AI Technology

Stuffle: Artificial intelligence is fundamentally shifting nursing education. Traditional assessment methods focus on selecting the correct answers to specific questions. Today there’s more attention paid to the processes behind clinical reasoning and decision-making. As AI tools become more accessible, students must develop the judgment to interpret, question and safely apply information in patient care.

In response, my approach emphasizes AI-resilient learning experiences that require students to think critically, prioritize care and make decisions in realistic clinical contexts. The goal is to prepare practice-ready graduates who use AI responsibly and effectively.

Noonan: AI is reshaping public affairs education because it changes what “good work” looks like. If a tool can draft a memo or summarize a report, then the harder and more important question becomes: Can you define the real problem, judge the quality of the answer, understand who is affected and defend a decision in public? That has changed how I teach.

AI is making a lot more advanced, exciting ideas and research more accessible. In my AI courses, I am less interested in whether students can get a tool to produce something polished and more interested in whether they can use AI thoughtfully, critique it and connect it to real-world judgment.

Wilkerson: AI is shifting education from delivering content to designing learning systems that are more adaptive and accessible. In social work, that raises an important balance: We want to use technology to expand access but not lose the human side of practice.

Our approach has evolved from using digital tools in individual courses to thinking about how AI fits into the entire learning environment: course design, faculty support and workforce preparation. For example, we’re developing new coursework focused on artificial intelligence and digital innovation in social work practice.

We apply this training in the IU School of Social Work’s “Because You Matter” practicum, where students deliver telepractice services in rural communities facing behavioral health workforce shortages, often working alongside trusted local partners. That experience helps students understand how digital tools, professional care and community relationships must be aligned to improve access. It reflects the reality they are entering—systems where technology, human judgment and community-based care all intersect.

Eller: As a scientist, one of AI’s most immediate impacts has been on the research process itself. Keeping up with the literature has always been a challenge, given the sheer volume of published work across any discipline. Literature review once required hours of sifting through abstracts on PubMed or Google Scholar, often just to identify what not to read.

AI is changing that. It can parse large bodies of literature quickly, surface the most relevant work, summarize findings and deliver a brief that lands on your desk before the workday begins. The result is that researchers are spending more time thinking and less time searching, making AI the next generation of the literature search. But that efficiency comes with a responsibility we now have to teach explicitly. AI summaries can be convincing but wrong, sometimes missing nuance, misrepresenting findings or confidently citing sources that are not credible.

My research workflow has expanded, but my teaching has had to evolve alongside it. Students in my AI literacy course learn not just how to use these tools, but how to interrogate the output: Is the source real? Is it relevant? Does the summary reflect what the paper says? The goal is to show students that AI is a force multiplier, something that lets them work more effectively, not a replacement for their own critical thinking.

Hodgson: AI is pushing educators to rethink some of the assumptions that have defined teaching and learning for decades. Higher education has traditionally focused heavily on evaluating final products and deliverables, but AI is shifting the conversation toward process: how students think, solve problems, reflect and make decisions.

As a result, much of my work in faculty development now centers on helping faculty redesign learning experiences and assessments around critical thinking, authentic application, metacognition and reflection rather than simply content production. Meaning, we’re spending more time asking questions about process, about critical and creative disciplinary workflows, about evaluating AI-generated information and creating learning experiences that invite students to explain their reasoning and decision-making process in relation to the output.

Stuffle: I incorporate AI by creating and using custom agents to enhance curriculum development, design online courses and create learning experiences that align with program learning outcomes while ensuring consistency and accessibility throughout all programs.

What makes my approach distinctive is that AI is not the focus; it’s a tool to enhance experiential learning, ensuring students actively engage in clinical reasoning, decision-making and real-world application. I also prioritize embedding ethical AI use into these experiences, helping students understand how to use AI and when to question it.

My intent is to use AI to elevate the quality and consistency of learning while keeping the emphasis on practice-ready skills.

Noonan: One of the most meaningful shifts in my teaching is treating project management as a core skill. As AI tools become more capable—especially tools that can carry out multi-step tasks —students will need to know how to define the goal, break work into stages, assign tasks wisely, check quality and decide when human judgment is required. That changes what we should do in the classroom.

AI can reduce some of the drudgery, but that should make learning more rewarding and not just make the work easier. It should free students and faculty to spend more time on higher-value work: judgment, critical thinking, feedback, mentoring and connecting ideas to real people and real situations.

What makes my approach distinctive is that I see AI not just as a tool inside a course, but as a reason to rethink what a course is for.

Wilkerson: One of the most meaningful ways we’re integrating AI is in course development and program design. We’re using AI-supported tools not just to generate content but to strengthen the design process—aligning learning outcomes, structuring courses, and creating more consistent and accessible learning experiences across programs.

What makes our approach distinctive is that AI is embedded within a human-centered, collaborative process. Through our Office of e-Social Work Education and Practice, faculty work with instructional designers to use AI in ways that are pedagogically sound and aligned with professional standards. We’re also using AI to expand what’s possible in online learning, including simulations and applied practice activities that give students more opportunities to engage and practice skills.

At the same time, the goal isn’t to replace teaching. AI helps reduce design workload, so faculty can focus more on student interaction, feedback and mentoring.

Eller: What has changed most meaningfully for me is the ability to build. In bioinformatics and in teaching, I have long had a clear sense of what I wanted a tool to do, but the barrier to actually building it—more realistically, the time—was often prohibitive. That gap is closing fast.

I have a background in coding, and the rate at which I can now design and deploy custom tools has increased dramatically. What once required weeks of work can now take an afternoon, and that level of customization is, if I am being honest, exhilarating and slightly addictive.

In my research, it means I can build pipelines that fit my exact questions rather than adapting my questions to fit existing software. In teaching, it means that within the real constraints of a university setting I can create resources and experiences that reflect the actual problems my students will face while working. The only limit now is imagination.

That said, having a coding foundation helps immensely. AI-assisted development is most effective and most reliable in the hands of someone who understands what is happening under the hood, and that expertise is what keeps the tools functional and trustworthy.

Hodgson: Using it as a collaborative thought partner rather than simply a content generator has been a meaningful turn in integrating AI into my work. In leading faculty development, a large part of the intellectual labor involves refining ideas, exploring possibilities, and thinking through how teaching and learning are changing. So, I regularly use conversational AI tools to challenge my assumptions, ask follow-up questions, surface patterns, and help me think more critically and creatively about complex problems.

But I’ve also leaned heavily into creating custom AI agents that automate repeatable workflows. For example, in our Generative AI Faculty Fellows program at IU, I’ve developed an agent that takes my ideas for faculty learning challenges and transforms them into structured, consistent deliverables based on frameworks and materials I’ve designed. The AI handles repetitive formatting and production work, which allows me to spend more time focusing on ideation, strategy, pedagogy and innovation. I’m less interested in AI replacing human thinking and more interested in how it can expand human capacity.

Stuffle: In simulation-based education, consistency and realism are critical to student readiness, and AI has helped us strengthen both. We have used AI to create standardized pre-briefing videos, so all students enter simulated patient encounters with the same foundation.

We have also used AI to build simulation scenarios and developed agents to review scenarios against best-practice standards, improving quality and alignment across experiences. I’ve also developed AI-driven personas to allow students to practice communication skills in a safe, repeatable environment, building confidence before entering real practice settings.

Noonan: One success I have seen is that AI helps students move past the tired debate that “AI is amazing” or “AI is dangerous.” Instead, they can make grounded judgments.

In my AI courses, students can use AI to explore examples, test arguments, compare perspectives and push into questions that would have been hard to support in a normal class because of time or resource limits. The win is not that students finish faster; the win is that they can go further, ask better questions and become more critical thinkers about the work in front of them.

Wilkerson: A good teaching example comes from a policy course where we replaced a traditional midterm with an AI-supported advocacy simulation. Students researched an elected official, created a profile and used AI to simulate a realistic constituent meeting.

This helped address a common gap in macro-level social work education, where students have fewer opportunities to practice real-time skills like persuasion and policy advocacy. Instead of only analyzing policy, they engaged in a back-and-forth exchange and adapted to questions in real time.

Students responded strongly because it made policy practice feel concrete and applied. It’s a good example of how AI can turn traditionally abstract content into interactive, practice-based learning.

Eller: My most vivid early example goes back to around 2022. A student in my statistics lab submitted a report where English was not her first language, and she was struggling to articulate what certain terms meant. As a result, I was struggling to grade it.

I used AI to help bridge that gap, translating what she was trying to express into language we could both work from. It was a small moment, but I remember it clearly. Here was a tool that could get two people on the same page almost instantly, explaining difficult concepts in whatever framing both parties could actually understand.

I used to tell students who were lost to read the textbook or search online. Now I also tell them they can take my lecture slides and ask AI to explain the material in a way that clicks for them. Sometimes hearing something framed differently is the difference between understanding and not. For students navigating a new language, a new discipline or both, that kind of accessibility is significant.

Hodgson: One of the most exciting ways AI is driving major leaps in learning is through simulations and sustained conversational experiences that make learning more interactive and authentic. Across IU, faculty are designing assignments where students engage with AI as a role-playing or conversational partner in ways that were previously difficult to scale. For example, teacher education students can practice parent-teacher conferences with AI-generated personas, while language learners can build culturally grounded conversational agents that allow them to practice dialogue in a second language within realistic social and cultural contexts.

What makes these experiences powerful is that AI is not simply generating answers for students; it is creating opportunities for practice, reflection and deeper engagement. We’re seeing AI help move learning from passive content consumption and production toward more active and authentic engagement.

Stuffle: AI emphasizes the need for human skills that are at the core of nursing—skills like empathy, therapeutic communication and patient-centered care. At the same time, clinical judgment is more critical than ever, as graduates must evaluate AI-informed insights, such as clinical decision support tools, within the context of each patient’s unique situation.

Faculty play a key role in modeling this by using AI transparently and guiding students in its ethical and meaningful integration into practice. To prepare for this shift, we are working closely with clinical partners to understand AI use in their settings and designing authentic learning experiences that reflect how AI is shaping real-world care environments.

Noonan: AI is making human skills more important, not less. Most students do not need to become AI engineers. They might need to become better managers of large staffs, of agentic AIs. They need to become better problem framers, better judges of evidence, better communicators and better stewards of public trust.

Faculty roles are changing, too. We are less valuable as repeat deliverers of basic content and more valuable as coaches, challengers, designers of learning experiences and assessors of judgment. Students need to know how to use AI but also when not to use it.

Wilkerson: In social work education, AI is changing both what students need to know and how professionals work. Students don’t just need to use AI; they need to understand how it shapes access, equity and real-world outcomes. That means stronger skills in critical thinking and responsible use of technology.

One way we’re preparing students is through digital citizenship curriculum that focuses on ethics, access and real-world use of digital tools. This helps them understand not just how to use AI, but how it affects the communities they serve.

Faculty roles are shifting as well. There’s less emphasis on building everything from scratch and more on guiding learning and helping students apply knowledge in practice. We support this shift through our Office of e-Social Work Education and Practice, where instructional designers and AI-assisted tools are integrated into course development.

Eller: One of the skills I think about most when preparing students is resilience and, more specifically, the willingness to exercise the mind even when an easier path exists. For most of human history, learning required struggle: working through practice problems, sitting with confusion and pushing through difficulty. That mental effort was not incidental to learning; it was the mechanism.

Now that answers are a click away, the temptation to shortcut that process is real, and the consequences for long-term learning are worth considering. I use mathematics as an example in my course. Will every student need the Pythagorean theorem in their career? Maybe, maybe not. But we do math in part because the act of working through it stretches the brain. Cognitive effort builds capacity, and that capacity transfers to other subjects; that is the reason math is a foundational subject.

That is why AI literacy, to me, is as much about knowing when not to use AI as it is about knowing how to use it well. My courses address both. Students who understand that distinction will be far better prepared than those who simply know which tools to reach for.

Hodgson: What AI is revealing, at least in part, is one, the increasing importance of “durable human skills”—communication, creativity, collaboration and critical thinking—and our commitments to centering those in education; and two, that our future workforce will require not only foundational AI literacy but the ability to adapt to rapid technological change, critically evaluate AI-generated information and work effectively in partnership with AI systems.

Further, despite public narratives to the contrary, I think one of the biggest shifts we’re seeing or will see is that the value of expertise is actually increasing, not decreasing. Disciplinary experts are able to use AI tools in far more sophisticated and impactful ways than novices because they can better direct, evaluate, refine and contextualize the outputs. That means students need more than technical proficiency; they need domain expertise to complement things like discernment, adaptability and what I would describe as an increasing digital resilience—the capacity to continuously learn and evolve alongside emerging technologies and technological transformation.

Stuffle: One of the most significant risks is overreliance on AI, which can undermine the development of foundational knowledge and critical thinking if not addressed intentionally.

Rather than avoiding AI, we are integrating it into the learning process to help students understand when its use is appropriate, when it is not, and how to critically evaluate outputs for accuracy and relevance. At the same time, we are supporting faculty with the resources and space to explore AI in ways that align with their teaching, while promoting ethical use and preserving academic freedom.

Transparency in how students and faculty use AI is central to this approach, creating a shared environment for learning and continuous improvement.

Noonan: The obvious risk is that students use AI to avoid the work that actually builds understanding. A polished answer can hide very shallow learning. But there is an institutional risk, too: responding with one-size-fits-all rules or simply bolting AI onto old course structures. That would be a missed opportunity.

Faculty in my school are trying different approaches: changing assignments, using more live discussion, requiring transparency and experimenting with new assessments. That variety is healthy.

We should be experimenting with everything related to learning. Otherwise, we risk losing out to the chatbot on a student’s laptop.

Wilkerson: One of the biggest risks is inconsistency. AI can produce very different results depending on how it’s used, which can affect course quality and the student experience. There’s also the risk of over-reliance, using AI without enough attention to accuracy, ethics or context. We address this by focusing on structure and oversight.

Through our Office of e-Social Work Education and Practice, we’ve built AI into structured, team-based course development workflows, where faculty and instructional designers work together and expectations are clear. We’re also emphasizing ethical use, including issues like bias, access and professional responsibility, so AI is used in ways that align with social work values.

For example, when using AI-supported simulations, we explicitly address the limits of representing lived experience and help students critically evaluate what the technology can and cannot authentically reproduce. The goal isn’t to limit innovation; it’s to make sure it’s intentional and consistent and supports quality learning.

Eller: Let me flip this question a little. One of the more uncomfortable things AI is exposing is how much of traditional education has relied on what we sometimes call “busywork”: book reports, one-page reflections, assignments that are simple to complete and simple to grade. It turns out AI is very good at busywork, and that is a problem, but it may also be seen as a catalyst for change since it causes us as educators to reflect on our own assignments.

The risk is not just that students will use AI to shortcut these tasks, but that we have perhaps leaned too heavily on them in the first place. AI is forcing a long overdue conversation about how we assess learning.

The good news is that education research has pointed toward better alternatives for years. High-impact practices, including project-based learning, capstone experiences, community-based learning and e-portfolios, give students far more meaningful opportunities to demonstrate what they actually know. They also happen to be much harder to outsource to AI. A language model cannot go out into the community or verbally defend your capstone project for you. The challenge ahead is redesigning our courses and assignments to reflect that.

It is difficult and time-consuming work, but AI may be exactly the pressure that finally makes it happen. It doesn’t hurt that the Institute for Engaged Learning at IU Indianapolis is one of the leading institutions nationwide and is helping enact these changes on campus.

Hodgson: One of the biggest risks with AI is the temptation to prioritize speed and convenience over meaningful learning. These tools can make it very easy for students to bypass the difficult, sometimes uncomfortable process of learning, especially in educational systems that are heavily driven by grades, efficiency and performance metrics. But real learning is often messy and often inefficient. It requires reflection, revision, struggle and growth, and those are exactly the kinds of experiences students may avoid if AI becomes a shortcut rather than a support for thinking.

Preparing the next generation means helping students build not only AI literacy, but also resilience, adaptability and the willingness to engage deeply with challenging ideas. The goal should not be to remove productive struggle from learning, but to help students use AI in ways that enhance human thinking and engagement rather than replace it.

Stuffle: Over the next three to five years, responsible and effective AI integration will require institutions to move beyond isolated tools toward coordinated, meaningful use across teaching, research and service. This includes building governance structures and infrastructure that support ongoing development, ethical use, and faculty and student support.

Collaboration, both across institutions and with clinical and industry partners, will be essential to stay aligned with how AI is evolving in practice environments. While interest in specific tools may fluctuate, the priority must remain on intentional use, ethical standards, and maintaining human judgment and intellectual oversight at the center of education.

Noonan: Responsible AI integration will require colleges and universities to experiment more boldly. This is not just an academic-integrity issue, and we should not let the IT tail wag the education dog. The real opportunity is to rethink old habits: fixed course structures, one-size-fits-all assignments, and assumptions about where learning happens.

AI should make education more flexible, more empowered and richer. But the winners will be institutions that redesign education around human judgment, context and connection—not institutions that simply license the newest chatbot.

Wilkerson: Over the next few years, the biggest shift will be from experimentation to system-level integration. Right now, many institutions are testing AI in isolated ways. The next step is building it into how programs are designed, delivered and evaluated.

Responsible and effective use will depend on having clear roles, consistent practices, and ongoing evaluation of impact on students and faculty. It also means aligning AI with professional standards, accreditation expectations and the broader mission of education.

Indiana University and its School of Social Work are focusing now on building that infrastructure by supporting faculty, strengthening instructional design and creating systems that allow innovation without sacrificing quality. Ultimately, AI will be most valuable when it expands access and improves learning without losing human relationships at the core of education.

Eller: Predicting where AI will be in three to five years feels like a fool’s errand. I could not have imagined even six months ago doing half of what I’m doing  today with these tools, and the pace shows no signs of slowing. So rather than forecasting specific capabilities, I will say this: The most important thing institutions and individuals can do right now is to remain flexible and open to change.

As a biologist, evolution comes to mind. Traits that go unused tend to disappear, and the same principle applies here. Educators and institutions that resist adapting risk being left behind, not because AI will replace them, but because those who ignore it risk becoming less effective by comparison.

We have a duty to prepare students for the world they are entering, and that means we must be willing to evolve alongside it.

The specifics of what AI will look like in 2030 are uncertain. What is not uncertain is that change is coming fast, and the institutions that will serve students best are the ones building the flexibility to meet it.

Hodgson: Over the next few years, responsible and effective AI integration will require institutions to think about AI as something embedded across the educational experience. Similar to how we once approached writing across the curriculum in the 1980s and ’90s, AI literacy, critical evaluation and human-AI collaboration should appear at meaningful points throughout a student’s educational journey, shaped by the needs and expectations of different disciplines and professions. The goal is not simply widespread adoption, but intentional integration that helps students understand how AI changes thinking, learning and practice within specific fields.

At the same time, responsible integration also means identifying where AI should not be centered. Institutions should be investing not only in tools and infrastructure, but also in the preservation of deeply human forms of learning: discussion, reflection, deliberation, creativity and relationship-building. Some of the most important educational experiences in the future may involve slowing down, engaging in sustained attention and creating space for human-to-human interaction without technological mediation.