An AI-driven Assistant leveraging OpenAI's APIs for interactive tutoring, comprehension assessment and continuous improvement of any topic!
Develop an AI assistant leveraging OpenAI's APIs, integrating text-to-speech (TTS), speech-to-text (STT), and other tools to streamline employee training, tutor users interactively, and assess their understanding.
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Document Intake & Processing
- Document Upload: Allow users to upload various document types.
- Content Analysis: Utilize GPT-4 for comprehension and goal identification.
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Goal Formation
- Objective Derivation: Use the content analysis to generate a structured list of learning objectives or goals.
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Interactive Tutoring Session
- Text-to-Speech Conversion: Convert textual content to speech for audio-based interaction.
- Speech-to-Text Input: Enable users to respond orally, transcribing their speech to text for the assistant's comprehension.
- Tutoring Session: Initiate an interactive session where the assistant verbally tutors users about the document content and learning goals.
- Engagement: Engage users through conversational styles, explanations, Q&A, or summaries using speech.
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Understanding Assessment
- Speech-to-Text for Responses: Convert user audio responses to text.
- Evaluation: Analyze and assess user comprehension through their oral responses or follow-up questions.
- Text-to-Speech: Employ TTS APIs to convert textual content into speech for interactive tutoring.
- Speech-to-Text: STT APIs for transcribing user's oral responses into text for assessment.
| User Story ID | User Story Description | Features |
|---|---|---|
| 1 | As a user, I want to upload diverse documents for training material, allowing the AI assistant to comprehend and structure content. | Document upload & processing |
| 2 | As a user, I want the assistant to analyze and identify learning objectives or goals based on the uploaded document's content. | Learning objectives identification |
| 3 | As a user, I want to engage in interactive tutoring sessions where the assistant verbally explains content and learning goals. | Interactive tutoring sessions |
| 4 | As a user, I want the assistant to ask questions or prompt discussions about the content to enhance my understanding. | Content engagement prompts |
| 5 | As a user, I want the assistant to summarize or clarify complex sections of the document using speech for better comprehension. | Speech-based document summarization |
| 6 | As a user, I want to respond orally during the tutoring session, expecting the assistant to transcribe my responses for evaluation. | Oral response transcription |
| 7 | As a user, I want the assistant to evaluate my responses against the learning goals derived from the document. | Response evaluation against learning goals |
| 8 | As a user, I want feedback from the assistant to understand my level of understanding and areas that need improvement. | Understanding feedback |
| 9 | As a user, I want the assistant to convert textual content to speech for an engaging audio-based interaction during the tutoring session. | Text-to-speech conversion |
| 10 | As a user, I expect a user-friendly interface allowing seamless interaction via both speech and text inputs during the session. | Speech and text interface |
| 11 | As an administrator, I want to manage the documents uploaded for training materials and their associated learning goals. | Document and goal management |
| 12 | As a user, I want the assistant's functionalities accessible across various devices or interfaces for flexible usage. | Multi-device accessibility |
| 13 | As a user, I want the assistant to handle errors gracefully, providing clear prompts or explanations in case of misunderstandings. | Error handling and guidance |
| 14 | As a user, I expect support documentation or assistance if I encounter issues during the interaction with the assistant. | Support and assistance |
project_root/
│
├── backend/
│ ├── app/
│ │ ├── document_processing/
│ │ │ ├── upload_handler.py # Backend logic for handling document uploads
│ │ │ └── analysis_service.py # Service to interact with OpenAI's document analysis API
│ │ │
│ │ ├── goal_formation/
│ │ │ └── goal_derivation.py # Algorithm/service for deriving learning objectives from analysis results
│ │ │
│ │ ├── tutoring_session/
│ │ │ ├── tts_service.py # Service to interact with TTS API
│ │ │ ├── stt_service.py # Service to interact with STT API
│ │ │ └── tutoring_logic.py # Backend logic for managing tutoring sessions
│ │ │
│ │ ├── understanding_assessment/
│ │ │ └── evaluation_service.py # Algorithm/service for evaluating user responses against learning objectives
│ │ │
│ │ ├── main.py # FastAPI main application entry point
│ │ └── error_handling.py # Error handling logic for API requests and user inputs
│ │
│ ├── requirements.txt # Backend Python dependencies
│ └── Dockerfile # Docker configuration for the FastAPI backend
│
├── frontend/
│ ├── public/
│ │ ├── index.html # Frontend HTML file
│ │ └── ... # Other public assets
│ ├── src/
│ │ ├── components/ # React components
│ │ ├── styles/ # Stylesheets for React components
│ │ ├── App.js # Main React component
│ │ └── index.js
│ ├── package.json # Frontend Node.js dependencies
│ └── Dockerfile # Docker configuration for the React frontend
│
└── README.md # Project documentation