✏️ AI Automated Course Notes

What if you could reduce over 280 hours of manual work to just 15 minutes—all for only $0.38? A company leveraged AI to revolutionize its course note generation process, cutting down the time from 280+ hours manually to 45 hours with partial AI assistance, and finally to just 15 minutes with full automation, drastically improving efficiency and reducing costs.

Background

A company offering educational courses wanted to streamline the process of creating comprehensive notes for each topic covered in their courses. The company had all the course information stored in CSV files, with each file representing a different subject and containing details of various classes within that subject. The manual process of generating notes was time-consuming and labor-intensive, requiring approximately 45 minutes per subject per person (with AI assistance). To enhance efficiency, the company decided to automate this process using Python and OpenAI's API.

Objectives

The primary objectives of this project were:

Organize Data Efficiently: Automatically categorize and arrange CSV files into corresponding directories.
Clean and Prepare Data: Ensure that the data is clean and ready for processing by filtering and organizing it by category.
Generate Comprehensive Notes: Use AI to create concise and informative notes for each class in every subject.
Optimize Processing Time: Reduce the time required to generate notes from several hours to a few minutes.

Solution Overview

The solution involved creating a Python script that could process the CSV files, clean and organize the data, and make API calls to OpenAI to generate descriptive summaries for each class. The following steps outline the process:

1. CSV File Organization

The script scans the current working directory to identify all CSV files.
For each CSV file found, the script creates a new folder named after the subject (derived from the file name).
The corresponding CSV file is then moved to its newly created directory. This organization ensures that all files are neatly categorized by subject.

2. Data Cleaning and Preparation

Inside each CSV, the script cleans the data by focusing on the 'Category' column, which represents different classes within the subject.
It fills any missing values (NaN) and trims unnecessary whitespace to standardize the data.
The script then segments the CSV content into distinct sections based on the unique categories present.

3. Handling Special Cases

Special handling is implemented for 'Introduction' and 'Conclusion' sections. Due to their brevity, these sections are merged with the next or previous sections, respectively, to create more substantial content for note generation.

4. Integration with OpenAI API

For each section identified, the script sends a request to the OpenAI API with a custom prompt tailored to generate a descriptive summary for that section.
The API response, which contains the generated notes, is captured and stored.

5. Generating and Storing Summaries

The generated summaries are saved as Markdown (.md) files, ensuring easy readability and access. Each Markdown file is named according to the category it represents and is stored in the corresponding subject's directory.
The script sanitizes filenames to ensure compatibility across different operating systems, removing any invalid characters.

6. Logging and Transparency

Throughout the process, the script logs all significant actions, such as file moves, section findings, and API responses. This logging provides transparency and helps in debugging or reviewing the script's operations.

Results

The automated process significantly reduced the time and effort required to generate course notes, achieving impressive results:

Efficiency Gains

Time Savings: Initially, generating notes manually took over 280 hours for all 60 subjects. By incorporating AI assistance, this time was reduced to 45 hours. With full automation, the process now takes only 15 minutes for all subjects. This drastic reduction demonstrates a significant improvement in operational efficiency, allowing the company to complete the task nearly 1,100 times faster than the manual process.
Resource Optimization: The automation allowed the company to reallocate human resources to more strategic tasks, such as course content development and improvement, rather than repetitive note-taking activities. This optimization maximized the productivity of employees and reduced burnout from mundane tasks.

Consistency and Quality

Standardized Output: By using AI to generate notes, the company ensured that all notes were consistently formatted and of uniform quality, regardless of the subject matter. This consistency is crucial for maintaining a professional standard across all courses.
Enhanced Content Quality: The AI-generated notes were not only concise but also well-structured and informative, enhancing the learning experience for students. The AI's ability to distill complex information into easily digestible summaries improved the overall quality of the course material.

Scalability and Flexibility

Scalable Solution: The automated system is highly scalable, capable of handling an increasing number of subjects and classes without a proportional increase in time or resources. This scalability is particularly beneficial for expanding course offerings or updating existing content.
Flexible Integration: The system’s design allows for easy integration of new subjects or modifications to existing content without requiring significant changes to the script. This flexibility makes it easier for the company to adapt to evolving educational needs and trends.

User Benefits (Students)

Improved Learning Materials: Students benefit from high-quality, concise notes that are easy to read and understand. The summaries generated by AI provide a clear and focused overview of each class, helping students quickly grasp the core concepts.
Enhanced Study Efficiency: With better-organized and more informative notes, students can study more efficiently, spending less time deciphering complex information and more time understanding and retaining key concepts. This efficiency leads to better learning outcomes and higher satisfaction with the course content.
Accessible and Organized Content: The markdown format of the generated notes ensures that they are easily accessible and well-organized, making it simpler for students to find and review the material they need. This accessibility is particularly useful for revision and exam preparation.

Stakeholder Benefits (Instructors and Content Developers)

Reduced Workload: Instructors and content developers no longer need to spend time creating and organizing notes manually, allowing them to focus on developing new courses, improving existing ones, and engaging more directly with students.
Quality Assurance: Automated note generation provides a consistent and high-quality output, reducing the need for constant revisions and quality checks. This consistency ensures that all students receive the same high standard of educational material.
Focus on Core Competencies: By automating the repetitive aspects of content creation, stakeholders can dedicate more time to refining the core competencies of the educational programs, such as innovative teaching methods and interactive learning experiences.

Company Benefits

Cost Reduction: The automation significantly reduced the operational costs associated with manual note creation. The cost savings from reduced labor hours and minimized errors directly contribute to the company's bottom line.
Cost Efficiency: The total API cost for automating note generation for over 60 courses was just $0.38 USD. This minimal expense demonstrates the cost-effectiveness of using AI-driven solutions, especially when compared to the manual labor costs previously incurred. The low operational cost allows the company to allocate its budget more efficiently while still delivering high-quality content.
Competitive Advantage: By efficiently producing high-quality educational content, the company strengthens its position in the competitive educational market. The ability to quickly adapt and expand course offerings enhances the company's reputation and attracts more students.
Data-Driven Decision Making: The automated system provides detailed logs and data on content generation, enabling the company to make data-driven decisions regarding curriculum development, content updates, and student needs.

Conclusion

The implementation of an automated process for generating course notes using artificial intelligence has brought numerous benefits to the company, its employees, and its students. By streamlining operations, improving content quality, and enhancing the learning experience, the company has set a strong foundation for future growth and success in the educational sector. This case study illustrates the powerful impact that AI and automation can have in transforming traditional business processes, driving innovation, and delivering exceptional value to all stakeholders involved.

Future Improvements

Dynamic Prompt Customization: Implementing a more sophisticated method for dynamically customizing prompts based on the content of each class could improve the quality of the AI-generated summaries.
Error Handling and Retries: Enhancing the script to handle API errors more gracefully and implement retries could further improve the robustness of the automation process.
User Interface: Developing a user-friendly interface could make the script more accessible to non-technical users within the company.

Access the Python Code

To explore the Python script used for automating the course notes generation process, you can find the complete code on GitHub: Find Python Code Here.

This repository contains all the necessary scripts, instructions, and documentation to help you set up and run the automation on your own CSV files.

Disclaimer: This case study has been modified to ensure client confidentiality. All names, brand elements, and other identifiable information have been altered. Permission has been obtained to share the details, and these changes ensure the protection of the client's privacy. Any similarity to real brands or events is purely coincidental and unintended.