AI Teaching Assistant

Dashboard
AI Agent using StackAI
Analysis Results
Student Performance Report Ahmed Hassan
Remediation Recommendations for Ahmed
Lowest Performing Students

Inspiration

Many students fall behind not because they lack ability, but because gaps in understanding go unnoticed until grades drop or confidence is already shaken. Teachers are overloaded with grading and lesson planning, and often don’t have time to deeply analyze patterns across dozens of students and assignments.

We were inspired to build AI Teaching Assistant to surface those hidden gaps early — giving teachers clarity and giving students personalized support before small misunderstandings become major barriers.

What it does

AI Teaching Assistant helps teachers and students identify exactly where learning is breaking down.

Teachers can upload:

textbooks, lesson plans, and lecture slides
graded homework, quizzes, and tests The system analyzes student performance against the curriculum and generates:
per-student reports highlighting weak subjects and topic
evidence tied to specific assignments and questions

Students get:

a personalized dashboard of topics they’re struggling with
an interactive AI tutor that explains concepts in their preferred language
voice or text chat for step-by-step help
automatically generated study plans to catch up efficiently

How we built it

We built the frontend using Lovable to rapidly prototype clean teacher and student dashboards.

On the backend, we used Stack AI to create specialized agents:

a curriculum-mapping agent
a student work analyzer
a gap detection agent
a multilingual tutoring agent
a study-plan generator

These agents are orchestrated through secure API routes in a Next.js backend that proxies requests, manages authentication, and stores results in a database for reuse across teacher and student views.

Challenges we ran into

Designing agents that reliably map messy real-world school documents to structured topic hierarchies.
Keeping tutoring responses age-appropriate, supportive, and multilingual.
Protecting API keys while integrating external AI services into a web product.
Handling long-running analyses while keeping the UI responsive.
Confirming that explanations helped students learn rather than just giving answers.

Accomplishments that we're proud of

Built end-to-end teacher and student experiences in a short time.
Created agents that surface topic-level learning gaps with evidence.
Implemented a conversational AI tutor that adapts to language preferences.
Generated dynamic study plans tailored to real weaknesses.
Designed dashboards that feel intuitive and teacher-friendly.

What we learned

AI is most powerful when broken into specialized agents instead of one giant prompt.
Structured outputs are critical for reliable UX.
Teachers value clarity and evidence more than flashy predictions.
Students respond better to supportive, interactive tutoring than static explanations.
Multilingual support isn’t a “nice to have” — it’s essential for equity.