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Personalized Quiz: A dive into my hackathon GenAI project using PartyRock

Personalized Quiz: A dive into my hackathon GenAI project using PartyRock

Discover the creation of a multi-language quiz app with AWS PartyRock. See how generative AI revolutionizes learning and technology access for all.

Published Mar 11, 2024
Last Modified Mar 12, 2024

The Genesis of the Idea

AWS PartyRock serves as a playground for building AI-generated apps, providing a perfect blend of user interaction and AI-driven content creation. Inspired by PartyRock's guiding principles—speed, innovation, and fun—I set out to build an app that leverages these aspects to offer a unique quiz experience.
For a firsthand look at this innovative project, click here to access the app. Below are some snapshot tests demonstrating the app's capabilities and the engaging user experience it offers:

Quizify

Core Features of the Quiz App

The app is designed with simplicity and inclusivity in mind, enabling users to:
  1. Select their preferred language for the quiz, making the app accessible to a wider audience.
  2. Choose a specific topic for the quiz, catering to diverse interests and educational needs.
  3. Decide the number of questions they wish to answer, allowing for customizable quiz lengths.
Using these inputs, the app employs the LLM Powered ChatBot Widget, powered by the Claude LLM Model, to dynamically generate multiple-choice questions (MCQs). This approach ensures each quiz is tailored to the user's specifications, enhancing the learning and entertainment value.

Interactivity Through Conversational AI

A standout feature of the app is its interactive chatbot, which guides users through the quiz, checking their answers and providing feedback in real time. This chatbot, also powered by the Claude LLM Model, is context-aware, capable of evaluating performance, and offers constructive feedback in the user-selected language.
The chatbot operates under this guiding prompt:
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You are tasked with creating and managing a MCQs quiz on the topic of [Topic].
The quiz should consists of [Number of Questions] questions.
Start generating questions as soon as user enters start.

Here's how you'll proceed:
1. Present the quiz questions to the user one at a time, waiting for their response to each question before moving on to the next one.
User can response with actual answer or the option which contains actual answer.
3. Irrespective of language selected, the options for each question should always have english identifiers i.e., A,B,C and D.
4. Irrespective of language selected the question should start with a numerical prefix i.e., 1), 2), 3) etc.
5. Collect and store the user's answers as they progress through the quiz.
6. Once the user has answered the last question, evaluate their overall performance. For this evaluation, consider the following:
- Correct answers: Acknowledge and reinforce correct responses.
- Incorrect answers: Provide detailed explanations for why the answer was incorrect, and offer constructive feedback for improvement.
7. Based on their performance, identify areas where the user can improve and suggest resources or practices to help them enhance their understanding of [Topic].
8. Throughout the interaction, communicate in [Language], ensuring the conversation is engaging, supportive, and easy to understand.
Your goal is to make this learning experience informative and positive, helping the user to not only test their knowledge but also learn from their mistakes
and improve in the topic of [Topic] .
This is how ChatBot Widget is configured:
ChatBot Widget Configuration with Claude Model
Here is an example snapshot of how this is helpful in interactively learning through conversation in the language of users choice:
Conversation and Evaluation in Hindi

Leveraging PartyRock's Widgets

The Widgets are the building blocks of app created using PartyRock. Below are the five widgets available**:**
1) User-Input Widget for gathering user preferences.
2) Static Text Widget for showing some static text in UI.
3) AI-powered Text Generation Widget for generating text based on user prompt and user input from other widgets.
4) AI-powered Image Generation Widget for generating images using Imaging Generation models availabe as part of bedrock.
5) AI-powered ChatBot Widget for creating chatbots.
PartyRock Widgets
The project demonstrates how these widgets can be interconnected to create a seamless, dynamic user experience.

Community Impact

The unique features of my hackathon project have the potential to make a significant impact on various communities, particularly in educational and language learning sectors. By allowing users to create quizzes in their preferred language on any topic, the app fosters an inclusive environment where learning is not bound by language barriers. This can be especially beneficial for non-native English speakers or communities with limited access to educational resources in their local languages.

Envisioned Real-World Application

In educational settings, teachers can use the app to create custom quizzes in the language that best suits each class or student, making learning more accessible and personalized. For language learners, the app provides a fun, interactive way to test their skills in their target language, with immediate feedback to aid in their progression.

Encouraging Adoption

To encourage adoption among target communities, I plan to:
  • Partner with educational institutions: Collaborating with schools and language learning centers to integrate the app into their teaching methodologies.
  • Community Workshops: Hosting workshops to demonstrate the app's capabilities and benefits, encouraging community-driven content creation.

Alternative Development Scenario with Amazon Bedrock

Had AWS PartyRock not been available, developing this application with Amazon Bedrock would involve a more hands-on approach to selecting and integrating foundation models (FMs) for generative AI capabilities.

Architectural Considerations

  • Model Selection: With Bedrock's access to a range of leading FMs from companies like AI21 Labs, Anthropic, and Cohere, I would evaluate and select models best suited for text generation and conversational AI, ensuring they are capable of supporting the required languages and content complexity.
  • Customization and Fine-Tuning: Leveraging Bedrock's customization features, I would fine-tune selected models using relevant datasets to enhance the app's accuracy and user experience, particularly for generating quizzes and interpreting answers in various languages.
  • Integration of Additional Tools: Incorporating AWS Lambda for serverless execution of app logic, Amazon API Gateway for RESTful service creation, and Amazon DynamoDB for storing quiz and user data would ensure scalability and manageability.

Model Integration

  • Interactive Chatbot: Employing a model capable of thoughtful dialogue, like Claude or Amazon's Titan, customized for conversational responsiveness in multiple languages, generating quizzes, fine-tuned for educational content creation and question answering.

Democratizing GenAI with AWS PartyRock

AWS PartyRock revolutionizes the way we think about and engage with generative AI technologies. By providing a simplified, intuitive platform, PartyRock removes the technological barriers that have traditionally restricted GenAI application development to those with specialized skills. This accessibility opens up a world of possibilities for individuals across various professions and backgrounds, making it a catalyst for innovation in countless fields.

Simplifying Complex GenAI App Development

One of the standout features of PartyRock is its ability to streamline the creation of complex GenAI-powered applications. With PartyRock, what once required hours of coding and a deep understanding of AI models can now be accomplished in minutes. This ease-of-use is illustrated in my hackathon project, where a multifaceted quiz app was developed swiftly, integrating advanced features like multi-language support and interactive AI without the need for extensive coding or AI expertise.

Bridging the Gap for Non-Tech Professionals

PartyRock's user-friendly interface and widget-based app building process make it an invaluable tool for non-tech professionals. Whether you're an educator looking to create personalized learning tools, a marketer exploring innovative ways to engage audiences, or a creative writer seeking inspiration, PartyRock provides the means to experiment with and harness the power of GenAI without the steep learning curve typically associated with AI technologies.

From Ideation to Production with AWS PartyRock and Amazon Bedrock

PartyRock not only serves as an excellent platform for rapid prototyping and ideation around GenAI use cases but also as a stepping stone to more complex, scalable application development using Amazon Bedrock. Once a concept is validated through a PartyRock prototype, developers can transition to Amazon Bedrock to leverage its extensive range of foundation models, customization options, and integration capabilities for production-level applications. This seamless progression from concept to production empowers creators to bring their GenAI applications to life with confidence and efficiency.

Conclusion

AWS PartyRock is more than just a tool; it's a gateway to the future of generative AI application development. By democratizing access to GenAI, PartyRock invites a diverse array of voices and ideas into the conversation, enriching the ecosystem with innovative solutions that were previously unimaginable. As my hackathon project demonstrates, PartyRock not only simplifies the development process but also inspires creativity, making it an essential platform for anyone looking to explore the possibilities of GenAI.