Tailored Tracks is an AI-powered platform designed to deliver personalized learning resources to students by aligning their interests and career aspirations with course content. This solution helps students build relevant skills for future careers while empowering faculty to integrate AI into education effectively.

A large state university wanted to begin its AI journey and leverage technology to:

• Drive student engagement.
• Enhance career readiness.
• Empower faculty to integrate AI into education seamlessly.

The challenge was to create a solution that personalizes learning without adding complexity for professors, ensuring both efficiency and relevance.

Tailored Tracks provides a human-in-the-loop AI approach for personalized learning:

1. Student Intake Form – Students outline their major, career goals, and learning objectives.
2. Professor Input – Professors upload the course syllabus, description, and student roster.
3. AI Curation – The system uses IBM watsonx to generate a curated list of resources tailored to each student’s profile.
4. Faculty Review – Professors approve or adjust recommendations to ensure alignment.
5. One-Click Distribution – Approved resources are sent to students via email.

This process offers:

• Students: Targeted guidance on key topics to prioritize for career readiness.
• Professors: An efficient, intuitive way to customize course materials while maintaining oversight.

1. Workshop

• Led a workshop with 30+ attendees (professors, faculty, and university leaders).
• Used a hackathon-style framework to divide participants into teams and guide them through:
• Defining goals and challenges.
• Generating big ideas.
• Storyboarding concepts.
• Voting on the most impactful and feasible idea.
• Outcome: Tailored Tracks was selected as the winning concept to move forward.

2. Build

• Collaborated with AI engineers, solution architects, and business technology leaders to build the proof of concept in under 4 weeks.
• Created student personas and stories to feed example criteria into the LLM for testing resource relevance.
• Conducted human-in-the-loop testing, reviewing 300+ scholarly articles to validate AI-generated recommendations.
• Designed a Figma interface for professors to:
• Manage course content.
• Review and approve recommended learning materials.
• Met with clients weekly to demonstrate progress.
• Presented the final demo to stakeholders, resulting in a sales win.

• Delivered a working proof of concept in <4 weeks.
• Enabled personalized learning at scale with AI + faculty oversight.
• Positioned the university as an AI innovation leader in education.
• Created a foundation for future AI-driven academic solutions.