Interest-Aware Learning

Inspiration

Everyone learns differently, but most educational material is written for a generic audience. A student fascinated by gardening might struggle with an abstract chemistry lesson, even though that chemistry directly affects the plants they grow.

We wanted to explore a simple question: Can an AI agent discover meaningful connections between educational material and a learner's personal interests without compromising scientific accuracy?

Interest-Aware Learning is our answer.

What it does

Interest-Aware Learning transforms ordinary educational material into explanations that connect with what a learner already cares about.

The learner supplies three things:

1. An interest they explicitly choose.
2. A passage of educational material.
3. A question they want answered.

The application uses Qloo's cultural intelligence to discover potentially relevant associations, researches promising connections, and generates an explanation grounded in the original educational material.

For example, we pasted a generic textbook passage about acids, bases, and pH, then added gardening as the learner's interest. The application independently connected the chemistry to soil pH and plant nutrient availability, using research from university agricultural extension services.

The original chemistry passage contained no gardening examples.

How we built it

Interest-Aware Learning uses a bounded, multi-stage agentic workflow implemented in Python.

Qloo — Cultural discovery: The agent resolves explicitly supplied interests and explores Qloo's tags, insights, and cultural associations. These provide candidate connections for making educational concepts personally relevant.

Tavily — Research: The agent searches for evidence supporting promising connections. Cultural associations are treated as personalization signals, not scientific proof.

OpenAI Responses API — Lesson generation: The agent writes structured educational explanations with intuitive examples, topic coverage, and key takeaways.

Source grounding and validation: The application analyzes the original source material and checks the generated lesson against its grounded claims. Web references are presented when suitable research evidence is available.

When verified cultural associations or suitable research are unavailable, the workflow can fall back to a source-grounded explanation rather than fabricating evidence.

The interface uses HTML, CSS, and JavaScript. The hosted demonstration runs on Render, with Upstash Redis managing a persistent shared generation allowance.

Challenges we faced

The biggest challenge was balancing personalization with factual reliability.

A cultural association can suggest a good teaching example, but it cannot establish whether a scientific claim is true. We had to separate cultural discovery, research verification, and educational explanation.

We also needed meaningful fallback behavior. An educational tool should not stop working simply because a cultural match is weak or web research returns no useful evidence.

Finally, deploying a live demonstration required secure access controls, persistent usage limits, and safeguards against uncontrolled API spending.

Accomplishments we're proud of

We completed two successful live hosted demonstrations.

Chemistry + gardening: A generic explanation of acids, bases, and pH became a gardening-focused lesson with verified Qloo interest evidence and external research citations.

Physics + Daft Punk: A generic explanation of sound waves became an intuitive lesson using electronic-music examples. Although some enrichment stages used fallback behavior, the application still produced a coherent, source-grounded explanation without claiming unsupported Qloo evidence.

The application also includes a readable learning interface, source and research citations, bounded workflow execution, and automated offline tests.

What we learned

Personalization is most useful when it helps explain the underlying concept rather than simply adding familiar words to a lesson.

We also learned that an AI agent needs to distinguish different kinds of evidence. Qloo can suggest culturally relevant connections, research can substantiate factual applications, and the learner's supplied material must remain central to the explanation.

A transparent fallback can be more trustworthy than an impressive-looking but unsupported connection.

What's next

We would like to improve the depth and variety of cultural connections, explore additional educational subjects, and test whether interest-aware explanations measurably improve learner comprehension and engagement.

Our longer-term vision is a learning tool that helps people approach unfamiliar subjects through the interests they already have—without sacrificing the accuracy of what they're learning.

Built With

• css3
• html5
• javascript
• openai-api
• python
• qloo-api
• redis
• render
• tavily
• upstash