10/28/2026
Grainger Engineering faculty are helping define the future of engineering education, setting new standards for AI fluency and responsibility through curricular innovation, human-centered teaching, and a commitment to ensuring AI strengthens learning without compromising rigor.
Artificial intelligence is transforming how quickly engineers solve problems and advance discovery. At The Grainger College of Engineering, one principle remains clear: AI may change how engineers work, but it cannot replace what they know, understand, or remain accountable for.
As AI rapidly reshapes how students learn, the college is preparing students to do more than use the technology. They must also develop deep engineering fundamentals, technical mastery, and the judgment to question, verify and responsibly apply what AI produces.
“When I think about the future of undergraduate education in engineering, my concern is that AI is very good at removing friction,” said Jonathan Makela, associate dean of Undergraduate Programs and Abel Bliss Professor of Engineering with the Department of Electrical and Computer Engineering. “You can place something in a chatbot, and it'll give you the answer right away. However, learning requires friction, and that underscores the reason our students come to Grainger Engineering. They chose an environment where they will be challenged.”
The risk AI poses to engineering education is not that students use it. It is that they skip the struggle where understanding forms. Every engineering and computing program now has to decide how to guard against that. At Grainger Engineering, the answer begins with a commitment to preserving the rigor of engineering education while embracing the possibilities of AI.
“You can place something in a chatbot, and it'll give you the answer right away. However, learning requires friction, and that underscores the reason our students come to Grainger Engineering. They chose an environment where they will be challenged.” — Jonathan Makela, associate dean of Undergraduate Programs
Dean Rashid Bashir argues that the fundamentals built through that struggle will become more valuable, not less, as AI advances.
“It is critical to train all our students to be AI fluent. At the same time, I believe in ensuring that our students have the fundamental math, physics and chemistry-based understanding of engineering – as this will become even more important while AI technology evolves and becomes more pervasive,” Bashir said. “Our college will continue to emphasize depth and rigor in instruction while also ensuring that all our students are AI fluent.”
Adapting pedagogy and curricula to a changing world is nothing new at the University of Illinois Urbana-Champaign. For more than six decades, the university has defined how technology serves learning.
That work began in 1960 with PLATO, the distributed computer-based learning system created at Illinois and the first generalized computer-assisted instruction system.
“The U. of I. didn’t wait for computing to reshape education. We built the tools that did it. PLATO showed that technology could change how and where students learn. Then as computing and data science became essential for more disciplines, we developed ways to scale education, both on campus and online,” said Nancy Amato, Abel Bliss Professor of Engineering and director of the Siebel School of Computing and Data Science.
“We pioneered CS + X degrees to effectively blend computing with other disciplines. Then we did the same with X + DS degrees, blending data science with other disciplines.
“Now, we’re excited and ready to do the same with AI – both using it to enhance learning and research and to design new X + AI blended degrees.”
That legacy also continues today with tools like PrairieLearn, an online assessment and learning system developed by faculty at Grainger Engineering and now used by instructors at other universities. It enables faculty to create meaningful, scalable assessments while identifying where students need additional support, whether through AI tutoring or in-person instruction.
Rethinking what it means to be AI fluent
Grainger Engineering students in every discipline must graduate not as passive consumers of AI, but as engineers capable of leading in an AI-enabled world. They must be equipped to evaluate AI-generated results, reconcile evidence, explain their judgments and make responsible, transparent decisions when using AI agents and large language models.
Delivering on this standard requires coordinated changes in what students learn, how they learn and how their knowledge is assessed. That transformation is already underway at scale.
This fall, AI modules in the required orientation courses ENG 100 and ENG 300 are introducing students to effective learning with AI, responsible use of the technology and how large language models are trained and work. Students now complete required introductory videos and quizzes that establish a foundational understanding of AI. In subsequent classes, they learn how to use AI to support learning, not just complete assignments.
“AI will influence the future engineering workforce and the engineering products we build. The sooner that students can learn to evaluate its utility and challenges, the better prepared Grainger engineers will be to contribute to the workforce they will soon enter.” — Holly Golecki, teaching associate professor, Department of Bioengineering
“This represents one step in making sure all Grainger Engineering students gain AI fluency during their time with us,” Makela said. “Beyond that, our goal is for students to form a foundational understanding of how the technology works. They will learn how it applies or doesn’t apply to the problems they examine in class, work groups and laboratories.”
Siebel School of Computing and Data Science Professor Derek Hoiem, co-chair of the Campus AI Curriculum Task Force, said students are first learning how AI works before being asked to evaluate a tool they have yet to understand.
Students are also examining the implications of AI use, including environmental effects, output verification and academic integrity.
Beyond these introductory classes, Hoiem points to “AI-enhanced course adaptation across majors” that continues throughout a student’s time on campus.
This past summer, the seven-week AI Enhanced Course Initiative (AICI) focused on updating courses to incorporate or adapt to AI. Instructors had access to AI and pedagogical experts for support.
This initiative culminated in faculty demonstrating how they are using AI to focus on what students need to learn and how to measure how well they're learning it.
Between AI implementation in ENG 100 and 300, as well as the 12 courses impacted by AICI, Grainger Engineering efforts will reach more than 4,600 undergraduate students this academic year, representing 45% of Grainger Engineering undergraduate students.
Faculty also understand students’ needs expand beyond the classroom and into industry. Engineering Career Services collaborated with the student-led Engineering Council to host multiple Industry Insights panels during which industry experts gave students the chance to hear how enterprises use AI and what they look for in hiring. Industry partners, in turn, gain access to a pool of highly trained future employees.
There is a real concern that AI integration will create dependency rather than learning, making safeguards essential to ensure students develop and demonstrate their own understanding. At Grainger Engineering, students, faculty and employers can rely on trustworthy, accessible evaluation as evidence of rigorous learning.
Hoiem said that begins with the Computer-Based Testing Facility (CBTF), which “supports testing at a large scale, available at many times during the week so that instructors can verify what students have learned without relying on AI.”
By crafting the right resources available to both Grainger Engineering students and faculty, the integrity of the assessment and learning infrastructure is strengthened, verifying what a student actually knows.
Rigor at scale takes a whole college
Part of what makes all this possible at Grainger Engineering is the depth of expertise the college can draw upon, including faculty who not only use AI but also build it.
That expertise enables coordinated change and investment at scale, putting the right tools, learning opportunities, and resources in the hands of those who need them.
“Grainger Engineering is taking the leadership position when it comes to the integration of AI, not just in instruction and teaching, but in all aspects of academic life,” said Siebel School Teaching Associate Professor Abdussalam Alawini.
He points to the college’s ongoing reinvestment in faculty and curriculum.
For example, the multiyear Strategic Instructional Innovations Program powers collaboration across college departments, campus, and peer institutions.
Investments are giving faculty the resources and support to experiment, adapt, and advance new approaches to teaching as AI continues to evolve.
“All of this is done to integrate AI in the best way possible at Grainger Engineering,” Alawini said. “Given the resources that we have, the brain power, the people that are actually in the cutting-edge technology development side of AI and infrastructure, we aim to use our resources to lead the right way forward.”
To address the accelerating pace of AI development, departments are incorporating AI-based assignments to provide students with a holistic understanding of the technology’s potential in their areas of study.
“I've seen many great examples of faculty helping students to be critical and ethical AI users, while centering the students' roles on bringing their own engineering judgment to every engineering challenge,” Golecki said. “I introduce students to emerging tools, demonstrate how to evaluate these tools, and consider ethics of their use in healthcare and bioapplications.
“Keeping students critical and capable of evaluating these changes can help them prepare for how AI may impact the future of bioengineering as a field.”
Additionally, human-centered engineering builds on these concepts by encouraging students to consider not only whether a solution works, but whom it serves and how it will be used.
“Human-centered engineering is a critical part of instruction,” Makela emphasized. “AI can spit out an optimal solution, perhaps, but that solution, if you don't understand where it comes from and who's going to be using it, may not actually be adoptable.”
Bashir spoke about this concept at the “AI, Learning, and the Future of Human Potential” panel at Techcon SoCal in May.
“At Grainger Engineering, we are educating engineers well-versed in AI. More than that, they will be the leaders who develop and apply AI responsibly, constructively and positively to make the world a better place,” he said.
The future of engineering education will not be defined by how quickly institutions introduce AI into the classroom, but by how thoughtfully they integrate it into the learning experience.
Here, that means taking the time to examine what students need to learn and investing in instructional approaches that support that learning. It also means keeping people at the center of technological innovation, ensuring students understand not only what AI can do, but the responsibility that comes with using it.
AI may remove friction from the engineering process. But knowing when to preserve that friction, and why it matters, may be one of the most important lessons engineering education can teach.
Grainger Engineering Affiliations
Abdussalam Alawini is an Illinois Grainger Engineering teaching associate professor of computer science.
Nancy Amato is an Abel Bliss Professor of Engineering and school director of the Siebel School of Computing and Data Science. Amato is affiliated with Beckman Institute, Carl R. Woese Institute for Genomic Biology (IGB), Coordinated Science Lab (CSL), Department of Electrical and Computer Engineering (ECE), and the National Center for Supercomputing Applications (NCSA).
Rashid Bashir is Dean of The Grainger College of Engineering, Vice Chancellor Chicago Strategic Partnerships and professor of bioengineering (BIOE). Bashir holds the Grainger Distinguished Chair in Engineering and is affiliated with Beckman, Carle Illinois College of Medicine (CIMED), College of Liberal Arts & Sciences (LAS), Department of Electrical & Computer Engineering (ECE), Department of Materials Science & Engineering (MatSE), Department of Mechanical Science & Engineering (MechSE), IGB, Materials Research Laboratory (MRL), and NCSA.
Holly McIlwee Golecki is a Grainger Engineering teaching associate professor of bioengineering. Golecki is affiliated with MechSE, Siebel Center for Design, CIMED and Social and Behavioral Science Institute.
Derek Hoiem is a Grainger Engineering professor of computer science, and he is affiliated with ECE and CSL.
Jonathan Makela is an Abel Bliss Professor of Engineering within the Department of Electrical and Computer Engineering and is associate dean of Undergraduate Programs with Grainger Engineering. Makela is affiliated with CSL.