Skip to content

Artificial Intelligence Final Year Topic: Design and Evaluation of AI Meeting Action Extraction

This Artificial Intelligence final year project examines extracting proposed tasks and owners from fictional meeting transcripts through a bounded AI prototype with an explicit baseline and evaluation dataset.

Why choose this project topic?

A study of extracting proposed tasks and owners from fictional meeting transcripts gives this topic a concrete purpose beyond simply adding an AI model. Comparing task precision, owner accuracy and invented-action rate helps a student demonstrate capability, inspect failure cases and explain where human review or a simpler method remains necessary.

How effectively can ai meeting action extraction support extracting proposed tasks and owners from fictional meeting transcripts, evaluated using task precision, owner accuracy and invented-action rate?

Choose a feasible licensed or synthetic dataset and available compute budget, then agree a narrow evaluation for extracting proposed tasks and owners from fictional meeting transcripts with your supervisor.

Proposed project objectives

  1. 01Define the task, evidence and evaluation assumptions for extracting proposed tasks and owners from fictional meeting transcripts.
  2. 02Write original transcripts with ambiguous and revised commitments, compare extraction methods and preserve uncertainty when an owner is unstated.
  3. 03Measure task precision, owner accuracy and invented-action rate against a stated baseline and analyse failure cases.

A suggested research approach

Write original transcripts with ambiguous and revised commitments, compare extraction methods and preserve uncertainty when an owner is unstated. Check dataset permissions and keep evaluation examples separate from model development. Record model versions, prompts or training settings, then compare task precision, owner accuracy and invented-action rate using repeatable runs and retain incorrect outputs for a transparent limitations discussion.

What you will need

  • Original fictional transcripts
  • Reviewed action annotations
  • Text extraction model

Keep your project scope clear

The prototype must not record real meetings without consent or invent commitments on participants' behalf. Generated project writing does not include a trained or deployed AI application.

Artificial Intelligence project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

  1. Chapter 1Introduction
  2. Chapter 2Literature Review
  3. Chapter 3System Analysis and Design
  4. Chapter 4System Implementation and Testing
  5. Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

Your title, department, research question and outline are ready. Add your institution, personalise the details and continue to your project workspace.

Generate the Complete Project Generation uses your word balance. Review the draft and supply your own verified research findings.