AI Assistant For Test Assignment

 

Photo by Google DeepMind

Creating an AI application to assist school teachers with testing assignments and result analysis can greatly benefit teachers and students. Here's an overview of why such an application would be beneficial and how it can be developed cost-effectively:

Grading assignments for all students is time-consuming for teachers. AI can automate this process for certain types of assessments, freeing up teachers' time for more interactive learning experiences.

Let's see how it can help our teachers.

1. Teacher Workload: Primary school teachers often have a heavy workload, including preparing and grading assignments for multiple subjects and students. Automating some of these tasks can significantly reduce their workload.

2. Personalized Learning: AI-based applications can provide personalized feedback to students, helping them understand their strengths and weaknesses, leading to more effective learning outcomes.

3. Efficiency: By automating tasks like grading and analysis, teachers can focus more on teaching and providing individualized support to students.

Key Features of the Application:

1. Assignment Creation: Teachers can create assignments for various subjects easily within the application, including multiple-choice questions, short-answer questions, and essay-type questions.

2. OCR Integration: Integration with Azure OCR services allows teachers to scan and digitize handwritten test papers quickly, saving time and effort.

3. AI-Powered Grading: Utilize OpenAI's ChatGPT for grading essay-type questions and providing feedback. Implement algorithms for grading multiple-choice and short-answer questions.

4. Result Analysis: Generate detailed reports and analytics on student performance, including overall scores, subject-wise performance, and areas of improvement.

5. Personalized Feedback: Provide personalized feedback to students based on their performance, highlighting strengths and areas for improvement.

6. Accessibility: Ensure the application is user-friendly and accessible to teachers with varying levels of technical expertise.

Development Approach:

1. Prototype Development: Start with a small-scale prototype to validate the concept and gather feedback from teachers and students.

2. Iterative Development: Adopt an iterative development approach, gradually adding features and refining the application based on user feedback.

3. Cloud-Based Architecture: Utilize cloud-based services for scalability and cost-effectiveness. For example, deploy the application on platforms like Azure or AWS, leveraging serverless computing and managed services.

4. Open Source Libraries: Utilize open-source libraries and frameworks to minimize development costs and accelerate development, such as Flask for the backend, React for the frontend, and TensorFlow for machine learning tasks.

5. Data Security and Privacy: Ensure compliance with data security and privacy regulations, especially when handling student data. Implement encryption, access controls, and data anonymization techniques as needed.

6. User Training and Support: Provide comprehensive user training and ongoing support to teachers to ensure they can effectively use the application.

By following these guidelines, you can develop a cost-effective AI application that enhances the teaching and learning experience for primary school teachers and students.

Here is a Python script to find out how much it costs to use the OpenAI tool for the application above.

def calculate_cost(params):

    """

    Calculates the cost for using ChatGPT for a dynamic assignment application in a school.

    Parameters:

    params (dict): A dictionary containing parameters for the cost calculation.

    Returns:

    float: The total cost of the assignment application.

    Explanation:

    - Extract parameters from the input dictionary.

    - Calculate the number of tokens based on the number of words (assuming 750 words per 1000 tokens).

    - Define costs for different models, fine-tuning, and embedding.

    - Determine the model to be used, considering fine-tuning and embedding.

    - Calculate the cost based on the chosen model, fine-tuning, embedding, number of students, and assignment subjects.

    - Return the total cost.

    """

    words = params["words"]

    tokens = words * 1.25  # Assuming 750 words per 1000 tokens

    model = params["model"]  # Which model to use

    fine_tuning = params["fine_tuning"]  # Fine-tuning required or not

    embed_model = params["embed_model"]  # For embedding model

    students = params["students"]

    assignment_sub_count = params["assignment_sub_count"]

    # Costs for different models

    models = {

        "gpt4": {"8k": 0.03, "32k": 0.06},

        "chatgpt": {"8k": 0.002, "32k": 0.002},

        "instructgpt": {

            "8k": {"ada": 0.0004, "babbage": 0.0005, "curie": 0.0020, "davinci": 0.0200},

            "32k": {"ada": 0.0004, "babbage": 0.0005, "curie": 0.0020, "davinci": 0.0200},

        },

    }

    # Fine-tuning costs

    fine_tuning_cost = {

        "ada": {"training": 0.0004, "usage": 0.0016},

        "babbage": {"training": 0.0006, "usage": 0.0024},

        "curie": {"training": 0.0030, "usage": 0.0120},

        "davinci": {"training": 0.0300, "usage": 0.120},

    }

    # Embedding model costs

    embedding_model = {"ada": 0.0004, "babbage": 0.005, "curie": 0.020, "davinci": 0.20}

    total_cost = 0.0

    instructgpt_models = ["ada", "babbage", "curie", "davinci"]

    if model in instructgpt_models:

        sub_model = model

        model = "instructgpt"

    if model == "instructgpt":

        if tokens > 32000:

            price_model = models[model]["32k"].get(sub_model, {})

        else:

            price_model = models[model]["8k"].get(sub_model, {})

    else:

        if tokens > 32000:

            price_model = models[model]["32k"]

        else:

            price_model = models[model]["8k"]

    if fine_tuning:

        total_cost += (tokens * fine_tuning_cost[sub_model]["training"]) + (

            tokens * fine_tuning_cost[sub_model]["usage"]

        )

    if embed_model:

        total_cost += tokens * embedding_model[sub_model]

    total_cost += price_model * students * assignment_sub_count

    return total_cost

params = {

    "words": 10000,

    "model": "ada",

    "fine_tuning": True,

    "embed_model": "ada",

    "students": 200,

    "assignment_sub_count": 8,

}

print(params)

cost = calculate_cost(params)

print(

    f"The total cost of using ChatGPT for an assignment application with {params['students']} students and {params['assignment_sub_count']} subjects is: ${cost:.2f}"

)

 

Some useful links from Azure

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/quickstarts-sdk/client-library?tabs=linux%2Cvisual-studio&pivots=programming-language-python

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40?tabs=visual-studio%2Clinux&pivots=programming-language-python

https://microsoft.github.io/PartnerResources/skilling/ai-ml-academy/openai

https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence

Improve ChatBot Performance

Improving the performance of your chatbot involves several steps. Let’s address this issue:

1. Latency Diagnosis:

   • Begin by diagnosing the causes of latency in your chatbot application.
   • Use tools like LangSmith to analyze and understand where delays occur.

2. Identify Bottlenecks:

   • Check if any specific components are causing delays:
     • Language Models (LLMs): Are they taking too long to respond?
     • Retrievers: Are they retrieving historical messages efficiently?
     • Memory Stores: Is memory retrieval slowing down the process?

3. Streamline Prompt Engineering:

   • Optimize your prompts:
     • Contextual Information: Include only relevant context in prompts.
     • Prompt Length: Avoid overly long prompts that increase LLM response time.
     • Retriever Queries: Optimize queries to vector databases.

4. Memory Store Optimization:

   • If you’re using a memory store (e.g., Zep), consider:
     • Caching: Cache frequently accessed data.
     • Indexing: Optimize data retrieval using efficient indexing.
     • Memory Size: Ensure your memory store has sufficient capacity.

5. Parallel Processing:

   • Parallelize tasks wherever possible:
     • Retriever Queries: Execute retriever queries concurrently.
     • LLM Requests: Send multiple requests in parallel.

6. Model Selection:

   • Consider using GPT-4 for improved performance.
   • Evaluate trade-offs between model size and response time.

7. Feedback Loop:

• Continuously monitor and collect user feedback.
• Iterate on improvements based on real-world usage.

General consideration for improvement of chatbot performance are following.

Here are some additional things you can consider:

Infrastructure Optimization:

• Virtual Machine (VM) Selection: Choose an appropriate VM size with sufficient CPU, memory, and network bandwidth for your chatbot's workload. Azure offers various VM options, so explore what best suits your needs.
• Resource Scaling: Implement autoscaling to automatically adjust resources based on real-time traffic. This ensures your chatbot has enough resources during peak usage and avoids unnecessary costs during low traffic periods.

Code Optimization:

• Profiling: Use profiling tools to identify areas in your chatbot code that are slow or resource-intensive. This helps you pinpoint specific functions or algorithms that need improvement.
• Caching Mechanisms: Implement caching for frequently used data or responses within your chatbot code. This can significantly reduce processing time for repeated user queries.
• Asynchronous Operations: If possible, make use of asynchronous operations for tasks that don't require immediate results. This prevents your chatbot from getting blocked while waiting for data from external sources.

Monitoring and Logging:

• Application Insights: Utilize Azure Application Insights to monitor your chatbot's performance metrics like latency, memory usage, and error rates. This helps identify performance issues and track the effectiveness of your optimization efforts.
• Logging: Implement detailed logging in your chatbot code to track user interactions and identify potential bottlenecks. This information can be invaluable for troubleshooting performance problems.

Additional Considerations:

• Data Preprocessing: Preprocess your training data to improve the efficiency of your language model. This can involve techniques like data cleaning, normalization, and tokenization.
• Compression: Consider compressing large data files used by your chatbot to reduce storage requirements and improve retrieval speed.
• Network Optimization: Ensure a stable and high-bandwidth network connection for your chatbot deployment. This minimizes delays caused by network latency.

If you are using Azure Function based serverless architecture then the following may help you. 

Leveraging Serverless Benefits:

• Cold Start Optimization: Since serverless functions spin up on-demand, there can be an initial latency for the first invocation (cold start). Consider techniques like pre-warming functions to minimize this impact.
• Scaling Configuration: Azure Functions automatically scales based on traffic. However, you can fine-tune the scaling settings to ensure your functions have enough resources during peak loads.
• Function Chaining: Break down complex chatbot functionalities into smaller serverless functions. This allows for better parallelization and potentially faster execution.

Azure Function Specific Optimizations:

• Durable Functions (if applicable): If your chatbot involves state management or workflows, leverage Azure Durable Functions to manage state efficiently without impacting performance.
• Trigger Selection: Choose the most efficient trigger for your chatbot interactions. For example, HTTP triggers might be suitable for user messages, while timer triggers can be used for background tasks.
• Integration with Azure Services: Utilize other Azure services tightly integrated with Functions. For instance, store chatbot data in Azure Cosmos DB for fast retrieval or use Azure Cognitive Services for specific tasks like sentiment analysis, offloading work from your functions.

Remember:

• Monitoring and Logging: As mentioned earlier, monitoring with Azure Application Insights and detailed logging within your functions are crucial for serverless performance optimization.
• Cost Optimization: While serverless offers pay-per-use benefits, monitor function execution times and resource consumption to identify any inefficiencies that might inflate costs.

By combining the previous recommendations with these serverless-specific pointers, you can significantly enhance your chatbot's performance within your Azure Function architecture.

Yes, you can potentially use WebSockets instead of a REST API for your chatbot communication between the front-end (user interface) and the server-side (Azure Functions) in your scenario. Here's a breakdown of the pros and cons to help you decide:

WebSockets for Chatbots:

• Pros:
  • Real-time communication: Ideal for chatbots where responses need to be delivered instantly, creating a more interactive experience.
  • Bi-directional communication: Enables the server to push updates to the client without waiting for requests, keeping the conversation flowing.
  • Reduced overhead: Compared to REST APIs with frequent requests and responses, WebSockets can reduce network traffic and improve performance.
• Cons:
  • Increased server complexity: Managing WebSocket connections on the server side requires additional code and potentially more resources.
  • Limited browser support: While most modern browsers support WebSockets, older ones might require workarounds.
  • Connection management: You'll need to handle connection establishment, maintenance, and disconnection in your code.

REST APIs for Chatbots:

• Pros:
  • Simpler implementation: REST APIs are a well-established standard with readily available libraries and frameworks, making development easier.
  • Wider browser support: Works with a broader range of browsers, ensuring wider user compatibility.
  • Scalability: REST APIs typically handle high traffic volumes well due to their stateless nature.
• Cons:
  • Higher latency: Communication happens through request-response cycles, potentially leading to slower response times compared to WebSockets.
  • More network traffic: Frequent requests and responses can increase network overhead compared to a persistent WebSocket connection.

Considering your Serverless Architecture:

Since you're using Azure Functions, WebSockets might introduce some additional complexity for managing connections within the serverless environment. However, the potential benefits for real-time communication and reduced overhead in a chatbot scenario can be significant.

Here are some additional factors to consider:

• Complexity of your chatbot: For simpler chatbots with less emphasis on real-time interaction, a REST API might suffice.
• Traffic volume: If you anticipate high user traffic, REST APIs might be more scalable for your serverless architecture.
• User experience: If real-time responsiveness is crucial for your chatbot's functionality, WebSockets can significantly enhance user experience.

Recommendation:

• Evaluate your chatbot's specific needs and prioritize real-time interaction if necessary.
• If real-time is a priority and you're comfortable with managing connections in a serverless environment, WebSockets can be a good option.
• For simpler chatbots or those requiring broader browser support, a REST API might be a suitable choice.

Ultimately, the decision depends on your specific requirements and priorities. You can even explore hybrid approaches where a combination of REST APIs and WebSockets might be beneficial.