The theme for DISC 2024 Debate will be "AI in Education," with panelists approaching the topic from different backgrounds and research contexts, enriching the conversation and broadening its impact. The discussion will explore areas of consensus and divergence on AI's future in education, covering a wide range of topics. These include the potential disruption of traditional teaching methods by large language models, the rapid pace of AI development outpacing educational research, and how institutions can adapt. Additionally, the conversation will address issues such as equity and bias in AI systems, the evolving role of educators in AI-enhanced classrooms, challenges to academic integrity with AI-assisted tools, and the growing need for AI literacy.
Tyler Wheeler is a fixed-term Assistant Professor in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University (MSU). His research focuses on algorithm development and machine learning applications for nuclear physics research. He earned his B.S. in Physics from Grand Valley State University, where he investigated molecular networks and metal-organic frameworks using positron annihilation lifetime spectroscopy. Tyler obtained a dual PhD in Physics and CMSE from MSU, where his work at the Facility for Rare Isotope Beams (FRIB) marked a shift toward experimental nuclear astrophysics. His research at FRIB focused on the thermonuclear rate of the 15O(α, γ)19Ne reaction by measuring the beta delayed charged particle emissions in the decay of 20Mg. Tyler contributed to the design and construction of the upgraded GADGET II time projection chamber (TPC), with which the data for these measurements was acquired at FRIB. His work also explored innovative deep learning applications for analyzing TPC data.
Marcos D. "Danny" Caballero is a Professor in Physics and Astronomy and Computational Mathematics, Science, and Engineering. He holds the Lappan-Phillips Chair of Physics Education, co-directs the Physics Education Research Lab, and leads research in the Learning Machines Lab and Computational Education Research Lab. Danny studies how tools and practices influence learning in physics and computational science.
He earned his B.S. in physics from the University of Texas and his M.S. from Georgia Tech, where he co-founded the Physics Education Research group. His Ph.D. from Georgia Tech focused on computational modeling instruction. Danny moved to the University of Colorado Boulder as a postdoctoral researcher and helped transform upper-division physics courses to more active learning environments.
Danny conducts research from the high school to the upper-division and is particularly interested in how students learn through their use of tools such as mathematics and computing. His work employs cognitive and sociocultural theories of learning and aims to blend these perspectives to enhance physics and computational science instruction at all levels. His projects range from the fine-grained (e.g., how students engage with particular computational ideas) to the course-scale (e.g., what kind of computing students are able to do after instruction) to the very broad (e.g., how do departments value computation). His work includes the use of data science to address questions in STEM education.
Michael Lachney is an associate professor in the Educational Psychology and Educational Technology program in the College of Education. He has a PhD in Science and Technology Studies from the Rensselaer Polytechnic Institute, where he learned about critical theories of technology and qualitative methods.
His research explores the cultural politics of educational technology, including how technology and race co-shape each other in school and out-of-school contexts. In addition, he works on educational technology design strategies and implementation tactics to help educators collaborate with community experts (e.g., braiders, urban gardeners, youth sports coaches, etc.) in culturally responsive science, technology, engineering, and mathematics education.
Michael's research has appeared in the journals, Computer Science Education, Interactive Learning Environments, Learning, Media and Technology, Science as Culture, among others in the fields of STS and educational technology. His most recent project explores how to theorize and ethically construct relationships between humans, nonhuman animals, plants, and technologies in educational technology research.
B.S., 1975, Univ. of Manchester, England
M.S., 1976, Univ. of Manchester, England
Ph.D., 1978, Univ. of Manchester, England
Melanie Cooper holds a joint appointment in the departments of Chemistry, Teacher Education and the CREATE for STEM Institute. Dr. Cooper conducted postdoctoral work in organic chemistry before moving to a focus on chemistry education research. Her current research focus is the development and assessment of evidence-based curricula in order to improve the teaching and learning of chemistry within large-enrollment undergraduate chemistry courses.