Designing for AI in Education: The Student Profile App
Creating a one-stop-shop for student achievement insights, utilizing AI.
My Role
Lead designer
Interaction designer
Visual designer
Prototype
Project manager
Platforms
Web
iOS
Android
Product Type
Web App
Client /
NWEA (now HMH)
Overview
At NWEA, I led the design of the Student Profile App, giving teachers a clear, holistic view of each student’s progress and growth. The goal was to make data interpretation effortless and empower educators to make faster, more informed decisions.​
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✨ Today, as AI reshapes how learning tools are built and personalized, I’ve revisited that project through a modern, AI-integrated UX lens, exploring how each stage of design can now leverage generative intelligence, multimodal data, and automation.
Problems
Our research showed that teachers use data to assess student achievement and areas of need, which resulted in the following problems:
Conflicting information across multiple MAP Growth reports
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Steep learning curve for MAP Growth report interpretation
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Time-consuming process to extract meaningful insights
Traditional UX Research Methods:
Field interviews, contextual inquiries, and affinity mapping—surfaced a core issue: educators were spending more time collecting data than acting on it.
✨ With AI Today:
AI could dramatically accelerate this phase.
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Natural language processing could analyze thousands of teacher comments, surfacing patterns in sentiment and recurring pain points in minutes.
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A conversational AI assistant could summarize interview transcripts, detect emotional tone, and suggest emerging themes across user groups.
Example tools:
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ChatGPT for thematic clustering, Notion AI for qualitative synthesis, Fireflies.ai for transcription.
Multimodal layer:
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Voice and tone analysis from interview audio could uncover stress or confusion patterns not evident in text, deepening empathy-driven insights.
Defining the User & Needs
I developed composite teacher personas based on recurring behavioral and emotional patterns. Teachers are data-driven, time-constrained, and relationally intuitive educators. These personas shaped the app’s hierarchy and focus on actionable, digestible data.
✨ With AI Today:
AI could refine personas continuously using real usage analytics or aggregated engagement metrics.
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Simulated user behavior modeling could predict how each persona type might navigate or interpret data, grounding empathy in evidence.
Example tools:
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ChatGPT for persona generation and scenario simulation, Midjourney for persona visualization.
Multimodal layer:
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Combining quantitative dashboards with natural language persona narratives creates a hybrid empathy model—part data, part story.
Opportunity
How might we better support educators who are assessing student data using our MAP Growth platform?
Goals
The new MAP Growth report must achieve these goals to be considered successful:
Launch
Develop a shippable MVP experience for the Student Profile Report that could be built, tested, and shipped in 6 months.
Build trust
This new report product must become a trusted place for educators to go for their data insights within 12 months.
Grow
We must reach product-market fit with our key customers before releasing it everywhere.
Ideation and Concept Development
Collaborative whiteboarding produced early concepts like customizable dashboards and growth visualizations. We explored how to present large-scale data simply, focusing on cognitive load and teacher trust.
✨ With AI Today:
Generative tools would amplify ideation range and speed. AI could propose new “how might we” questions or automatically visualize multiple dashboard layouts based on design intent.
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Example tools:
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ChatGPT for persona generation and scenario simulation, Midjourney for persona visualization.
Multimodal layer:
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Speech-to-sketch tools could translate brainstorming sessions into wireframes in real time, enhancing co-creation.
Prototyping & Interaction Design
I created mid- and high-fidelity prototypes in Figma and conducted usability testing with teachers to refine clarity, data density, and emotional tone. The focus was on reducing friction and helping teachers interpret growth trajectories effortlessly.
✨ With AI Today:
Figma’s AI features and external assistants could now generate multiple layout variations, automate accessibility checks, and analyze user flow complexity. Predictive simulation could estimate where teachers might experience hesitation or overload before testing even begins.
​Example tools:
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Figma AI for rapid interface generation, Uizard for text-to-wireframe iteration.
Multimodal layer:
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Integrating natural language input (“show me how my class did this week”) transforms dashboards into conversational tools—bridging UX and voice interaction design.
Evaluation & Iteration
Usability sessions revealed subtle barriers to comprehension. Namely, how color, phrasing, or chart complexity affected trust and confidence. Iterations focused on simplification and emotional reassurance.
✨ With AI Today:
AI-enabled analysis could now quantify qualitative data. Sentiment models could assess emotional tone across test recordings, and pattern recognition could detect moments of confusion via eye-tracking or cursor hesitation.
​Example tools:
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Maze for automated usability insights, ChatGPT for heuristic summarization.
Multimodal layer:
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Combining visual and verbal feedback data builds a more dimensional picture of user experience, bridging what users say with how they feel and act.
The MVP
Our research defined the essential feature set needed to launch the MVP student insights report. This single report allowed educators to access and interpret student data efficiently, facilitating data-driven collaborations, goal-setting, and seamless navigation between student profiles
Final wireframe showing product vision
MVP, with just the essential features and user benefits.
2nd release, with the next set of essential features and user benefits.
3rd release, with more robust, nice-to-haves.
Overcoming Adoption Barriers
The barrier to building trust was low adoption among educators, who perceived the transition as time-consuming and disruptive to their established workflows. Many had relied upon legacy reports and tried and true workarounds to extract the insights they needed, making them hesitant to embrace a new system.
To address this challenge, we partnered with influential voices in the education space, leveraging social media influencers to promote the value of the new report. Their outreach highlighted the efficiency gains, demonstrating how educators could save time and streamline decision-making by adopting the new system.
Massive Scale
With user trust now solidified, the Student Profile Report soon achieved unprecedented adoption, surpassing all legacy reports in usage within its first academic quarter. Its intuitive design, actionable insights, and user-centered approach quickly established it as the most popular app on the NWEA platform. Within just one school year, the Student Profile Report became the brand’s defining product, shaping marketing strategies and reinforcing NWEA’s leadership in educational technology.
✨ With AI Today:
AI-driven analytics could make this process continuous.
Usage data, behavioral clustering, and predictive modeling could highlight which design elements correlate with improved teacher efficiency or student performance.
AI-generated executive summaries could deliver real-time reporting to stakeholders.
​Example tools:
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Tableau with AI insights, ChatGPT for automated report summarization.
Multimodal layer:
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Integrating voice feedback, written reflections, and interface telemetry could create a truly living feedback loop, closing the gap between design intent and classroom impact.
Expanding Insights and Functionality
The Student Profile's success transformed the company's brand identity, becoming the face of its marketing efforts and sales strategies for years. This strategic shift reinforced the product’s value, driving customer engagement, adoption, and long-term business success.
Opportunity
Continuing with the product vision, the concept for a Class Profile dashboard would level up insights at the class level and bring much-needed efficiency and productivity to educators at all levels.
Reflection: Designing with, Not Just For, AI
The Student Profile App was originally a visualization challenge: How to make complex learning data intuitive and actionable for educators. Reimagined today, it becomes an AI-powered understanding system, one that not only shows progress but anticipates needs, prompts reflection, and supports teacher intuition with adaptive intelligence.
My takeaway from this reflection is that AI shouldn’t replace design, it should expand the designer’s empathy bandwidth. It lets us see users more clearly, test ideas more dynamically, and measure outcomes more holistically.
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In education, the goal is not automation but amplification, helping teachers focus on what only humans can do: connect, motivate, and inspire learning.
