Product Development & Testing: VoizAI ACLA Learning Platform

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Project Overview:

This project involves developing and testing the MVP (Minimum Viable Product) for VoizAI ACLA (Anyone Can Learn Anything), an early-stage agentic education platform concept. Students will play a crucial role in shaping the product from ideation through initial development, conducting foundational testing, prototype development, and feature validation while gaining hands-on experience in educational AI product development and user-centered learning solutions.

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Project Key Milestones:

1. Weeks 1–2: 

Product ideation and conceptualization phase. Research target user needs across diverse learning topics, brainstorm core platform features, and define the MVP scope for the ACLA learning platform.

2. Weeks 3–4: 

Conduct initial platform audit and foundational testing. Evaluate existing prototype functionality, identify technical requirements, and develop comprehensive testing protocols for the MVP.

3. Weeks 5–6: 

Design and implement A/B testing framework for different learning agents across diverse subject areas (history, finance, cooking, childcare, etc.). Develop testing methodologies and begin systematic bug detection and documentation processes.

4. Week 6: 

Build and deploy your own custom learning agents tailored to specific educational topics and learning styles. Collect performance data, analyze learning agent effectiveness, and begin development of innovative platform features based on testing insights.

5. Week 7: 

Showcase your custom learning creations and present testing results through interactive demonstrations. Collaborate on advanced feature recommendations and platform optimization strategies with direct input into product roadmap decisions.

6. Week 8: Finalize feature development, compile comprehensive testing report, and create roadmap for continued platform evolution and enhanced user- driven learning agent capabilities.

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Expected Deliverables:

-Comprehensive platform testing report with bug documentation and resolution recommendations

-A/B testing analysis comparing learning agent performance across different subject areas and user engagement metrics

-2-3 newly developed custom learning agents with documentation and implementation guides for diverse topics

-Feature development specifications and prototypes for platform enhancement focusing on user-driven learning experiences

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