Reimagining online learning for nontraditional students
The University of Texas at San Antonio approached Big Tomorrow to help them reimagine a digital platform tailored to helping non-traditional learners navigate their journeys through higher education.
Company
Big Tomorrow
Client
UTSA (University of Texas at San Antonio)
Role
Lead designer
My responsibilities
Research, workshop facilitation, initial concepts, wireframes, visual comps, prototyping
UTSA spent a decade rebuilding the institution around its students; the next phase was rebuilding the digital experience around them.
01 CHALLENGE
UTSA tripled its four-year graduation rate over a decade from 6% in 2010 to 32% in 2022 by rebuilding advising, retention, and student-services infrastructure for a student body that is majority Hispanic, 44% first-generation, and overwhelmingly transfer, working, and part-time.
UTSA, as well as other institutions for higher education, face increased pressure in a number of areas:
Student retention and persistence
Low engagement from non-traditional students
Fragmented digital experiences
Rising student support costs
Difficulty scaling advising services
Complex schedules
Limited campus engagement
Administrative friction
Decision fatigue
Lack of personalized guidance
Difficulty understanding pathways and requirements
Adhering to the rigid structuring of higher education that doesn’t easily accomodate their complicated life situations
Non-traditional students face challenges such as:
We partnered closely with The University of Texas to map multiple student journey pathways, informing the design of a more thoughtful online learning platform tailored to the unique needs of each learner. This resulted in a newly designed student dashboard, marketplace, and AI-powered digital student companion.
02 APPROACH
We facilitated a series of workshops with UT and faculty from across departments to map the student lifecycle. The resulting learner lifecycle map highlights key moments across the experience—from the Marketplace to the Learning Dashboard to the Activity Player—while surfacing opportunities to align triggers, interventions, the community of care, and enabling systems along the journey.
Student lifecycle
Designing Adaptive Dashboards Through Non-Traditional Learner Personas
Overview
Our team was tasked with improving the learner dashboard experience for a diverse audience with widely different motivations, behaviors, and support needs. Early research showed that traditional personas based on role, age, or experience level were too broad to meaningfully guide design decisions. Learners with similar demographics often interacted with the platform in completely different ways.
In order to create a more effective experience, we shifted toward building non-traditional learner personas based on behaviors, goals, emotional drivers, and learning contexts. These personas became the foundation for identifying, designing, and prioritizing dashboard modules tailored to distinct learner needs.
Personas
The team developed personas to better understand the needs, motivations, and barriers of non-traditional students. Instead of designing for a generic “student,” the team grounded product decisions in realistic learner archetypes built from interviews, behavioral research, enrollment data, and student support insights.
The personas represented a wide spectrum of life situations and educational motivations:
The Returning Veteran — transitioning from military service into academics, valuing structure, clarity, and career alignment while navigating identity shifts and administrative complexity.
The On-the-fence Freshman — uncertain about college value and direction, needing reassurance, guidance, and early wins to stay engaged.
The Swamped Caregiver — balancing education with caregiving and household responsibilities, prioritizing flexibility, mobile access, and low-friction experiences.
The Military Trainee — completing coursework alongside demanding training schedules, requiring asynchronous learning and resilient access across devices and environments.
The Focused Professional — career-oriented and efficiency-driven, seeking credentialing, advancement, and streamlined pathways with minimal wasted time.
The Reluctant Completer — previously stopped out or disengaged, often carrying frustration or self-doubt, needing encouragement, simplicity, and momentum-building support.
Journey Mapping
To determine which components should appear most prominently for each learner type, we conducted a prioritization workshop to map out the different journeys of each of our established personas. Through this process we were able to evaluate:
What information students needed most urgently
What actions created the greatest friction
Which workflows most impacted persistence and completion
What support students needed during high-stress moments
Building the Dashboard Component Library
We first identified the core information and actions students needed throughout their educational journeys. These were translated into reusable dashboard modules that could be combined, prioritized, or surfaced differently depending on the learner.
Examples of components included:
Upcoming assignments and deadlines
Course progress tracking
Registration and enrollment alerts
Financial aid reminders
Career recommendations
Each component was designed as part of a scalable system with consistent interaction patterns, hierarchy, and responsive behavior across devices.
Evolution from Student Dashboard to AI-Powered Digital Companion
What began as an effort to redesign the student dashboard quickly evolved into a broader exploration of how institutions support non-traditional learners throughout their educational journeys. While tailored student dashboards improved the experience for non-traditional learners, students still struggled with navigating complexity, uncertainty, and competing life responsibilities.
Through research and journey mapping with non-traditional learners, the team discovered that many students were not engaging consistently with traditional dashboards, not because the information lacked value, but because the interaction model itself often required too much time, attention, and cognitive effort.
However, non-traditional students frequently interacted with their education in shorter, more fragmented moments throughout the day between work shifts, during commutes, while caring for family members, or late at night after other responsibilities were complete.
This insight led the team to explore a more accessible and conversational interaction model through an AI-powered digital student companion.
From Dashboard to Conversation
Starting Point — A dynamic, comprehensive student dashboard. Information-rich, but students wanted decisions.
Pivot Point — Students wanted guidance, not a wall of data.
Service Transition — Conversation hands off to traditional calendar UI.
Inline Applet — Complex task surfaced inside the chat thread without breaking flow.
Blending Conversational & Traditional UI
In order for the university to deliver a truly context-driven experience that is both flexible and legible, an AI-first interaction model needs to blend the best of conversational and traditional interfaces:
Conversational baseline — Most interactions start with a conversation so the system can determine a learner’s intent in an open-ended way. Rich actions embedded into keyboard conventions help learners navigate what actions are available during a given interaction.
Service transitions — As a conversation narrows to a particular task, service transitions help surface new sets of actions and capabilities through keyboard conventions.
Inline applets — When a particular action is more complex, small apps more consistent with traditional UI are contextually surfaced inline by the system. When the action’s complete, the applet disappears to preserve the conversational flow of the interaction.
Designing for Real-Life Contexts
The conversational approach better aligned with the realities of non-traditional students because it supported:
Quick, mobile-first interactions
Asynchronous engagement
Low cognitive load
Just-in-time guidance
Personalized recommendations
Task prioritization during high-stress periods
Instead of requiring students to interpret data and determine next steps themselves, the AI could proactively surface the most relevant information and help students act more confidently in the moment.
03 RESULT
We delivered a strategic concept: personas, journey maps, a component library, and an AI-companion prototype. Production sat outside our scope and went to Salesforce.
04 REFLECTION
If I could go back, the artifact I'd most want is six months of usage data on the conversational/traditional split, which queries got dropped from chat into UI, and how often students bounced back.
This project reinforced the importance of designing for ambiguity. Educational journeys are rarely linear, especially for nontraditional students whose goals, responsibilities, motivations, and schedules continuously evolve. Designing for this audience requires building systems that can adapt over time rather than rigidly prescribing a single path forward.
As AI becomes increasingly embedded within educational infrastructure, designing responsible systems that balance autonomy, trust, and oversight will become just as important as the interfaces themselves.