Elevating Digital Strategy in Higher Education through the Lens of UX

Throughout my career as a user experience designer, I have witnessed firsthand the transformative power of a user-centered approach to digital strategy across diverse industries, including healthcare, finance, for-profit, non-profit, boutique agencies, and large corporations alike. Bringing that experience to higher education presents its own unique challenges. Academic environments are dynamic ecosystems, each with distinctive cultures, learners, and goals. Yet, the common denominator for successful digital transformation remains the same: an unwavering commitment to UX principles, championing clarity, accessibility, and empathy at every touchpoint. This is universal.

Harnessing Deep UX Expertise for Institutional Impact

A robust background in UX enables us to see beyond pixels and interfaces, recognizing that every digital interaction is an opportunity to build meaningful connections. This perspective is especially critical in academia, where digital platforms bridge gaps between students, faculty, staff, and the broader community. My experience leading branding initiatives, website launches, and cross-functional design teams has only reinforced the importance of embedding UX thinking early and consistently into strategic planning.

My experience [...] has only reinforced the importance of embedding UX thinking early and consistently into strategic planning.

Identifying and Closing Gaps

Effective UX professionals are skilled not only at identifying user needs but also at spotting technical and organizational gaps that may hinder progress. For institutions, this means proactively assessing where legacy systems, siloed processes, or misaligned digital tools create friction for end users. Facilitating conversations among design, development, and administrative stakeholders fosters cross-functional understanding, resulting in solutions that maximize creative potential and operational efficiency. Sometimes, the answer is as straightforward as holding a workshop to establish a common naming convention or an agreed-upon process for outputting deliverables.

In every role, evolution has been a constant, whether driven by technology or market demands, or simply as colleagues come to understand the full impact of UX. UX requires agility and the ability to adapt. This adaptability welcomes feedback, values experimentation, and crucially, centers diverse voices. My work has underscored the importance of equitable access, ensuring that accessibility and universal design are foundational, not remedial, considerations. Fostering media literacy, cultural relevancy, and systems thinking within design teams and campus projects pays dividends in improved engagement, satisfaction, and institutional reputation.

Strategic Leadership and Upskilling

I have never considered myself a natural public speaker, but if you give me a chance to share about something in my area of expertise (and hyper-focused special interests), I’ll forget about any stage fright. UX designers are, by nature, lifelong learners, and when I lead upskilling workshops, the return on investment is clear. I have seen firsthand how investing in people yields high returns for digital strategy, as evidenced by the design departments I have helped grow. Focusing on talent development ensures that teams are not only proficient with current tools but also resilient, adaptive, and empowered to contribute visionary ideas. Encouraging regular communication with stakeholders helps designers become effective translators, bridging institutional goals and technical realities with compelling visual and functional solutions.

A recurring lesson throughout my career has been the value of involving UX experts at the very beginning of new initiatives, especially when mapping out the user journey. Whether redesigning digital platforms, establishing design systems, or integrating new technologies, early UX engagement aligns project trajectories with user needs and organizational capacity. This foresight mitigates risk, fosters buy-in, and unlocks opportunities for innovative problem-solving.

A recurring lesson throughout my career has been the value of involving UX experts at the very beginning of new initiatives, especially when mapping out the user journey.

If you find yourself wondering, “When is the right time to bring in a UX expert?” The honest answer is: yesterday. But now works just as well. Prioritizing UX early isn’t just a best practice; it’s the foundation for building digital experiences that truly work for everyone. Every moment you wait is a missed opportunity for smarter, more impactful solutions, so don’t hesitate to make UX expertise part of your strategy today.

Design for Generative AI: Beyond Conversational Interaction

In the last post I covered some conventional UX practices and methodologies that apply to generative AI interfaces but are not exclusive to generative AI. What are some methodologies or principles that are more specific to generative AI that differ from the UX design for other, more traditional applications? First, we define generative AI interfaces are those that use artificial intelligence to create or modify content, such as text, images, audio, or video. They are different from other applications in that they require a high level of user trust, control, and feedback. Some of the UX design principles, approaches, or methodology that are specific or important for generative AI interfaces are:

Transparency: The user should be able to understand how the generative AI works, what are its capabilities and limitations, and how it uses the user's data. This can be achieved by using clear labels, icons, tooltips, explanations, and examples to communicate the AI's functionality and purpose ¹ ².

Control: The user should be able to influence the generative AI's output, such as by providing input, setting parameters, choosing options, or editing the result. This can be achieved by using sliders, buttons, menus, checkboxes, or text fields to allow the user to adjust the AI's behavior and outcome ¹ ².

Feedback: The user should be able to see the generative AI's output, evaluate its quality and relevance, and provide feedback to the AI. This can be achieved by using progress bars, previews, ratings, comments, or suggestions to show the user the AI's progress and result, and to collect the user's opinion and preference ¹ ².

Iteration: The user should be able to refine the generative AI's output, such as by repeating, modifying, or combining the results. This can be achieved by using undo, redo, save, delete, or merge functions to enable the user to experiment and improve the AI's output ¹ ².

Some examples of generative AI interfaces that use these principles are:

Framer: A design tool that uses generative AI to create realistic mockups and prototypes based on the user's sketches. It uses a magic wand icon to indicate the AI feature, a slider to control the level of detail, a preview to show the AI's output, and a save function to store the result ¹.

Photoshop: A photo editing tool that uses generative AI to enhance or transform images. It uses labels and tooltips to explain the AI features, such as neural filters, content-aware fill, or sky replacement. It also uses buttons and menus to allow the user to select and adjust the AI options, a progress bar to show the AI's progress, and an edit function to modify the result ¹.

Notion: A note-taking and collaboration tool that uses generative AI to generate text based on the user's input. It uses a sparkles icon to indicate the AI feature, a text field to provide the input, a preview to show the AI's output, and a rating function to collect the user's feedback ¹.

Diagram: A diagramming tool that uses generative AI to create diagrams based on the user's text. It uses a label and an example to explain the AI feature, a text field to provide the input, a preview to show the AI's output, and a merge function to combine the results ¹.

(1) Emerging UI/UX Patterns in Generative AI: A Visual Guide. https://www.whitespectre.com/ideas/emerging-ui-ux-patterns-in-generative-ai/.

(2) Design Principles for Generative AI Applications - arXiv.org. https://arxiv.org/abs/2401.14484.

(3) Redefining UX Design for Generative AI Models in Enterprise. https://law.stanford.edu/2023/11/16/redefining-ux-design-for-generative-ai-models-in-enterprise/.