AI Lecture Note Generator
Budget: $750 – $1,500 USD
I want to create an AI-driven module that sits inside a nursing / radiology education platform and automatically turns live lecture recordings into clean, well-structured study material.
First milestone – and the priority for this post – is the note-generation engine. As soon as a session ends (or even in near-real time), the system should:
• capture the audio feed,
• transcribe it with medical-grade accuracy,
• summarise the key points, learning objectives and any drug / anatomy terms,
• save everything to the correct course module for later retrieval.
Once that core workflow is solid, I’ll extend the same pipeline to generate flash cards and short-answer quizzes from the final notes, so please keep that in mind when choosing architecture and data schemas.
Key technical needs
• Robust speech-to-text (Whisper, DeepSpeech, or another engine that handles heavy medical vocabulary).
• NLP summarisation model fine-tuned for didactic language.
• Simple web interface or API endpoint where I can upload / stream the recording and immediately see the generated notes.
• Database or cloud storage layer that tags notes by course, lecturer, and date.
Acceptance criteria for this first deliverable
1. A working prototype I can run locally or on a small cloud instance.
2. Transcription word error rate ≤ 10 % on a supplied 10-minute nursing lecture.
3. Summary no longer than 15 % of original transcript while retaining clinically relevant details.
4. Clear README covering setup, model choices and how to retrain on additional data.
Timeline is tight—ASAP—so reusable models, pre-trained checkpoints, and pragmatic engineering are welcome. When you reply, include links or repos that show past work on speech recognition, summarisation, or ed-tech tooling; that will help me gauge fit quickly.
First milestone – and the priority for this post – is the note-generation engine. As soon as a session ends (or even in near-real time), the system should:
• capture the audio feed,
• transcribe it with medical-grade accuracy,
• summarise the key points, learning objectives and any drug / anatomy terms,
• save everything to the correct course module for later retrieval.
Once that core workflow is solid, I’ll extend the same pipeline to generate flash cards and short-answer quizzes from the final notes, so please keep that in mind when choosing architecture and data schemas.
Key technical needs
• Robust speech-to-text (Whisper, DeepSpeech, or another engine that handles heavy medical vocabulary).
• NLP summarisation model fine-tuned for didactic language.
• Simple web interface or API endpoint where I can upload / stream the recording and immediately see the generated notes.
• Database or cloud storage layer that tags notes by course, lecturer, and date.
Acceptance criteria for this first deliverable
1. A working prototype I can run locally or on a small cloud instance.
2. Transcription word error rate ≤ 10 % on a supplied 10-minute nursing lecture.
3. Summary no longer than 15 % of original transcript while retaining clinically relevant details.
4. Clear README covering setup, model choices and how to retrain on additional data.
Timeline is tight—ASAP—so reusable models, pre-trained checkpoints, and pragmatic engineering are welcome. When you reply, include links or repos that show past work on speech recognition, summarisation, or ed-tech tooling; that will help me gauge fit quickly.