Learn.AI For AIKEN Education Growth

[GENERAL] Name and surname of main applicant

Peter Irase

[GENERAL] Are you delivering this project as an individual or as an entity (whether formally incorporated or not)

Individual

[GENERAL] Requested funds in ada

150000

[GENERAL] Please specify how many months you expect your project to last (from 2-12 months)

10

[GENERAL] Please indicate if your proposal has been auto-translated into English from another language

No

[GENERAL] Summarize your solution to the problem (200-character limit including spaces)

Learn.AI promotes active learning of concepts (blockchain, cryptocurrency, etc) in the Cardano ecosystem by engaging learners with relevant questions. This keeps learners focused, encouraged, etc.

[GENERAL] Does your project have any dependencies on other organizations, technical or otherwise?

No

[GENERAL] If YES, please describe what the dependency is and why you believe it is essential for your project’s delivery. If NO, please write “No dependencies.”

No dependencies

[GENERAL] Will your project’s output/s be fully open source?

Yes

[GENERAL] Please provide here more information on the open source status of your project outputs

The type of license I intend to use for open source is MIT License.

The elements that will be open source are:

1. Core AI API Code: The core algorithms and codebase that drive the AI-based learning prompts will be fully open-source.
2. API Integrations: Any custom APIs or connectors built for integration with LMS platforms will also be made open-source.
3. Documentation: User guides, API documentation, and developer tutorials will be included in the open-source repository to enable others to adopt and contribute to the project.

The project’s code will be made available throughout the lifecycle of the project on a platform such as GitHub or GitLab, with regular updates and contributions encouraged.

The team will maintain the repository, review contributions, and ensure documentation is kept up to date.

[METADATA] Horizons

AI

[SOLUTION] Please describe your proposed solution

I have learned blockchain on different platforms, the challenge I had was lack of focus and retention due to the new words I wasn't used to, this got me distracted and discouraged most times.

By delivering timely, context-based questions, Learn.AI encourages retrieval practice, a proven method to improve memory retention by making students recall what they've just learned.

Learn.AI will be able to bring learners together, because there will be reward/grade for learning.

Learn.AI drives the Cardano ecosystem growth through education and community building, onboard developers, businesses, and users into the Cardano ecosystem.

By addressing education, developer growth, and creating innovative reward systems using Cardano blockchain, Learn.ai could be a key player in driving adoption, innovation, and community engagement with the Cardano ecosystem.

Impact can be measured at the end of a learning session, where the AI will prompt students/participants to recall/summarize what they have learned and display results immediately. this method will measure if the student was active or not and then get rewarded or not.

Learn.AI provides real-time data and analytics, enabling instructors to monitor student progress effectively. It helps identify students who may be struggling with specific topics and allows for timely intervention.

By prompting students with interactive questions, the AI breaks up long learning sessions, encouraging active participation and maintaining attention over extended periods. Instructors can have the option to send a personalized question.

[IMPACT] Please define the positive impact your project will have on the wider Cardano community

The steadily increasing community of learners in the Cardano ecosystem will be increased by 99% through Learn.AI, because learners will be interested to learn on the Cardano blockchain because they will be rewarded while they learn. This can be adopted by institutions which will increase the use case of Cardano blockchain.

By providing a space for collaboration and knowledge-sharing, Learn.AI can contribute to the sense of community within Cardano, fostering an ecosystem where knowledge drives innovation. The AI can also ensure that developers stay up to date with evolving Cardano technologies and governance updates, ensuring that Cardano remains competitive in the blockchain space.

I will be able to measure impact quantitatively by:

1. Tracking the frequency and accuracy of learners responses to course-related prompts. METRICS: Number of questions answered, response accuracy rates, completion rates, and time spent engaging with prompts.
2. Pre- and post-assessment scores to measure knowledge retention and improvement over time. Metrics: Percentage improvement in test scores, reduction in knowledge gaps, and performance consistency.
3. Number of students or attendees actively using the API over a specific period. Metrics: Sign-up rates, daily active users (DAUs), monthly active users (MAUs), and retention rates.
4. Measure how many students complete the course successfully and how many achieve better outcomes as a result of the AI prompts. Metrics: Course completion rates, student pass rates, and overall satisfaction with learning outcomes.

In measuring impact qualitatively, I will:

1. Collect feedback through surveys and interviews on how useful students found the AI-prompted questions and their overall learning experience. Metrics: Survey scores on learning effectiveness, qualitative responses on how well students feel prepared after using the tool.
2. Gather input from educators on how well the API supports teaching efforts and whether they observe improvements in student understanding and participation. Metrics: Feedback forms, focus groups, and interviews.
3. Analyze student engagement patterns, such as when and how they respond to AI prompts and the type of content they interact with most. Metrics: Heatmaps of engagement times, question types, and levels of student engagement across different topics.

I will share the outputs and opportunities that result from this project

1. Data Analysis and Reports:

With Whom:

• Course instructors and educational institutions to help refine teaching strategies and improve student performance.
• Project stakeholders, developers, and researchers interested in the use of AI in education.
• Cardano community members who are interested in innovative tech for decentralized education.

How:

• Share data insights and analysis through periodic reports, webinars, and presentations to stakeholders and community members.
• Publish open-access reports and case studies showing the impact of the API on learning outcomes, adoption rates, and engagement metrics.

2. Knowledge Sharing:

With Whom:

• Broader education communities, both within and beyond the Cardano ecosystem, through open forums, conferences, and workshops.
• Open-source communities that can benefit from adopting or improving upon the API.

How:

• Publish detailed documentation, tutorials, and use-case studies on the API to inspire others in the Cardano community and beyond.
• Organize knowledge-sharing sessions and webinars with other developers and educators interested in leveraging AI for education.
• Provide GitHub repositories, templates, and instructional content on how to integrate the API with other Cardano-based projects or platforms.

3. Collaborative Networks and Partnerships:

With Whom:

• Cardano-based educational initiatives and blockchain developers who could further build on the API, integrating it with decentralized applications (dApps) or blockchain-powered platforms.

• How:
• Establish partnerships with universities and educational platforms to pilot and expand the use of the API, sharing resources and insights to refine the system.

Value to the Cardano Community

1. Innovation in Education:

This AI-driven project can showcase how Cardano's blockchain technology can

integrate with advanced AI solutions to drive innovation in education. It will demonstrate

Cardano’s commitment to enhancing learning experiences and accessibility through

technology.

2. Decentralized Education Solutions:

The API could be integrated with decentralized educational platforms built on

Cardano, providing transparent, scalable, and secure education tools that ensure fair

access and data integrity.

3. Economic and Skill Development:

By improving learning outcomes, the project can contribute to the upskilling of

individuals within the Cardano community, which could lead to broader adoption of the

blockchain platform as more individuals become competent in blockchain technology,

AI, and decentralized education systems.

Sharing Opportunities and Outputs

1. Open-Source Contributions:

• Release the API as an open-source tool within the Cardano community, enabling developers to improve upon or integrate it with other decentralized platforms.
• Provide access to educational institutions and organizations interested in using blockchain technology for learning, contributing to a global open education movement.

2. Community Engagement:

• Conduct community workshops, meetups, and hackathons to explore the use of AI in education and foster a culture of innovation within the Cardano ecosystem.
• Use Cardano’s governance structures (like Catalyst) to encourage collaborative funding for future educational AI projects.

[CAPABILITY & FEASIBILITY] What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?

Recently, I managed funds and developed the web presence of an NGO, by developing the website (www.bdeff.org), I created their socail media page and run ads for thier upcoming program.

My team understanding of natural language processing and machine learning is essential to build and refine the AI API for educational use. My team have experience in software development, data processing, and user interface design to integrate AI systems effectively. To ensure successful delivery, expertise in educational technologies or Learning Management Systems (LMS) aligns the AI API with course content. Strong project management using Agile or Scrum ensures timely execution, while knowledge of AI ethics and data privacy laws like GDPR ensures compliance. Effective communication with stakeholders guarantees transparency. Feasibility can be validated through prototype development, user feedback, market research, and financial viability analysis.

The following are steps that demonstrate trust in managing funds:

1. I will provide a detailed project budget that outlines how the funds will be allocated (e.g., development, testing, personnel, and marketing costs).
2. I will commit to regular financial reporting and progress updates. Use project management tools (such as Trello, Asana, or Jira) to share real-time progress with stakeholders or investors.
3. I will Implement financial controls to ensure proper use of funds. This includes having separate accounts for the project, tracking expenses against the budget, and conducting internal audits.

[PROJECT MILESTONES] What are the key milestones you need to achieve in order to complete your project successfully?

Milestone 1: Project Setup and Prototype Development

A. Milestone Outputs:

• Establishment of the project team, development environment, and initial design of the API.
• Build and deliver an MVP (Minimum Viable Product) to test basic features such as course-related prompts based on user behavior.

B. Acceptance Criteria:

• MVP functionality demonstrated to stakeholders, with successful delivery of basic AI-driven prompts.
• Team and tool setup completed.

C. Evidence of Milestone Completion:

• Screenshots or video walkthrough of the MVP.
• Internal reports and stakeholder feedback on MVP.

Milestone 2: User Testing and Feedback Collection

A. Milestone Outputs:

• Conduct testing with real users (educators/students) in a controlled environment.
• Gather feedback to improve the system based on real-world application.

B. Acceptance Criteria:

• Minimum of 10 testers interacting with the API.
• Documented feedback collected from users and analyzed.

C. Evidence of Milestone Completion:

• Survey results, usage analytics, and feedback reports.
• Iteration of the API based on user feedback.

Milestone 3: Market Research and Financial Viability Assessment

A. Milestone Outputs:

• Completion of a comprehensive market analysis to determine demand and identify competitors.
• Assess financial feasibility, including projected development costs, potential pricing, and monetization models.

B. Acceptance Criteria:

• Detailed market research report and cost-benefit analysis.

C. Evidence of Milestone Completion:

• Documentation of research findings, competitor analysis, and financial projections.

Final Milestone: Project Close-Out Report and Video

A. Milestone Outputs:

• Final version of the API, ready for full deployment.
• A detailed project report summarizing key achievements, challenges, financial breakdown, and future plans.
• A video demonstrating the functionality and success of the API.

B. Acceptance Criteria:

• Final version of the API meets all outlined objectives.
• Project report and video submitted to stakeholders and grant authorities.

C. Evidence of Milestone Completion:

• Delivery of the project report, video, and API.
• Feedback from stakeholders and users confirming the project’s success.

[RESOURCES] Who is in the project team and what are their roles?

Peter Irase

UI/UX Designer

https://www.linkedin.com/in/peterirase/

A dedicated UI/UX designer with a zeal for crafting digital experiences that resonate and captivate. My goal is to help make technology easier to understand and more fun to use.

Aisha Saliu

Project Manager

https://www.linkedin.com/in/aisha-salihu/

Aisha is a passionate and results-driven Project Manager with a proven track record in steering the success of software development projects. With over 5+ years in a customer service role and transitioning into project management, Aisha is skillful in project management, process improvement, customer satisfaction, client retention and team leadership.

Abdulfatahi Ishaq

Frontend Developer

https://www.linkedin.com/in/abfatahi/

Abdulfatahi is a professional and experienced software and web developer with a practical history of working in the information technology and services industry. He is skilled in Business Planning, Product Design & Development, and Management.

Peter Ewanfo

Backend Developer

https://www.linkedin.com/in/peter-ewanfo-2b398318a/

Peter is a lover of tech. Mobile engineer – Flutter, dev blogger, and occasionally writing APIs with Python – Flask

Adebayo Solomon

Blockchain Developer

https://www.linkedin.com/in/adebayo-solomon/

Adebayo is a blockchain developer with special interest in De-Fi and smart contract audit.

Osayi Akoko

DevOps Engineer

linkedin.com/in/osayiakoko

Sandra Adodo

AI/ML Engineer

https://www.linkedin.com/in/sandra-adodo/

Additional roles for future need:

Community Manager

Data Engineer

Quality Assurance (QA) Tester

Legal and Compliance Advisor

[BUDGET & COSTS] Please provide a cost breakdown of the proposed work and resources

Total Personnel Costs: 92,500 ADA

Total Third-Party Costs: 9,375 ADA

Total Marketing/Engagement Costs: 35,000 ADA

Total Documentation/Reporting Costs: 7,500 ADA

Total Miscellaneous Costs: 5,625 ADA

[VALUE FOR MONEY] How does the cost of the project represent value for money for the Cardano ecosystem?

The Learn.AI project offers significant value for money by directly addressing key needs within the Cardano ecosystem: developer education, community engagement, and innovation. Each cost has been carefully calculated to ensure that the investment made will result in measurable benefits for the ecosystem, driving adoption, and creating a robust developer and user base.

1. Personnel Costs (92,500 ADA)

Personnel costs, are crucial for ensuring the high-quality development and implementation of Learn.ai. These costs cover specialized roles such as AI/ML engineers, backend developers, and DevOps engineers, which are vital to the success of an AI-based learning system. The following points justify these expenses:

• High-Skilled Expertise: AI/ML engineers and backend developers command competitive rates in the global market. According to industry standards, the salaries for these roles can range from $80,000 to $150,000 per year, depending on location and skill level. Our budgeted personnel costs align with average freelance rates and full-time salaries, ensuring that we attract top talent without overextending resources.
• Long-Term Value: Investing in top-tier talent will result in a high-quality product that can scale efficiently, reducing future maintenance and operational costs.
• Supporting Evidence: The costs are consistent with the standard rates for AI and API development professionals in regions with strong tech ecosystems. By ensuring we pay competitive rates, we are securing a skilled workforce that will ensure the success of the project.

2. Third-Party Costs (9,375 ADA)

Third-party costs cover essential services such as cloud infrastructure, AI tool licenses, and external auditing.

• Cloud and AI Services: We have budgeted third-party costs to include services like AWS, Google Cloud, or Microsoft Azure, which are necessary for running and maintaining the AI models and API infrastructure. These services typically have usage-based pricing, and we have planned for scalable needs to ensure the infrastructure can grow with increased user activity.
• External Auditing and Compliance: Given the importance of ethical AI, a portion of third-party costs is allocated for audits to ensure the system adheres to Cardano's values of transparency and decentralization.

3. Marketing/Engagement Costs (35,000 ADA)

• Community Engagement: Cardano thrives on community-driven projects. A significant portion of this budget will go towards engaging developers and learners within the ecosystem. These activities will include workshops, webinars, and hackathons, all aimed at onboarding new users and developers into the ecosystem.
• Industry Rates: Marketing and engagement costs are based on industry rates for digital campaigns, event organization, and developer outreach.
• Value to the Ecosystem: By investing in community engagement, we ensure widespread adoption of Learn.ai, which benefits the entire Cardano ecosystem through increased activity and dApp development.

4. Documentation/Reporting Costs (7,500 ADA)

• Comprehensive Documentation: Learn.AI requires thorough documentation to ensure that developers, educators, and learners can easily use the platform. These costs cover technical writing, user guides, and API documentation, which are essential for adoption.
• Ongoing Updates: A portion of these costs is also reserved for continuous updates as the platform evolves, ensuring that the Cardano community always has access to the most relevant and accurate information.
• Industry Benchmarks: The cost aligns with typical freelance rates for technical writers, who charge $50 to $100 per hour depending on complexity.

5. Miscellaneous Costs (5,625 ADA)

• Contingency: Miscellaneous costs provide flexibility for unforeseen expenses, such as additional tools, support services, or minor adjustments in the project. This ensures the project remains on track even if unexpected challenges arise.
• Minimal Overhead: By keeping this allocation small (3.75% of the total budget), we ensure the majority of resources are focused on delivering high-impact outcomes for the ecosystem.

Justifying Costs as Proportional to Industry Standards:

• Competitive Industry Rates: We have benchmarked costs against current freelance rates and salaries in the AI and API development sectors. By ensuring our rates align with these benchmarks, we are making a fair investment in skilled labor, which is necessary for the project's success.

• Efficiency of Funds: The 150,000 ADA budget is carefully distributed to maximize the return on investment for the Cardano ecosystem, focusing on key areas like talent acquisition, infrastructure, and community engagement. Each expense directly contributes to the quality and scalability of Learn.AI, offering long-term benefits.

Conclusion:

The project’s costs represent excellent value for money by focusing on skilled labor, essential infrastructure, and community engagement. By ensuring we invest in the right areas—talent, marketing, and support—Learn.ai will deliver a high-quality product that benefits both the Cardano ecosystem and its broader community.