every prompt has a price: how to make the invisible visible

Introduction

Results of our initial brainstorming session.

As AI innovation reaches new depths every day, many of us use these new tools to automate tasks and achieve new things. However, with this scaling in capability comes an invisible, but equally scaling expansion in data centers, and with it, increased energy and water usage and other effects.

Our project brief tackled this question head-on: How might we help people understand the hidden environmental consequences of everyday AI use by connecting familiar digital behaviors to the physical infrastructure that makes them possible?

This prompt clearly defined the ends of our project, but not the means: rather than work immediately with an assigned deliverable in mind, this project challenged us to also determine what it was we needed to design.

To begin exploring possible directions and narrow the scope of the project, we began a brief brainstorming session. The purpose of this session was to get ideas on paper and explore what was possible, even if not yet fully validated.

One idea I pitched was inspired by PaperCut's environmental printing dashboard, displayed in our institution's printing portal which informs users of impacts from their printing habits. However, when applying this concept to AI usage, practicality becomes an issue, especially thinking about incentives: a university is incentivized to lower its students' paper usage, and so it displays these efforts with the intent of lowering costs. On the other hand, an AI company may have less incentive to present statistics that could discourage users from engaging with its products.

It was with this reasoning that platform providers may not to discourage users that we ultimately decided the best strategy for our designs was to communicate directly with everyday users—inspired by ad campaigns and PSAs for similar issues. With this general strategy, our skills, and the semester timeframe in mind, we settled on three final deliverables:

1. An interactive website with rich data visualizations and animations
2. A video vignette showcasing everyday AI usage and its environmental implications
3. A poster and slide deck presented at the 2026 JHU Sustainability Symposium detailing our findings and main deliverables

Additionally, we needed to better define our audience. At this point, our user group included all AI users, which in our modern world encompasses just about everyone. We instead narrowed our focus to students, who were both accessible to us on campus and frequent users of AI tools.

Research

Research methodology.

With our audience and general direction established, we turned to research. Before deciding how to communicate the issue, we needed to understand both the environmental impacts of AI and how students currently understood them. We approached this through secondary research, a student survey, and interviews.

Literature review

Before we could begin communicating the issue to students, we needed to understand it ourselves. We divided our research into broad topics, including water usage and infrastructure. I focused on carbon emissions, examining academic papers, industry publications, and relevant articles.Our research revealed that the environmental cost of AI extends beyond electricity consumption. Data centers also require water for cooling, while the environmental impact of individual AI interactions can vary depending on factors such as the model, infrastructure, and energy source.This research primarily informed the content of our deliverables, helping us determine which environmental impacts and concepts to communicate to our audience.

Expert interviews

To supplement our literature review, we conducted interviews with subject-matter experts through JHU connections to gain additional perspectives on AI's environmental impacts and highlight gaps in our knowledge. Our three experts provided us insights in governance, environment, and transparency, respectively. These experts gave us unique insights and recommendations:

• Natural gas usage has increased and renewable energy is not expanding fast enough to keep up with demand.
• It may be helpful to appeal to emotion: simply listing facts will not get our point across as effectively.
• The true scale of data center impacts remains unknown due to lack of reporting mandates.

User survey

To better understand our audience, I took the lead in designing and conducting a survey of students' AI usage, existing awareness of AI's environmental impacts, and attitudes toward the issue. I wanted to understand not only how frequently students interacted with AI, but also how they prioritized its potential impacts and whether they felt informed about the environmental consequences.

I used a combination of multiple-choice, Likert-scale, and open-ended questions. Quantitative questions allowed us to identify broader patterns across respondents, while open-ended responses gave students an opportunity to explain their reasoning in their own words, if they wished.

Survey participants were asked several questions, like:

Survey questions.

• What do you use AI for?
• Rate from strongly agree to strongly disagree: I feel well informed of AI environmental impacts.
• Rank aspects and impacts of AI by personal importance (rapid innovation, global environmental effects, economic growth, etc.)

Survey recruitment

Because recruitment relied primarily on convenience and peer networks, I did not intend to represent the Purdue student population statistically. Instead, the survey gave me an initial view into how students in our accessible audience understood and perceived the issue. I recieved 71 responses from these sources:

• Word of mouth link sharing in classes
• Campus flyers
• Personal network
• Purdue UX community Discord server
• r/purdue subreddit

I collected basic demographic information, including academic college, to understand the composition of our sample. The distribution of respondents' colleges roughly matched Purdue's student population, although this comparison does not eliminate potential sampling bias.

Survey results.
Click image for details.

Survey results

I exported the data into Excel and began data analysis. I performed data cleaning, removing incomplete responses and moved the data to PowerBI. The survey both confirmed some of our predictions and generated new key insights:

• Students were divided on AI rankings: many placed rapid innovation first, while others prioritized environmental impacts.
• Students do not feel well informed of impacts, but strongly agree that increased regulation is necessary.
• AI usage was widespread, even among students that ranked environmental impacts highly.

These findings established a baseline for how we approached our audience. While students expressed concern about AI's environmental impacts and support for increased regulation, many did not feel well informed about the issue. This suggested an opportunity to raise awareness and spark curiosity through specific, accessible information, rather than relying on general statements about AI's environmental consequences.

User interviews

User flow sketches
from brainstorming to final.

Finally, we conducted 3 user interviews. These were students from our survey who indicated they were willing to participate in interviews for further insight. These interviews gave us more detailed knowledge of student awareness and sentiment which informed the design of our deliverables.

Sketching + brainstorming

With a robust background in what the problem is, what students know and think, and how to present the information, we advanced to sketching. We split into two groups by primary deliverable:

• Group A - Data rich interactive website
• Group B - Video vignette

I was part of Group A, responsible for building data visualizations, filling in text, and creating wireframes. Our subteam structure also allowed us to provide feedback—as Group B sketched storyboards, filmed, and edited, I provided feedback and ideas.

User flow

Low-fidelity wireframes.

In Group A, we started by sketching user flows. These synthesized our secondary research knowledge with insights from our users about how to best display and order content. I considered which aspects deserved their own section, and how to draw and keep attention to the most crucial elements.

Low-fidelity and mid-fidelity wireframes

I oversaw the design and provided feedback about how users may perceive each section and what each section required. I was not responsible for carrying the website through the entire fidelity process. However,aAnother junior team member developed much of the low-fidelity work, while our subteam lead ultimately led the implementation and much of the final visual and interaction design.

Data visualizations

KPIs.

Meanwhile, I focused on generating high-fidelity data visualizations to be integrated in the final site:

• A water scarcity map with data center locations in the Eastern US. Shows viewers that data centers are ubiquitous, even in places with high water scarcity.
  
• KPIs that display key numbers boldly and clearly.

Implementation

We had a lot of statistics, but displaying them as raw numbers made them difficult to contextualize. I explored KPI-style visuals as a way to give important figures more visual weight.

With a coherent user flow defined, layouts set from low-fidelity mockups, and data visualizations defining page content, we were ready to start website production. Our subteam lead took charge in this and quickly generated our rough website. While this initial iteration had the basic code structure and layout complete, major content was missing and color was applied in a way that added confusion, not coherence. I assisted our team lead in adding content, making a visually consistent color system, and implementing our data visualizations.

Final deliverables

After iterating, tightening text content, and implementing my data visualizations, our interactive website was finished! The vignette team finished around the same time, and we presented our deliverables to our sponsor and to the class.

Post-testing

After our deliverables were finished, we reached back out to our experts and users for feedback. While participants found the website as a whole enjoyable and insightful, there were some issues that we had overlooked:

• Because most elements were animated similarly, users had trouble identifying what items were interactable.
  
• Many additional graphs and charts lacked consistency.
• The website generally ran long, reducing skimmability and distracting from our goals.

As our project timeline had neared its end, we could not implement these changes immediately. Instead, we documented our recommendations to our sponsor in our transition document.

Conclusion

This project was the perfect introduction to the world of UX. It taught me so much about the research and design process, and that good UX doesn't have to mean making an interface pretty or designing buttons. I've come to learn it's about the user-centered process from start to finish. If I had to do the project again, I would have taken a more proactive approach to the website design beyond research and ensure every decision is justified, but I'm still really proud of what we accomplished, and in the end I really think we achieved our project goal: we have made the invisible more visible.

Finally, while this project has made me more concientious of my own AI use, I would like to clarify that I am not categorically anti-AI. I continue to experiment with AI tools, like Claude and ChatGPT, albeit with more discretion.