eduPhysics Physics Mastery Agent — From Prototype to Cloud Deployment Update
A major milestone for the eduPhysics Physics Mastery Agent!
The project has evolved from an initial Physics tutoring prototype into a tested, containerized, cloud-deployed agentic learning system.
The agent is designed around an adaptive learning loop:
Diagnose → Retrieve → Explain → Challenge → Evaluate → Detect → Adapt → Re-test → Advance
Instead of simply answering a Physics question, the agent attempts to understand what the student actually knows and determine the next best learning action.
What has been implemented
• Evidence-grounded Physics knowledge retrieval • Student-response evaluation • Persistent mastery state across learning turns • Adaptive tutoring behavior • Misconception-aware learning workflow • Diagnostic questioning • Adaptive conceptual and numerical challenges • Student reasoning evaluation • Student-readable mathematical solutions without raw LaTeX formatting • Physics corpus packaged into the production container • Docker containerization • Google Cloud deployment
Testing
The project currently has 121 automated tests passing.
This was particularly important because the goal is not just to demonstrate an AI response, but to verify that the agent's learning state and tutoring behavior persist across interactions.
Cloud deployment
The tested container has now been deployed to Google Cloud Run in asia-south1, with Gemini-powered inference and the required cloud infrastructure configured.
Example learning interaction
When a student says:
"I don't understand terminal velocity."
the agent doesn't necessarily start with a long explanation.
It can first investigate the student's understanding of gravity, air resistance, force balance, and acceleration.
If the student demonstrates partial understanding, the agent can move forward to questions such as:
"What happens when the upward drag force becomes equal to the downward weight?"
This allows the system to turn a question-answer interaction into a learning progression.
The bigger idea
The central principle behind the project is:
Don't just answer the student's question. Determine what the student needs to learn next.
The next phase is to expand this focused demonstration into a broader Physics Mastery Map, with prerequisite-aware learning paths, adaptive numerical practice, CBSE/NEET/JEE preparation, misconception tracking, revision planning, and deeper integration with the eduPhysics learning ecosystem.
Building this as a solo project has been challenging, but seeing the agent move from local experiments → automated testing → Docker → Google Cloud deployment has been a major milestone.
The journey from AI tutor to AI learning companion continues.
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