10th K AI: I Built an AI Tutor for My Sister Who Was Stuck on Textbook Questions

A developer built 10th K AI, a retrieval-augmented generation tutor for Karnataka SSLC Class 10 textbooks, created for a sister who struggled to parse textbook question phrasing. The system chunks textbook PDFs, embeds them with Sentence Transformers' all-MiniLM-L6-v2 into a FAISS index, and combines keyword and semantic hybrid retrieval with an open-weight LLM served locally via Ollama to produce answers grounded in textbook evidence, along with question-pattern recognition and memory aids.

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend 10th K AI — An AI Tutor I Built for My Sister My sister is a Class 10 student. I noticed something frustrating when she was studying: sometimes she knew the subject, but still got stuck because the textbook language was difficult to understand. The bigger problem wasn't always "I don't know the answer." It was: "I don't understand what type of question this is asking me." A question might be asking for a person and their contribution, a cause and its effect, a formula and a calculation, or a definition and a term. So I built 10th K AI specifically for her. It is an AI-powered Karnataka SSLC Class 10 textbook tutor that doesn't just give an answer. It helps a student understand: What is the concept? → What question pattern is this? → How do I solve it? → How do I remember it? What I Built 10th K AI lets a student ask a question from their Class 10 textbook and get a response grounded in the actual textbook. For example, if the student asks: Who derived the quadratic formula? 10th K AI searches the Karnataka SSLC textbook and finds the relevant passage: "Sridharacharya C.E. 1025 derived a formula, now known as the quadratic formula..." Instead of stopping there, the tutor turns the information into a learning structure: Answer Textbook Evidence Simple Explanation Question Pattern Memory Trick Similar Practice Question The goal is not just to help a student get one answer. The goal is to help them recognize the pattern the next time they see a similar question. Who I Built It For I built 10th K AI for my sister. While helping her study, I realized that students can struggle even when the information is technically available to them. A textbook may say something like: "Sridharacharya derived a formula..." But an exam question might ask: "Who derived the quadratic formula?" The student needs to recognize: Person → Contribution That small recognition can make a big difference during an exam. That became the central idea behind 10th K AI. How It Works 10th K AI uses a Retrieval-Augmented Generation RAG pipeline. The textbook PDFs are processed into smaller chunks. Each chunk stores information such as: Subject Book Page number Textbook content I created embeddings for the textbook chunks using: Sentence Transformers — all-MiniLM-L6-v2 Those embeddings are stored in a FAISS vector index. When a student asks a question: Student Question ↓ Keyword Search ↓ Semantic Search ↓ Hybrid Retrieval ↓ Relevant Textbook Passage ↓ Open-Source LLM ↓ Learning-focused Answer The LLM is instructed to use the retrieved textbook evidence rather than simply inventing an answer from general knowledge. Open-Source AI at the Core The project uses Ollama and an open-weight model for the AI generation layer. This was important to me because I wanted the project to be built around an AI system that I could actually control and run locally. With Ollama, I can run the model on my own machine during development instead of making my application completely dependent on a closed AI API. The architecture also keeps the LLM layer replaceable, which means I can experiment with different open models without rebuilding the entire RAG system. Why Open Innovation Matters For an educational tool, I think control over the AI layer matters. With a closed API, the model is essentially a service I depend on. With an open model through Ollama, I can: Run inference locally Experiment with different models Control the prompting Change the retrieval strategy Keep the textbook retrieval pipeline under my control Build the system without locking the entire project to one proprietary model For 10th K AI, the combination of open model + local inference + textbook RAG makes the project much more understandable and controllable. The Interesting Part: Question Patterns This is the part I care about most. I don't want 10th K AI to become another chatbot where a student asks: "What is the answer?" and immediately copies it. Instead, I want the student to gradually recognize patterns. For example: Cause → Effect Event → Person Definition → Term Formula → Numerical calculation The student starts learning: "I've seen this type of question before." That is the skill I wanted to build into the tutor. Tech Stack Frontend / App Streamlit AI Ollama Open-weight LLM RAG FAISS Sentence Transformers all-MiniLM-L6-v2 Text Processing PyMuPDF Python Data Karnataka SSLC Class 10 English-medium textbooks Deployment Streamlit Community Cloud Demo Live Demo: https://10thk-ai-d6w3ycqcfe6i4zdecucqxp.streamlit.app/ https://10thk-ai-d6w3ycqcfe6i4zdecucqxp.streamlit.app/ Try asking: or another question from the Karnataka SSLC Class 10 Mathematics, Science, or Social Science textbooks. Code GitHub: https://github.com/aditikshetty/10thK-AI https://github.com/aditikshetty/10thK-AI The repository contains the RAG pipeline, application code, textbook processing code, and retrieval system. Why I Built It I didn't start this project because I wanted to build another AI chatbot. I started because I saw someone I care about getting stuck while studying. That made me think about what an AI tutor should actually do. Maybe the most useful answer isn't always: "Here is the answer." Sometimes it is: "This is the kind of question you're looking at. Here's how you recognize it next time." That's what I wanted 10th K AI to teach. What's Next There is still a lot I want to improve: Better question-pattern classification Support for textbook diagrams and images More accurate similar-question generation Student progress tracking Personalized practice sets Voice-based interaction More regional-language support Better evaluation of RAG accuracy But the first version is now working, and more importantly, it was built for a real person with a real problem. What I Learned The biggest lesson from this weekend wasn't just about RAG or embeddings. It was that a small, specific problem can be much more meaningful than a generic AI idea. Instead of asking: "What AI app can I build?" I started with: "What is frustrating someone I care about, and can I build something to help?" That changed the entire project. Prize Categories I am submitting 10th K AI for the overall Hacktoberfest Weekend Challenge: Build for a Friend.