Build an Adaptive Exam Preparation AI Tutor using Vercel AI SDK and OpenAI o3
Google's launch of AI-powered learning tools for Indian competitive exam prep highlights the need for personalized tutoring. In this challenge, you will develop an adaptive web-based question synthesizer and AI tutor application using Vercel's AI SDK and OpenAI's o3 model reasoning capability. The application will stream step-by-step solutions for complex competitive exams (JEE, NEET, UPSC), dynamically evaluate student mastery levels, and generate customized practice problems with fine-tuned difficulty curves.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an adaptive competitive exam AI tutor streaming deep reasoning responses using AI SDK and OpenAI o3.
How work is evaluated
Evaluates accuracy of competitive exam solution steps and LaTeX output formatting.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
latex_formatting_test
Verifies that solution steps contain valid LaTeX mathematical notations.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
explanation_clarity
Completeness and step accuracy of problem decomposition. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement real-time streaming LLM responses using Vercel AI SDK in Next.js/TypeScript.
Utilize OpenAI o3 reasoning capabilities for complex step-by-step STEM problem solving.
Build dynamic user mastery estimation models based on historical practice attempts.
Generate structured JSON question schemas with LaTeX mathematical formatting.
Reference links and supporting material
Curated set of competitive examination STEM problems with step-by-step ground truth solutions.
Challenge setup
Evaluates accuracy of competitive exam solution steps and LaTeX output formatting.
Challenge input
JSON with exam_type, subject, problem_statement.
AI SDK
Required framework for streaming user interface integration.
o3
Required model for complex STEM multi-step problem solving.
Evaluated output
JSON with latex_steps, final_answer, and concept_tags.
- Verifies that solution steps contain valid LaTeX mathematical notations.
- Completeness and step accuracy of problem decomposition. • target: 0.95 • range: 0-1
- Benchmark: MathVista STEM Benchmark
- Explanation Clarity target: 0.95
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Exa · Optional
- Vercel AI SDK · Optional
- o3 · Required
- Vercel AI SDK · Optional
Evaluation contract
- latex_formatting_test · Weight 1
- explanation_clarity · Weight 1
Metrics
- explanation_clarity · Target 0.95
Benchmarks
- MathVista STEM Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with exam_type, subject, problem_statement.
Output: JSON with latex_steps, final_answer, and concept_tags.
Execution harness: JavaScript
Evaluation tools
- AI SDK · Streaming AI UI Framework
- o3 · Reasoning Model
Recipe freeze marker
No recipe freeze time is recorded.