UX Visionary Series | Key Lime Interactive
AI is changing both the tools UX professionals use and the experiences they are asked to design. For educators, that raises a practical question: how do you prepare students for a field whose methods and expectations are still evolving?
Nancy Rasche brings that question into the classroom and into real client work. An Associate Professor of Practice in User Experience Design at Purdue University, she helped develop the UX major’s Experience Studio courses, where students work directly with industry partners on unique client-determined UX challenges and take on greater responsibility as they progress through multiple semesters.
In this edition of the UX Visionary Series, Nancy shares the foundations she believes emerging practitioners need, what she is seeing from today’s students, and how experiential learning can help them develop sound judgment in an AI-shaped profession.
What skills, mindsets, or fundamentals do you believe emerging UX professionals and designers need to develop today, and how is that different from what was expected five or ten years ago?
Today, we need to prepare students to become capable UX practitioners within an ecosystem increasingly influenced by AI, whether it is in their design process or in their design of products that utilize AI.
Students still need the traditional foundations of UX. They need to understand people, conduct research, synthesize findings, design interactions, evaluate usability, communicate effectively, build empathy for users, and, most importantly, validate their design decisions. These fundamental skills have not changed. What has changed is the context in which those skills are applied.
First, students need a much stronger understanding of responsible AI use. They need the ability to evaluate expectations, communicate transparently about their use of AI, and understand their professional responsibilities. Digital literacy now includes AI literacy, which is still open to interpretation and is still evolving. The AI landscape is changing so fast that we will have to continually iterate how we address it. The best way to understand AI expectations is to maintain a close connection with industry and UX practitioners.
Second, we need to build students’ design judgment and help them become critical thinkers. As AI makes it easier to generate concepts, content, and prototypes, the critical human skill is no longer generating possibilities, but exercising the judgment to identify which possibilities create meaningful value. Students need enough competency in UX methods and principles to vet AI contributions, identify weak assumptions, recognize bias, assess reliability, and determine whether proposed solutions genuinely support user needs. The ability to critically evaluate AI-generated outputs may become more important than the ability to generate designs themselves.
Third, students need preparation for an emerging specialization in designing AI-enabled experiences. Future UX professionals will increasingly participate in creating products that include conversational interfaces, recommendation systems, adaptive experiences, autonomous features, and other forms of intelligent interaction. This requires new knowledge involving trust, explainability, transparency, ethics, human oversight, and user agency, to name a few. These topics were not typically central components of a UX curriculum ten years ago, but they have become increasingly important as consumers interact with AI across a growing range of products, services, and digital experiences.
At Purdue, one way we are addressing these challenges is by combining foundational UX education with applied, real-world practice. The Experience Studio serves as a bridge between academic preparation and professional expectations. Students work on industry-sponsored projects over multiple semesters, receiving mentorship from industry partners who help validate the relevance of their skills and methods. As students progress from contributors to leaders, they gain experience making decisions, defending recommendations, collaborating with stakeholders, and adapting to emerging technologies on real-world projects.
The goal is not simply to prepare students to use or design for today’s AI tools. The goal is to prepare them for a career in which technologies, platforms, and workflows will continue to evolve. The students who will be most successful are those who can combine strong human-centered design foundations with sound judgment, adaptability, ethical awareness, and a commitment to lifelong learning.
What are you seeing from today’s UX and design students that gives you insight into how the next generation is preparing for an industry being reshaped by AI and emerging technologies?
One of the advantages of working with students every day is that I get to observe how quickly their relationship with technology evolves. What I’m seeing is not just students learning new AI tools. I’m seeing students navigate a rapidly changing educational and professional environment that is still trying to establish the norms around AI use.
Three shifts stand out in UX education today:
- Navigating constantly evolving AI policies
- Responsibly incorporating AI into the design process
- Demonstrating value in the design of human-centered, AI-enabled experiences
Navigating evolving AI policies. Many of today’s students’ first experiences with AI were “don’t use AI.” Within just a few years, that evolved into a wide range of course-specific, institution-specific, and industry-specific policies. In academia, students now move between classes where AI may be prohibited, encouraged, required, or allowed only under certain conditions.
For many students, this creates uncertainty. These are digital natives who view AI as part of their everyday environment, yet they often worry about the consequences of using an AI tool incorrectly or, worse, being wrongfully accused of it. As educators, we need to help students understand not only what is permitted, but why responsible and transparent use matters.
Incorporating AI into the UX design process. Much of the conversation around AI focuses on efficiency and productivity, but our responsibility is to ensure students understand where AI supports human-centered design principles and methods.
Through our research on student–AI collaboration, we found that students actively evaluate AI outputs and make judgments about reliability, relevance, and creative ownership rather than simply accepting generated content [1, 2]. Our findings suggest that AI introduces new forms of design judgment, including decisions about when an AI contribution is trustworthy and beneficial to the design process.
Informed human review of AI outputs seems to be the most important step I am seeing. Blindly using AI-generated content is not the same as understanding it, validating it, and determining whether it truly aligns with the needs of users, stakeholders, and the goals of the design challenge.
Designing AI-enabled experiences. I believe this area will significantly influence UX education over the next decade. As the profession expands beyond traditional interfaces, designers must be able to advocate effectively for users in dynamic, AI-driven systems. Current UX methods may need to grow or adapt to encompass these experiences. How we teach design processes for such systems is an essential curriculum question, and educators need to explore ways to integrate these topics across their programs.
We are actively tackling these shifts by establishing new competencies around AI integration and partnering closely with industry leaders to validate that our education aligns with professional practice. For instance, I am designing a targeted course, Experience Design Foundations for AI Tools and Systems, focused on deepening student proficiency within this rapidly changing field. Moving forward, we will assess student outcomes and refine our instruction so students can stay adaptable as AI and the broader user experience landscape evolves.
We also expose students to real-world scenarios in our Experience Studio [3] courses as they work on client-determined projects over multiple semesters. I’ve noticed that students are eager to work on AI-driven projects to enrich their portfolios with AI design experience. Working directly with industry sponsors exposes them to current practices, emerging technologies, and diverse organizational perspectives on AI.
Rather than discussing AI design in the abstract, students are applying UX methods to real client problems and learning to make informed decisions in an environment that closely resembles professional practice.
Closing perspective
Nancy’s observations point to a durable strength in UX: the ability to ask whether an experience serves people, even as the technology behind it changes. AI can expand what teams create and how quickly they create it. Research, judgment, and validation help determine whether those possibilities are useful, trustworthy, and appropriate.
Preparing the next generation means giving students room to practice those decisions with real people, real constraints, and real consequences. That connection between human-centered foundations and applied experience will be increasingly important as AI reshapes the role of UX designers and redefines the experiences they create.
UX Visionary is a Key Lime Interactive series featuring perspectives from people shaping the future of research, design, technology, and human experience.
References
- Shukla, P., Naik, S., Obi, I., Backus, J., Rasche, N., & Parsons, P. (2025, July). “Rethinking citation of AI sources in student-AI collaboration within HCI design education.” In Proceedings of the 7th Annual Symposium on HCI Education (pp. 1–8).
- Naik, S., Shukla, P., Obi, I., Backus, J., Rasche, N., & Parsons, P. C. (2025, June). “Tracing the Invisible: Understanding Students’ Judgment in AI-Supported Design Work.” In Proceedings of the 2025 Conference on Creativity and Cognition (pp. 438–442).
- Purdue Experience Studio: https://engineering.purdue.edu/ExStudio/