ai for education and workforce development
Equipping students and workers to excel in an AI-driven future
As a land-grant university, UT aims to connect not only our students but every Tennessean with innovations in education aligned with 21st-century workforce needs. Many of those needs—today and for years to come—relate to the application of AI in a vast array of industries.
UT faculty are breaking down barriers between people and the benefits of AI. Their research informs teacher preparation programs, curriculum development, and workforce training. They teach educators, students, and professionals to use AI tools confidently, ethically, and productively. They also provide guidance in using AI to create technology and business innovations that solve complex problems beyond personal productivity. Their work is preparing Tennesseans to fill evolving and emerging roles that strengthen the economy.
UT’s Approach
UT is integrating AI into education at every level, from K–12 to higher education to workforce training. On campus, new courses, degree programs, and interdisciplinary faculty communities expand expertise while initiatives like the provost’s AI in Higher Education program guide the adoption of AI tools and policies. Faculty are bringing AI into the classroom with applications ranging from chatbots that support business students to immersive AI environments for language learning.
Beyond campus, we’re helping to lead the national effort to bring AI education to all K–12 students and shaping the ways in which teachers and students develop AI literacy, creativity, and critical thinking. UT researchers are studying AI’s effects on workforce needs and partnering with schools, community colleges, non-profit organizations, and technical programs to design AI-ready career pathways. Through AI TechX, UT extends this work to industry, co-developing training programs in sectors like manufacturing, supply chain, and health care to ensure that Tennessee’s workforce remains competitive in the AI economy.
“Our approach to AI is highly pedagogical: How can we educate ourselves to educate our students to use it responsibly? How can we ensure AI becomes a tool, not to do your work for you, but to assist and empower both educators and students? With a grant from AI Tennessee, we organized a unique symposium bringing together humanities and social sciences specialists, computer scientists, and technologists to enrich collaborative opportunities and outcomes.”
—Anne-Hélène Miller, Associate Professor of French and Riggsby Director of UT’s Marco Institute for Medieval and Renaissance Studies
Highlights
Facilities & Initiatives
UT faculty are working in classrooms and conference rooms, on campus, and around the state to prepare educators, researchers, students, employers, and employees to use and benefit from emerging AI technologies.
- Anderson Center for Entrepreneurship and Innovation
- Center for Enhancing Education in Mathematics and Sciences
- Center for the Dynamics of Cultural Complexity (DySoC)
- Denbo Center for Humanities and the Arts
- JTV Center for Applied Business Analytics
- Language Resource Center
- Marco Institute for Medieval & Renaissance Studies
- National Institute for Computational Sciences
- UT Cluster Hiring Initiatives
- UT Education Research & Opportunity Center
- UT Extension
Researchers
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Associate Professor, Chemical and Biomolecular Engineering
Computation, simulation, theory, cell biology, immunology, intracellular transport, synthetic biology, systems biology, biomembrane, cytoskeleton, nanoparticle, biophysics, machine learning
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Professor, Physics and Astronomy, Electrical Engineering and Computer Science
Algorithm development, quantum materials, condensed matter physics, entanglement, quantum information, machine learning for science, human-machine teaming
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Professor, Mechanical, Aerospace and Biomedical Engineering
Adjoint methods, design optimization, reduced-order models (ROM), machine learning (ML), CFD
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Assistant Professor, Industrial and Systems Engineering
Quantum optimization algorithms; graph theory games
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UT–ORNL Governor’s Chair for Power Electronics
Power systems, smart grid, micro grid, infrastructure reliance, energy policy
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Associate Professor, Mathematics
Machine learning, AI, topological data analysis, differential geometry, graph theory, drug discovery, mathematical biology, quantitative systems pharmacology, numerical methods for PDEs
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Dan Doulet Faculty Fellow & Professor, Industrial & Systems Engineering
Integer programming, stochastic programming, non-linear programming, combinatorial optimization, power systems, scheduling problems, energy markets
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Associate Professor, Mechanical, Aerospace and Biomedical Engineering
Scientific machine learning, decision intelligence, digital twin, fluid dynamics, data assimilation, numerical methods, high performance computing.
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UT–ORNL Governor’s Chair for Advanced Composites Manufacturing
Composites manufacturing, design and product development, recycling and sustainable technologies, hybrids, engineered plastics and high performance materials
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Associate Professor, Chemistry
Theoretical and computational chemistry, machine learning, electronic structure theory, catalysis, noncovalent interactions, molecular topology, chemoinformatics
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Governor’s Chair Professor, Nuclear Engineering
Gas behavior in solids, neutron irradiation effects, plasma surface interactions, nuclear fuel performance
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Assistant Professor, Industrial and Systems Engineering
Big data analytics, physics-informed machine learning, computer simulation and optimization, biomedical and health informatics, data mining and signal processing, sequential decision making, sensor-based modeling and control
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UT/ORNL Governors Chair Professor, Nuclear Engineering and Materials Science and Engineering
Materials science, microstructural characterization, advanced manufacturing, materials by design, radiation effects, extreme environments
