AI-Powered EdTech Content Processing MVP
Budget: $750 – $1,500 USD
Freelancer Contract Scope: Development of an LLM-Based Educational Content Processing System (MVP)
Project Overview
We seek an experienced developer/team to build a Minimum Viable Product (MVP) for an AI-powered educational content processing system. The system will use an API-based Large Language Model (LLM) to process study materials (PDFs, Word documents, PowerPoint slides, images) and generate study aids: summaries, Q&As, mind maps, and flashcards. The MVP targets students and educators via mobile (iOS/Android) and desktop (Windows/macOS) apps, with integration capabilities for learning platforms (e.g., Canvas, Moodle). It must scale to 100,000 users, ensure secure data handling, and be delivered within one month on a $1,000–$2,000 budget.
Objectives
• Develop an MVP using an API-based LLM to process educational materials.
• Generate summaries, Q&As, mind maps, and flashcards from uploaded files.
• Provide mobile and desktop apps for file upload and result viewing.
• Enable integration with learning platforms (Canvas, Moodle).
• Ensure scalability for 100,000 users with secure, cost-efficient design.
Scope of Work
The freelancer will design, develop, test, and deploy an MVP with these components:
1. Input Processing Module
• File Upload and Parsing:
• Support PDFs, Word (.docx), PowerPoint (.pptx), images (.jpg, .png).
• Extract text using PyPDF2 (PDFs), python-docx (Word), Tesseract OCR (images).
• Handle files up to 10 MB.
• Preprocess content to remove noise (e.g., headers, footers).
• Content Analysis:
• Use an API-based LLM to identify key concepts/topics.
• Support English-only content.
2. LLM Integration
• Model:
• Integrate an API-based LLM (e.g., OpenAI GPT, Anthropic Claude, xAI Grok via https://x.ai/api).
• Optimize API calls (e.g., batch processing, caching) to reduce costs.
• Content Processing:
• Summaries: 100–200 words.
• Q&As: 5–10 multiple-choice/short-answer questions per document.
• Mind Maps: Text-based hierarchical structure.
• Flashcards: 10–20 term/definition pairs per document.
• Customization:
• Basic output settings (e.g., summary length, question count).
3. Output Generation
• Summaries: Bullet-point, exportable as .txt/.pdf.
• Q&As: Text-based, exportable as .csv.
• Mind Maps: Text-based (JSON), convertible to visuals externally.
• Flashcards: Exportable as .csv for Anki/Quizlet.
• Export: Downloadable as .txt, .csv, .pdf.
4. User Interface (UI)
• Platforms:
• Mobile app (iOS/Android) via Flutter/React Native.
• Desktop app (Windows/macOS) via Electron/Flutter.
• Features:
• Drag-and-drop file upload.
• Interface for selecting outputs and viewing results.
• Progress indicator.
• Usability:
• Responsive design.
• Error handling for unsupported files/API issues.
5. Integration Tools
• Learning Platforms:
• Basic integration with Canvas/Moodle via APIs (e.g., upload outputs to courses).
• Use OAuth for secure authentication.
• Export Compatibility:
• Outputs compatible with learning management systems (.csv for quizzes, .pdf for notes).
6. Backend and Infrastructure
• Backend:
• FastAPI for LLM calls and file processing.
• Minimal RESTful APIs for frontend-backend communication.
• Database:
• SQLite for user profiles/cached outputs.
• Cloud:
• Deploy on Heroku, AWS Free Tier, or Vercel.
• Use AWS S3 for temporary file storage.
• Scalability:
• Support 100,000 users via serverless architecture/caching.
• Implement rate limiting for API costs.
• Security:
• HTTPS, JWT authentication, file encryption.
• Delete files post-processing.
7. Testing
• Unit testing: File parsing, LLM outputs, UI.
• Integration testing: Frontend-backend-LLM communication.
• Accuracy testing: Validate outputs with sample materials.
• Performance testing: Process 10-page PDF in <30 seconds.
8. Documentation
• Technical: Codebase/deployment overview (2–3 pages).
• User: In-app help section (e.g., “How to Upload Files”).
• Handover: Brief system walkthrough.
Deliverables
1. Source code (GitHub, commented).
2. Deployed MVP (mobile/desktop apps on cloud).
3. Documentation (technical/user guides, 2–3 pages each).
4. Test results (accuracy/performance summary).
5. Sample outputs (summaries, Q&As, mind maps, flashcards).
Technical Requirements
• Languages: Python (backend), JavaScript/Dart (frontend via Flutter/React Native).
• Frameworks:
• Backend: FastAPI.
• Frontend: Flutter/React Native.
• File Processing: PyPDF2, python-docx, Tesseract OCR.
• Infrastructure:
• Cloud: Heroku/AWS Free Tier/Vercel.
• Database: SQLite.
• Performance: Process 10-page PDF in <30 seconds.
• Security: HTTPS, JWT, file encryption.
Project Timeline (1 Month)
• Week 1: Requirements, design, LLM API setup.
• Week 2: File processing, LLM integration, outputs.
• Week 3: UI, backend APIs, Canvas/Moodle integration.
• Week 4: Testing, deployment, documentation, handover.
Budget and Payment Terms
• Budget: $1,000–$2,000.
• Payment Schedule:
• 30% at kickoff.
• 40% after file processing/LLM integration.
• 30% at final delivery.
• Bidding Requirements:
• Proposal with timeline, cost, experience.
• Examples of AI/EdTech projects.
Developer Qualifications
• Experience with API-based LLMs (e.g., OpenAI, xAI Grok).
• Proficiency in Python, Flutter/React Native, FastAPI.
• Familiarity with file processing (PDFs, Word, images), OCR.
• Cloud deployment (Heroku, AWS) and scalability expertise.
• Ability to deliver MVP under tight constraints.
Additional Notes
• Weekly progress updates required.
• Client provides sample study materials.
• Document third-party API costs (e.g., LLM, OCR).
• MVP focuses on core features; future phases may expand.
How to Apply
Submit bids to [your email/freelancer platform link] by [April 22, 2025] with:
• Proposal (approach, timeline, cost).
• AI/EdTech project examples.
• Resume/team profile.
Instructions to Create a PDF
Follow these steps to convert the scope into a PDF:
1. Copy the Text:
• Highlight and copy the scope above (from “Freelancer Contract Scope” to “How to Apply”).
2. Paste into a Word Processor:
• Open Microsoft Word, Google Docs, or similar.
• Paste the text.
• Format for clarity:
• Use Heading 1 for “Freelancer Contract Scope.”
• Use Heading 2 for sections (e.g., Project Overview, Objectives).
• Use bullet points for lists.
• Choose a professional font (e.g., Arial or Times New Roman, 12pt).
• Add page breaks if needed for readability.
3. Export as PDF:
• Microsoft Word: File → Save As → Select “PDF” format.
• Google Docs: File → Download → PDF Document.
• Save as “Educational_LLM_Scope.pdf.”
4. Review:
• Open the PDF to ensure formatting is correct (e.g., no cut-off text, clear headings).
• Adjust margins or font size if needed.
5. Alternative Tools:
• Use online converters (e.g., SmallPDF, ILovePDF):
• Paste text into a .docx file.
• Upload to the converter and download as PDF.
• Use markdown-to-PDF tools (e.g., Pandoc):
• Save the scope as scope.md.
• Run pandoc scope.md -o scope.pdf (requires Pandoc installation).
6. Optional Enhancements:
• Add a cover page with your project title and contact info.
• Include a table of contents for professionalism (auto-generated in Word/Docs).
Notes on Feasibility
• Budget/Timeline: The $1,000–$2,000 budget and one-month timeline are very restrictive for mobile/desktop apps, 100,000-user scalability, and integrations. The MVP scope is minimized (e.g., small file sizes, English-only, basic UI) to fit. You may need to:
• Prioritize mobile or desktop if bids exceed budget.
• Consider a phased approach (e.g., MVP now, scaling later).
• LLM Costs: API-based LLMs (e.g., xAI Grok, OpenAI) incur usage fees. Developers must optimize calls to stay within budget.
• Scalability: Serverless architecture and caching are proposed to handle 100,000 users cost-effectively.
Project Overview
We seek an experienced developer/team to build a Minimum Viable Product (MVP) for an AI-powered educational content processing system. The system will use an API-based Large Language Model (LLM) to process study materials (PDFs, Word documents, PowerPoint slides, images) and generate study aids: summaries, Q&As, mind maps, and flashcards. The MVP targets students and educators via mobile (iOS/Android) and desktop (Windows/macOS) apps, with integration capabilities for learning platforms (e.g., Canvas, Moodle). It must scale to 100,000 users, ensure secure data handling, and be delivered within one month on a $1,000–$2,000 budget.
Objectives
• Develop an MVP using an API-based LLM to process educational materials.
• Generate summaries, Q&As, mind maps, and flashcards from uploaded files.
• Provide mobile and desktop apps for file upload and result viewing.
• Enable integration with learning platforms (Canvas, Moodle).
• Ensure scalability for 100,000 users with secure, cost-efficient design.
Scope of Work
The freelancer will design, develop, test, and deploy an MVP with these components:
1. Input Processing Module
• File Upload and Parsing:
• Support PDFs, Word (.docx), PowerPoint (.pptx), images (.jpg, .png).
• Extract text using PyPDF2 (PDFs), python-docx (Word), Tesseract OCR (images).
• Handle files up to 10 MB.
• Preprocess content to remove noise (e.g., headers, footers).
• Content Analysis:
• Use an API-based LLM to identify key concepts/topics.
• Support English-only content.
2. LLM Integration
• Model:
• Integrate an API-based LLM (e.g., OpenAI GPT, Anthropic Claude, xAI Grok via https://x.ai/api).
• Optimize API calls (e.g., batch processing, caching) to reduce costs.
• Content Processing:
• Summaries: 100–200 words.
• Q&As: 5–10 multiple-choice/short-answer questions per document.
• Mind Maps: Text-based hierarchical structure.
• Flashcards: 10–20 term/definition pairs per document.
• Customization:
• Basic output settings (e.g., summary length, question count).
3. Output Generation
• Summaries: Bullet-point, exportable as .txt/.pdf.
• Q&As: Text-based, exportable as .csv.
• Mind Maps: Text-based (JSON), convertible to visuals externally.
• Flashcards: Exportable as .csv for Anki/Quizlet.
• Export: Downloadable as .txt, .csv, .pdf.
4. User Interface (UI)
• Platforms:
• Mobile app (iOS/Android) via Flutter/React Native.
• Desktop app (Windows/macOS) via Electron/Flutter.
• Features:
• Drag-and-drop file upload.
• Interface for selecting outputs and viewing results.
• Progress indicator.
• Usability:
• Responsive design.
• Error handling for unsupported files/API issues.
5. Integration Tools
• Learning Platforms:
• Basic integration with Canvas/Moodle via APIs (e.g., upload outputs to courses).
• Use OAuth for secure authentication.
• Export Compatibility:
• Outputs compatible with learning management systems (.csv for quizzes, .pdf for notes).
6. Backend and Infrastructure
• Backend:
• FastAPI for LLM calls and file processing.
• Minimal RESTful APIs for frontend-backend communication.
• Database:
• SQLite for user profiles/cached outputs.
• Cloud:
• Deploy on Heroku, AWS Free Tier, or Vercel.
• Use AWS S3 for temporary file storage.
• Scalability:
• Support 100,000 users via serverless architecture/caching.
• Implement rate limiting for API costs.
• Security:
• HTTPS, JWT authentication, file encryption.
• Delete files post-processing.
7. Testing
• Unit testing: File parsing, LLM outputs, UI.
• Integration testing: Frontend-backend-LLM communication.
• Accuracy testing: Validate outputs with sample materials.
• Performance testing: Process 10-page PDF in <30 seconds.
8. Documentation
• Technical: Codebase/deployment overview (2–3 pages).
• User: In-app help section (e.g., “How to Upload Files”).
• Handover: Brief system walkthrough.
Deliverables
1. Source code (GitHub, commented).
2. Deployed MVP (mobile/desktop apps on cloud).
3. Documentation (technical/user guides, 2–3 pages each).
4. Test results (accuracy/performance summary).
5. Sample outputs (summaries, Q&As, mind maps, flashcards).
Technical Requirements
• Languages: Python (backend), JavaScript/Dart (frontend via Flutter/React Native).
• Frameworks:
• Backend: FastAPI.
• Frontend: Flutter/React Native.
• File Processing: PyPDF2, python-docx, Tesseract OCR.
• Infrastructure:
• Cloud: Heroku/AWS Free Tier/Vercel.
• Database: SQLite.
• Performance: Process 10-page PDF in <30 seconds.
• Security: HTTPS, JWT, file encryption.
Project Timeline (1 Month)
• Week 1: Requirements, design, LLM API setup.
• Week 2: File processing, LLM integration, outputs.
• Week 3: UI, backend APIs, Canvas/Moodle integration.
• Week 4: Testing, deployment, documentation, handover.
Budget and Payment Terms
• Budget: $1,000–$2,000.
• Payment Schedule:
• 30% at kickoff.
• 40% after file processing/LLM integration.
• 30% at final delivery.
• Bidding Requirements:
• Proposal with timeline, cost, experience.
• Examples of AI/EdTech projects.
Developer Qualifications
• Experience with API-based LLMs (e.g., OpenAI, xAI Grok).
• Proficiency in Python, Flutter/React Native, FastAPI.
• Familiarity with file processing (PDFs, Word, images), OCR.
• Cloud deployment (Heroku, AWS) and scalability expertise.
• Ability to deliver MVP under tight constraints.
Additional Notes
• Weekly progress updates required.
• Client provides sample study materials.
• Document third-party API costs (e.g., LLM, OCR).
• MVP focuses on core features; future phases may expand.
How to Apply
Submit bids to [your email/freelancer platform link] by [April 22, 2025] with:
• Proposal (approach, timeline, cost).
• AI/EdTech project examples.
• Resume/team profile.
Instructions to Create a PDF
Follow these steps to convert the scope into a PDF:
1. Copy the Text:
• Highlight and copy the scope above (from “Freelancer Contract Scope” to “How to Apply”).
2. Paste into a Word Processor:
• Open Microsoft Word, Google Docs, or similar.
• Paste the text.
• Format for clarity:
• Use Heading 1 for “Freelancer Contract Scope.”
• Use Heading 2 for sections (e.g., Project Overview, Objectives).
• Use bullet points for lists.
• Choose a professional font (e.g., Arial or Times New Roman, 12pt).
• Add page breaks if needed for readability.
3. Export as PDF:
• Microsoft Word: File → Save As → Select “PDF” format.
• Google Docs: File → Download → PDF Document.
• Save as “Educational_LLM_Scope.pdf.”
4. Review:
• Open the PDF to ensure formatting is correct (e.g., no cut-off text, clear headings).
• Adjust margins or font size if needed.
5. Alternative Tools:
• Use online converters (e.g., SmallPDF, ILovePDF):
• Paste text into a .docx file.
• Upload to the converter and download as PDF.
• Use markdown-to-PDF tools (e.g., Pandoc):
• Save the scope as scope.md.
• Run pandoc scope.md -o scope.pdf (requires Pandoc installation).
6. Optional Enhancements:
• Add a cover page with your project title and contact info.
• Include a table of contents for professionalism (auto-generated in Word/Docs).
Notes on Feasibility
• Budget/Timeline: The $1,000–$2,000 budget and one-month timeline are very restrictive for mobile/desktop apps, 100,000-user scalability, and integrations. The MVP scope is minimized (e.g., small file sizes, English-only, basic UI) to fit. You may need to:
• Prioritize mobile or desktop if bids exceed budget.
• Consider a phased approach (e.g., MVP now, scaling later).
• LLM Costs: API-based LLMs (e.g., xAI Grok, OpenAI) incur usage fees. Developers must optimize calls to stay within budget.
• Scalability: Serverless architecture and caching are proposed to handle 100,000 users cost-effectively.