Smart, secure, student-centered recommendations
Enrolla brings together student insights and program data to recommend the right fit.
How It Works
Enrolla evaluates program fit by combining three key data sources: personality traits from the Big Five, student preferences from the Program Fit Assessment, and program metadata. These insights create personalized, high-accuracy recommendations.
Understand the student behind the application
Through the Big Five assessment, we capture each student’s core personality traits, helping match them with programs that fit their natural strengths, motivations, and learning styles.
Discover your ideal program
Align programs with student goals
The Program Fit Assessment gathers each student’s academic and lifestyle preferences, from ideal learning environments to program duration. Ensuring recommendations reflect what students truly want.
Leverage rich program data
By mapping detailed attributes like career outcomes, delivery method, and study abroad options, we ensure each recommendation connects students with the programs best suited to their interests and goals.
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Transparent, explainable logic
Our goal is to avoid “black box” AI. The recommendation output includes:
Ranked List
Explore a prioritized list of programs tailored to you.
Fit Factors
Understand the key reasons behind each recommendation.
Confidence Score
See how confident the system is in each match.
Private by design
The AI runs in a secure environment and never uses student data for external training. We:
Encrypt and isolate student input
Use institution-specific logic where applicable
Allow for on-premise or region-specific hosting
Continuously learning, never guessing
As more students use the system, we’re able to enhance:
Recommendation accuracy
Response-time efficiency
Personalization depth
Our AI stays current with human-reviewed updates and institution-level controls for transparency.
See the AI in action
Want to test how it interprets your institution’s program list? Book a walkthrough and we’ll show how the recommendation logic works, step-by-step.
