Case Study: Freshman On-Track Intervention Recommender

Freshman year performance is the strongest predictor of high school graduation, yet educators often lack systematic tools to match at-risk 9th graders with evidence-based interventions. The challenge was creating a system that could bridge the gap between educational research and practical classroom application.

• Inconsistent Intervention Selection: Educators relied on intuition rather than proven best practices, leading to varied and potentially ineffective support
• Information Overload: Educational research was scattered across numerous documents, making it difficult for busy educators to quickly find relevant strategies
• Time Constraints: Teachers needed immediate, actionable guidance but lacked the time for extensive research
• Communication Barriers: Different stakeholders (teachers, parents, administrators) required recommendations tailored to their specific roles and perspectives

I designed a sophisticated RAG system to transform educational research into an intelligent, conversational interface. The approach centered on making evidence-based interventions immediately accessible and actionable, augmenting rather than replacing educator expertise.

Strategic Design Decisions:

• Quality-First Data Strategy: Instead of building a complex PDF extraction pipeline, I manually curated a high-quality knowledge base from authoritative sources to guarantee maximum accuracy
• Semantic Chunking: Developed a concept-based chunking strategy that groups related interventions together, creating more meaningful and contextually rich data
• Persona-Based Generation: Implemented distinct prompt templates for teachers, parents, and principals, ensuring recommendations were appropriately tailored for each audience
• Evidence-Based Foundation: Grounded all recommendations in peer-reviewed research from organizations like Network for College Success and IES

AI-Human Collaboration Methodology:

This project leveraged AI as a strategic co-pilot throughout development. I engaged multiple models in iterative dialogue to refine architecture and implementation decisions, while carefully managing conversational context to maintain high standards and drastically reduce hallucination, leading to better, more consistent code quality.

The system was built with a modern Python stack and deployed as a live, interactive application. The architecture prioritizes accuracy, maintainability, and a clear path to production scaling.

Core RAG Architecture:

• Knowledge Base: Built from 6 authoritative educational sources, manually curated into 27 high-relevance intervention chunks using semantic clustering
• Retrieval Engine: Utilizes the all-MiniLM-L6-v2 sentence transformer with FAISS vector database for efficient semantic search
• Generation Engine: Integrates Google's Gemini 1.5 Flash model with three distinct, persona-optimized prompt templates
• Quality Controls: Implements similarity score filtering to ensure only the most relevant evidence is used in recommendations

Production Features:

• Live Interactive Demo: Deployed on Hugging Face Spaces with Gradio interface and example scenarios
• Modern Python Stack: Python 3.12 with fast dependency management via uv, formatted with black and ruff
• Source Attribution: Full transparency with page numbers and relevance scores for all recommendations
• Secure Access: Protected demo environment with access key authentication

Technologies Used:

• Python 3.12 with sentence-transformers and FAISS for retrieval
• Google Gemini 1.5 Flash for natural language generation
• Gradio for interactive web interface
• Hugging Face Spaces for deployment and hosting
• Comprehensive testing suite with pytest and quality tooling

The FOT Intervention Recommender successfully transforms scattered educational research into an intelligent, accessible tool that empowers educators with evidence-based guidance.

The system delivers significant value to educational stakeholders:

• Immediate Access to Best Practices: Transforms hours of research into seconds of targeted recommendations based on student narratives
• Improved Intervention Quality: Grounds recommendations in peer-reviewed research, moving beyond intuition-based decisions
• Enhanced Stakeholder Communication: Tailored outputs ensure teachers, parents, and administrators receive relevant, actionable guidance
• Complete Transparency: Full source attribution with page numbers and relevance scores builds trust and enables verification
• Scalable Architecture: Production-ready codebase with comprehensive testing enables easy expansion and deployment

This project reinforced several important principles for building domain-specific AI systems:

• Quality Over Quantity: Manual curation of high-quality knowledge significantly outperformed automated extraction in accuracy and relevance
• Domain Expertise Matters: Understanding the educational context was crucial for designing effective chunking strategies and prompt templates
• User-Centric Design: Different stakeholders need different types of information presented in different ways—one size doesn't fit all
• Transparency Builds Trust: Providing source attribution and confidence scores was essential for adoption in high-stakes educational contexts
• AI as Co-Pilot: Using AI strategically throughout development accelerated progress while maintaining quality through careful context management

These insights directly inform how I approach building AI systems for other knowledge-intensive domains where accuracy and trust are paramount.

This proof-of-concept establishes a strong foundation. To evolve this tool into a robust, production-grade system, I would focus on the following key areas:

1. Containerization & API Deployment

Package the application into a formal REST API using FastAPI and containerize it with Docker. This enables deployment to a scalable, serverless platform like Google Cloud Run, ensuring high availability and cost-effective operation.

2. Building the "Data Flywheel" for Evaluation

True long-term value comes from learning and improving. I would implement an observability layer to capture input/output pairs. This data would then be used to:

• Create a system for subject-matter experts (educators) to label and rate the quality of recommendations.
• Establish a continuous evaluation pipeline to measure model performance against this "golden dataset," creating a data flywheel that drives ongoing improvements.

3. Enhancing System Modularity & Scalability

To ensure the system is adaptable and future-proof, I would refactor the core logic to be more modular. This includes:

• Abstracting the core components to easily swap out embedding models, generative models (e.g., GPT, Claude), or vector databases (e.g., Pinecone, Weaviate) to leverage the best technology as it evolves.
• Developing more advanced data preparation and semantic chunking strategies to handle a larger and more diverse knowledge base.