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Case study · EdTech

EdTech Provider · EdTech · Higher Ed

A fine-tuned classroom assistant — trained on the course, not the internet.

A classroom AI agent fine-tuned on course materials that answers student questions instantly, distributes resources, and supports faculty triage.

2,400+

student questions answered weekly

−65%

faculty time spent on Q&A triage

4.8 / 5

student satisfaction with answer quality

EdTech Provider — A fine-tuned classroom assistant — trained on the course, not the internet.

EdTech · Higher Ed

EdTech Provider

Client

EdTech Provider

EdTech · Higher Ed

Headline metric

2,400+

student questions answered weekly

Deliverables

4

shipped to production

Stack

4+ tools

across the build

01

Challenge

Faculty were drowning in repetitive student questions about course content, deadlines, and resources. Generic LLM tools hallucinated on course-specific material. The provider needed an agent that was reliably accurate on the curriculum.

02

Approach

Fine-tuned an open-weight Llama 3.1 model on the curriculum corpus, supplemented with RAG over course documents for current information. Built a refusal layer when the model wasn't confident, plus a faculty review queue for unclear questions.

03

Outcome

Over 2,400 student questions answered weekly with 4.8/5 satisfaction. Faculty triage time dropped 65%, freeing them for higher-value teaching. The agent has been the platform's top-engaged feature for six consecutive months.

Over 2,400 student questions answered weekly with 4. The team owned this end-to-end.

EdTech Provider

EdTech · Higher Ed

2,400+

student questions answered weekly

−65%

faculty time spent on Q&A triage

4.8 / 5

student satisfaction with answer quality

Deliverables

What we shipped.

  • Fine-tuned classroom agent
  • Course-document RAG
  • Faculty review console
  • Analytics dashboard
Stack

The tools we used.

Llama 3.1Fine-tuningRAGNext.js
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