How NIU is preparing students for an AI-enabled world

August 27, 2026

The following update was shared by Jason Rhode, vice provost for Academic Innovation and Lifelong Learning, at the Academic Affairs, Student Affairs and Personnel Committee meeting of the NIU Board of Trustees on August 27, 2026.

When I last presented to this committee in May 2024, we were still in a responsive phase. Faculty were asking how to adapt assignments, how to talk with students about generative AI and how to use these tools responsibly.

Two years later, that conversation has matured. We are still supporting responsible use, but the central question is now more forward-looking: How do we intentionally prepare every Huskie to learn, work and lead in a world increasingly shaped by AI? Today I will focus on what that looks like in the curriculum and especially on the faculty-led innovation now taking shape across NIU.

From responding to AI to preparing students for AI

The easiest way to understand our progress is as a shift across three phases.

In 2023 and 2024, the work was largely responsive. We listened to faculty concerns, built the AI in teaching toolkit, offered panels and workshops, and piloted AI capabilities within Blackboard. That was appropriate for a moment when the technology was new and changing quickly.

During 2024 and 2025, the Academic Affairs AI Task Force helped us establish a more durable institutional framework: ethical guidelines, an AI literacies framework and a rubric for evaluating AI tools.

This past year, we have moved increasingly into integration and innovation: the AI network, the student guide to AI, the AI at NIU website, and most importantly for today’s conversation, intentional redesign of courses and programs. The shift is from managing disruption to designing learning for an AI-enabled future.

What does an AI-ready NIU graduate look like?

That leads to an important question: What does an AI-ready NIU graduate look like?

Our AI literacies framework gives us a useful progression. At the first level, students need knowledge: the vocabulary, concepts, benefits and limitations of AI. At the second level, they need to use AI tools effectively and transparently.

But our aspiration cannot stop at use. Students also need critical analysis: the ability to recognize hallucinations, flawed reasoning, bias, ethical concerns and broader societal impacts. And at the highest level, students should be able to combine AI capabilities with what they know in their discipline to create, solve problems and innovate.

We increasingly think about this as an equation: AI literacy, plus disciplinary expertise, plus the human capacities of judgment, creativity, communication and ethical reasoning. That combination is what makes an NIU graduate ready for an AI-enabled world.

AI literacy is a curriculum

This is why we do not frame our strategy as simply creating one required AI course. AI literacy is developmental and disciplinary.

A student might first encounter foundational AI literacy in general education, where they learn to evaluate information, question an AI-generated response or examine ethical implications. In major coursework, those ideas become discipline-specific.

As students move into advanced coursework and capstones, the applications should become more authentic. They should be asked to make decisions, solve problems and defend their reasoning in environments where AI may be part of the workflow.

At graduation, our goal is adaptability. We cannot predict which AI tools a student will use five years from now. We can however prepare them to evaluate new tools, learn continuously and bring sound human judgment to whatever comes next.

Students are already learning this way at NIU

One concrete example is IDSP 310: Generative AI Across Disciplines, taught by Cindy York. This example is especially useful because it is not a computer science course. It fulfills general education in creativity and critical analysis and draws students from across disciplines.

Students gain hands-on experience using generative AI for academic writing, visual content creation, research synthesis, professional communication and task automation. But they are not simply practicing prompts. They are also asking the questions we want every student to ask: How do I verify what the tool produces? What ethical considerations apply? Where do human creativity and critical thinking remain essential?

By the end, students build a portfolio of AI-enhanced work with direct academic and professional relevance. This is a good example of moving beyond talking about AI to designing purposeful learning experiences with it.

Program-level AI integration in the College of Business

Another important piece of the curriculum story is happening at the program level in the College of Business. The college has updated its online graduate business curriculum so that AI skill development is infused across multiple programs, including MS in Business Analytics + AI, MS in AI for Business, Business Analytics Certificate and MS in Digital Marketing.

What is significant is not just that AI appears in a program title. The programs embed AI across coursework in analytics, strategy, operations and decision-making, and the digital marketing program includes specialized AI coursework in artificial intelligence in digital marketing, data intelligence and AI agents.

This represents the next stage of curricular innovation: AI as a program-level learning outcome, not simply a stand-alone topic. It also helps position NIU to meet the needs of working professionals and graduate students who need flexible, applied pathways into AI-enabled business practice.

Investing in faculty-led curricular innovation

The most important point I want to emphasize is that this work is not limited to a few centrally designed initiatives. We know many faculty across the institution are already incorporating AI into their teaching and their research.

The 2026 AI curricular innovation grants were an opportunity to invest in that faculty leadership and help accelerate the work already underway. Over the summer the development of eight AI-focused faculty projects from across five colleges began.

These grants are not about imposing one model. They are about supporting faculty who know their disciplines and their students, and giving them time, tools and instructional design support to redesign real courses. In many cases, faculty research and disciplinary expertise are flowing directly into teaching innovation. That is one reason this work can scale: the innovation is being generated by the people closest to the curriculum.

2026 AI Curricular Innovation Grant recipients

• Melissa Clucas Walter, Clinical and Developmental Sciences
• Catherine Ivack, Special and Early Education
• Eric John, Accountancy and Finance
• Nicole LaDue, Earth, Atmosphere and Environment

• Iman Salehinia, Mechanical Engineering
• Jiao (Aimee) Wu, Operations Management and Information Systems
• Lei Zhang, Computer Science
• Yao Zheng, Accountancy and Finance

What is really exciting to see here is the breadth.

We have Health and Human Sciences, Education, Business, Liberal Arts and Sciences, and Engineering and Engineering Technology represented. We have general education courses, gateway courses, upper-level professional courses and required courses in the major.

And the innovations are equally varied:

• An AI-powered child development simulation
• AI literacy for future teachers,
• AI literacy for personal finance and business students
• Critical AI use in an online science course
• A custom course GPT for manufacturing
• AI cybersecurity
• Adaptive learning in computer science, and
• Career-focused AI literacy in a business gateway course.

Let me briefly spotlight 3 of these curricular innovations in more detail.

Faculty Spotlight: Changing what students can practice

This first example illustrates how AI can change what students are able to practice.

Melissa Clucas Walter is redesigning HDFS 230: Child Development, around a semester-long guided child-development simulation. Students create a fictional child and follow that child from birth through adolescence. AI generates weekly developmental snapshots, but the student work is not simply accepting those outputs. Students analyze them, correct inaccuracies, identify bias and make evidence-based caregiving and communication decisions. The AI creates the scenario; students do the higher-order thinking.

Faculty Spotlight: Developing AI literacy

This second example is Nicole LaDue’s work in EAE 120: Planet Earth, a high-enrollment general education course in Earth, Atmosphere and Environment.

This project redesigns the course to build AI literacy alongside scientific reasoning and communication. Students will not simply ask AI for explanations; they will critique AI-generated responses and visuals, identify hallucinations or oversimplifications, and compare the output against course evidence and scientific standards.

This is also a helpful example of how widespread AI is becoming in both teaching and research. Faculty disciplinary expertise and scholarship are directly shaping how students learn to question AI tools in context. In this case, AI becomes a prompt for better science learning: students practice evaluating evidence, explaining uncertainty and communicating clearly.

Faculty Spotlight: AI, cybersecurity and governance

The third faculty spotlight is Jiao Wu’s redesign of OMIS 460 and 660: Business Data Networks and Cybersecurity.

This example is important because it reminds us that AI is not only a tool students will use. In many professions, AI itself is becoming something that must be secured, audited and governed.

In this course students will examine risks that have emerged as organizations adopt AI systems, including prompt injection, adversarial data manipulation, data leakage and vulnerabilities in AI infrastructure. They will work through scenario-based activities such as red-team versus blue-team simulations, AI audits and case analyses of security incidents.

This is a strong workforce example. We are not simply preparing students to be users of AI. We are preparing them for professional roles in which they may be responsible for assessing risk, securing systems, auditing outputs and making governance decisions.

Human judgment remains central

Again these were just a few samples of the many curricular innovations that are happening across all our colleges and programs. A common theme that I think is worth emphasizing is that human judgment remains central.

AI is intentionally being positioned as an input to thinking – not a substitute for it.

Our faculty are making it clear that the purpose is not to let AI make decisions for students. AI can be a tool for generating inclusive resources while the teacher still provides heart, judgment and creativity. AI can provide a raw scenario while students perform the higher-order thinking.

As AI technology will continue to become more capable, the curricular goal is not to make the student less responsible for thinking. Rather, is to design experiences in which students become more skilled at verifying, questioning, correcting and making decisions in the presence of AI.

Scaling innovation across NIU

Individual faculty innovation becomes much more powerful when there is infrastructure around it. Our strategy is to create a reinforcing cycle.

The ethical guidelines and AI literacies framework establish shared principles. Faculty support through the Center for Innovative Teaching and Learning and the AI network gives faculty a place to learn, ask questions and connect with colleagues. The AI curricular innovation grants then give us intentional pilots in real courses. And as those projects are implemented, we can capture examples, evaluate what works and make successful approaches easier for other faculty and programs to adopt.

The AI at NIU website is becoming the public hub for this ecosystem — guidance, licensed tools, news, frequently asked questions, research and teaching resources. The student guide gives students a centralized place to develop AI literacy and human-wisdom skills.

The goal is not to centralize the curriculum. It is to make faculty-led innovation easier to start, safer to implement, and easier to share.

The student experience we are building

If we bring these pieces together, this is the student experience we are trying to build.

We want students to understand AI — its concepts, capabilities and limitations. We want them to use it effectively and transparently. We want them to question what it produces, including accuracy, bias, ethics and appropriateness. And ultimately, we want them to create: to combine AI capabilities with disciplinary expertise and human judgment to solve meaningful problems.

That progression can happen differently in every discipline, and that is appropriate. A finance student, a teacher candidate, a mechanical engineer and a child-development student will not use AI in exactly the same way.

We want our students to develop the skills to prepare them for not only for their first job, but for lifelong learning as the technology continues to change.

Sharing AI teaching strategies

To help move this work beyond individual grants and isolated examples, we are building a new AI teaching strategies repository on the AI at NIU website. This repository will showcase practical examples of faculty teaching students about AI and teaching students to use AI responsibly. It will be searchable, making it easier to find examples by title, faculty member, description or teaching theme.

The goal is to make faculty expertise more visible, support collaboration across colleges and make it easier for faculty to adapt promising strategies in their own courses. In other words, this is how we begin moving from pilots to shared campus practice.

Looking ahead

As we look ahead, we see three priorities. First, we need to learn from these faculty pilots and scale what works. Second, we need to continue building AI literacy across the curriculum — through general education, the majors and authentic career preparation. And third, we need to keep our focus on lifelong learning. The tools we are using today will not be the tools our graduates use throughout their careers.

Our goal is not to predict exactly what AI will look like five years from now. Our goal is to prepare students who can thrive regardless of what comes next.

The future of AI at NIU is not ultimately about AI. It is about our students — students who can think critically, act ethically, adapt confidently, and bring creativity and human judgment to an AI-enabled world.