Artificial intelligence is rapidly transforming how people learn and work, and at the University of Illinois Springfield, faculty are ensuring students are prepared to navigate and shape that change.

Across disciplines, UIS faculty are integrating AI into coursework, research and community-focused projects, giving students hands-on experience with tools and concepts that are changing the workforce.

Here are just a few of the ways AI is being used at UIS:

Computer Science

In computer science classrooms, students are working directly with large-scale data and advanced computing systems. Assistant Professor Sunshin Lee has developed hands-on computing environments that allow students to work with artificial intelligence at scale, including Hadoop and Spark clusters, Kubernetes GPU clusters and large-scale social media datasets containing billions of posts. These resources enable students to move beyond simulations and use the same tools and data found in industry and research, giving them experience with real-world applications of AI and data analytics.

Lee is also developing AI-powered educational systems, including tutoring and quiz platforms that provide personalized feedback and guide students through problem-solving. His work focuses on using AI to support learning rather than replace it, helping students build critical thinking skills while also expanding access to education through on-demand support. In addition, he involves students in applied AI research, mentoring projects that allow them to apply AI techniques to real-world problems while developing both technical and analytical skills.

“One of my goals is to use AI not simply to give students answers, but to guide them toward developing critical thinking and problem-solving skills. Current large language models, such as ChatGPT, are effective at generating direct answers, but in education, the more important challenge is helping students learn to think through problems themselves,” Lee said.

This approach also shapes how Lee designs learning tools and classroom experiences. Rather than relying on AI to complete tasks, his systems are built to guide students step by step, reinforcing understanding and encouraging deeper engagement with course material.

Business and Marketing

Faculty in business and technology-related fields are also embedding AI into the curriculum and student projects. Associate Professor of Marketing Shipra Gupta has been leading efforts to integrate artificial intelligence, including curriculum revisions that embed AI applications in core courses as part of the BBA program.

In her own classes, Gupta incorporates open AI tools for applied marketing work such as SWOT analyses, predictive analytics and questionnaire development, while also using AI-driven simulations to enhance experiential learning. These efforts extend across undergraduate and graduate courses, helping students build practical, industry-relevant skills and understand how AI supports data-driven decision-making.

“AI has significantly enhanced both the efficiency and effectiveness of my work, particularly in curriculum development, teaching innovation and student engagement,” Gupta said. “It enables me to introduce students to real-world business applications of AI by integrating tools for data analysis, market research, predictive analytics and content generation into the classroom.”

Gupta also plans to develop a new MBA course, Marketing Research and Analytics, which will focus on the application of artificial intelligence in marketing data analysis and emphasize the generation of customer and market insights to support strategic decision-making.

Music

Artificial intelligence is also being explored in creative disciplines. Hei-Chi Chan, associate professor and department chair of mathematical sciences and philosophy, co-designed a module on AI tools in music education for an introductory course, working with adjunct faculty member See Tsai Chan. The module introduces students to ways AI can be used in K-12 music classrooms while also encouraging creativity and experimentation.

As part of the course, students explored interactive AI music tools such as Google’s Viola the Bird and Blob Opera, which allow users to create music through simple movements while AI adjusts pitch and harmony in real time. These tools help demonstrate how artificial intelligence can enhance musical learning and engagement, making complex concepts more accessible and interactive for students.

Management Information Systems

In management information systems, Assistant Professor Sahar Farshadkhah has contributed to the development of generative AI training and curriculum, reflecting a growing emphasis on equipping students with practical knowledge of emerging technologies.

“I believe it is important for our graduate students to learn how to use generative AI tools responsibly and effectively in both their personal lives and future professional roles,” Farshadkhah said. “As this is an emerging and rapidly evolving area, we are all learning alongside its development. I make every effort to educate my students and increase their awareness of both the benefits and potential risks of using generative AI tools.”

Public Management and Policy

Beyond the classroom, faculty are also exploring how AI can support decision-making in the public sector. Professor Richard Funderburg is developing a next-generation decision support system that uses agentic artificial intelligence to provide governments with the business intelligence they need to negotiate better tax incentives and economic development subsidies for the benefit of their taxpayers. His work aims to improve community investment decisions for economic development, enabling local economies to attain goals for growth and human development, while improving managerial efficiency to decrease nonproductive spending and better target incentive programs to reach distressed communities.

The project brings together researchers from across the University of Illinois System, combining expertise in business tax accounting, economic development policy and computer science. By modeling how incentives affect business profitability across federal, state and local systems, the team can estimate how different incentive packages influence where companies choose to invest. Using agentic AI, the system can scale these calculations across millions of potential business scenarios, offering governments a more comprehensive view of how to structure competitive and effective incentive strategies.

“State and local governments cannot know a developer’s return on investment from the tax incentives they offer to a development project until long after contracts are finalized, facilities have been constructed, and workers have been added to the payroll unless the developer voluntarily reveals the information beforehand. Developers have an incentive to withhold the true value of incentives as part of negotiation strategy. State and local governments need critical information about the value of incentives before project vesting, early in negotiations.”