinical notes automatically for them. The tools work best when nurses clearly say aloud what they are doing and why. Researchers behind the framework described it in the September 2026 editorial in the Journal of Transcultural Nursing. Students first build the nursing knowledge they need to narrate care accurately, then practice narrating clearly in simulations. Finally, they review the notes the AI generate and learn to catch errors, hallucinations and ethical problems. The School of Nursing and Health Studies is running a pilot project to build this clinical narration skill in students before they reach the bedside.
School of Science and Engineering
Associate professor Mostafizur Rahman, Ph.D. leads a $3 million National Science Foundation Research Traineeship program that trains graduate students to use artificial intelligence to mitigate alcohol and drug abuse. Master’s and doctoral students in the AI-MADA (Mitigating Alcohol and Drug Abuse Using Artificial Intelligence and Secure Networked Sensing) program learn to securely collect, store and analyze data on alcohol and drug abuse through the utilization of noninvasive sensing technologies, widespread wireless technologies and cloud-based cyber infrastructure. They also help build an AI reasoning engine that turns that data into insights about addiction. Professors Yugyung Lee, Ph.D. and Dianxiang Xu, Ph.D. are co-principal investigators on the project. The school also launched a Master of Science in Artificial Intelligence program to lead AI-driven innovation in healthcare, cybersecurity and intelligent systems.
