Individual Capstone Defense: Classroom Visual Attention Monitoring – Community Presentation

Creator prompt

The idea behind this presentation

# AI Prompt – Create a Professional PowerPoint Presentation for My Individual Capstone Defense I will provide you with my **individual capstone report**. Your task is to create a **professional PowerPoint presentation** based entirely on that report. ## Assessment Focus Keep the following assessment objective in mind throughout the presentation: > **"The intention of this assessment is to combine and evaluate the depth and breadth of the individual project report outlining the technical understanding, theoretical aspect, analysis and design of the final software solution."** The presentation should therefore demonstrate: * Technical understanding * Design decisions * Analysis and reasoning * Implementation knowledge * Ability to explain and justify my work Do **not** simply summarize the report. Instead, prepare slides suitable for an oral defense where I need to explain and defend my implementation. --- # Scope This is an **individual presentation**, not a group presentation. Only focus on **my individual contribution**, which consists of: * Video Preprocessing * Data Visualization * LLM-based AI Summary Implementation Do **not** spend time discussing modules developed by other team members except for briefly explaining how my work fits into the overall system. --- # Presentation Requirements Create approximately **10–12 slides** with a clean, modern, and professional design. Use: * Minimal text * Clear headings * High-quality diagrams where appropriate * Icons * Flowcharts * Tables * Screenshots (leave placeholders if necessary) Each slide should contain concise bullet points rather than paragraphs. --- # Suggested Slide Structure ## Slide 1 Title Slide * Project title * Name * Student ID * Supervisor * University --- ## Slide 2 Project Overview Briefly explain * project objective * problem statement * why the project is important Do not spend more than one slide here. --- ## Slide 3 My Individual Contribution Clearly identify my responsibilities. Include: * Video Preprocessing * Data Visualization * AI Summary using LLM Briefly explain where these fit within the overall system. --- ## Slide 4 Video Preprocessing Explain: * Why preprocessing is necessary * FPS reduction * Frame resizing * Processing workflow * Benefits of preprocessing Include a simple workflow diagram. --- ## Slide 5 Data Visualization Explain: * Why visualization is important * Charts generated * Dashboard * How processed data is converted into meaningful insights Use example chart placeholders where appropriate. --- ## Slide 6 LLM Implementation Explain: * Why AI-generated summaries were introduced * Ollama * Qwen 2.5 1.5B * Overall workflow * Input * Processing * Output Include a simple architecture or workflow diagram. Avoid unnecessary theory about LLMs. Focus on implementation. --- ## Slide 7 System Workflow Illustrate how my implemented modules interact. Example flow: Video Input ↓ Video Preprocessing ↓ Database ↓ Data Visualization ↓ LLM Summary ↓ Final Report Keep the workflow simple and easy to explain. --- ## Slide 8 Challenges Encountered Discuss realistic technical challenges, such as: * Optimizing video preprocessing * Selecting suitable FPS reduction * Handling inconsistent data * Integrating Ollama locally * Generating meaningful AI summaries * Visualization formatting * Debugging frontend integration For each challenge briefly explain: * Problem * Solution * Outcome --- ## Slide 9 Key Learning Outcomes Explain what I learned during the project. Include points such as: * Practical software engineering * Working with AI models * Data processing techniques * Visualization design * Problem-solving * Debugging * Integration of multiple technologies * Team collaboration and communication * Time management --- ## Slide 10 Future Improvements Suggest realistic improvements related only to my work. Possible examples: * Real-time processing * Improved preprocessing pipeline * Better visualization dashboards * Faster LLM inference * More advanced summarization * Cloud deployment * Performance optimization --- ## Slide 11 Conclusion Summarize: * My objectives * What was successfully achieved * Contribution to the overall project * Final takeaway Keep this concise. --- ## Slide 12 Questions Simple professional "Thank You / Questions?" slide. --- # Speaker Notes For every slide, provide detailed **speaker notes** explaining: * what I should say * how to transition to the next slide * important technical points the examiner may ask The notes should help me confidently deliver a 10–12 minute presentation. --- # Possible Viva Questions At the end, include **10–15 likely questions** my supervisor or examiner may ask specifically about my contribution. For each question, provide a concise model answer. Focus on questions related to: * Video preprocessing * FPS reduction * Image resizing * Data visualization * LLM implementation * Ollama * Qwen 2.5 * Design decisions * Challenges * Future improvements --- # Design Guidelines The slides should be: * Professional * Modern * Minimal * Consistent * Easy to read * Suitable for an academic project defense Avoid overcrowding slides with text. Use visuals wherever they improve understanding. The final presentation should demonstrate strong technical understanding, analytical thinking, design decisions, implementation knowledge, and confidence in defending my individual work.

Individual Capstone Defense: Classroom Visual Attention Monitoring – Community Presentation | Presenton