Explainable
Explainable bridges the AI Literacy gap by offering a hands-on, visual learning experience for learners, builders, and curious minds seeking to explore generative AI with greater clarity.
Client
Personal Project | Renaiya
Season
Summer 2025
Timeline
2 weeks | ongoing
project overview
project context
Explainable is an interactive educational Kaggle notebook designed to demystify generative AI capabilities and behaviors by presenting technical concepts as engaging, modular explanations. Its goal is to empower users to build trust in AI through understanding, rather than blind adoption.
This project was developed as the capstone submission for the Gen AI Intensive Course with Google, held from March 31 to April 4, 2025. The original Kaggle notebook was completed over two weeks, from April 4 to April 20, 2025. Explainable is now evolving into a web-based learning platform beyond the Kaggle environment.
Explainable breaks down foundational generative AI concepts into clear, digestible modules. It covers core capabilities like prompting, structured output, embeddings, retrieval-augmented generation (RAG), and GenAI evaluation. Each section includes interactive examples and visual explanations designed to make complex AI behaviors approachable for learners, builders, and curious minds alike.
team + role
I served as the sole designer and developer, supported by AI collaboration tools including Gemini, GPT, and Claude. These AI assistants helped me brainstorm ideas and troubleshoot technical challenges during development and integration phases.
This project also drew extensively on published studies, reports, whitepapers, and open-source tools. Special thanks to the Google course team and contributors of these invaluable resources. A full list of citations can be found here: <link>.
challenges
Complexity Barrier: Generative AI concepts are often steeped in jargon and technical detail, making them inaccessible to non-experts.
Trust Deficit: Users tend to either blindly trust AI or reject it due to misunderstanding how it works and its limitations.
Lack of Interactive Learning Tools: Few resources offer hands-on, modular, and visual approaches to learning AI concepts that adapt to different learning styles.
goals
Simplify & Clarify: Break down core GenAI capabilities into approachable, bite-sized modules that anyone can grasp.
Build Trust Through Understanding: Empower users to critically engage with AI by making its inner workings transparent and comprehensible.
Create Interactive Experiences: Develop engaging demos and examples that encourage exploration and experimentation rather than passive consumption.
outcomes
Modular Educational Toolkit: Delivered an interactive Kaggle notebook covering essential GenAI topics—prompting, embeddings, RAG, structured output, and evaluation.
Positive User Engagement: Early feedback (informal testing and peer review) highlighted increased confidence and clarity among learners.
Foundation for Growth: Established a scalable framework that is now evolving into a web-based learning platform to reach a broader audience.
discovery highlights
Understanding Scope
This project focuses on creating an accessible, hands-on educational tool to help non-technical users grasp core generative AI concepts. While it meets the requirements of the Google Gen AI Intensive Course Capstone, it intentionally limits scope to foundational capabilities and practical learning experiences, leaving out advanced technical content and production-level features. The project is evolving toward a more interactive web-based platform to broaden its impact and accessibility.
in the scope
competition requirements
Build a publicly viewable, fully functional Kaggle notebook
Demonstrate at least three generative AI capabilities
Provide clear documentation explaining the use case and AI implementations
audience requirements
Design for non-technical users seeking approachable GenAI education
Include interactive, modular demos to build foundational understanding
out of scope
resource constraints
Limited time and tooling restricted development to Kaggle notebook environment
No extensive user testing or video content included within the competition timeframe
not aligned
Not intended to replace formal AI education or serve advanced learners seeking deep technical mastery.
Does not provide exhaustive technical documentation or in-depth exploration of advanced GenAI capabilities.
Unsuitable for developers needing production-ready AI tools, real-time deployment, or MLOps workflows.
looking ahead
in progress
Expanding to a web-based learning platform to improve accessibility and interactivity
Enhancing UI/UX design to better serve non-technical audiences
future thoughts
Incorporate broader AI literacy topics and additional capabilities
Add personalized learning pathways and community engagement features
Generative AI has experienced explosive growth, rapidly embedding itself into healthcare, education, media, retail, and daily life. Despite this ubiquity, public understanding of how AI works, its capabilities, limitations, and risks lags significantly behind adoption rates. Understanding the context and landscape around the problem I intended to address verified my assumptions and illustrated value.
40%
of U.S. adults used generative AI in 2024, but only 30% could correctly identify six basic AI examples (Pew Research, 2023).
86%
of people say AI outputs need to be more transparent for them to trust the results (Keragon, 2025).
71%
of organizations report regular use of GenAI in at least one business function as of early 2025; up from 65% in early 2024 (McKinsey & Company, 2025).
genai literacy
Awareness Levels: A 2025 Pew Research Center study indicates that while awareness of AI is growing, understanding remains limited among the general public .
Trust and Adoption: Understanding AI processes enhances trust. Users are more likely to trust AI outputs when they comprehend how data is collected and processed d and processed . SAGE Journals
Equity and Inclusion: Addressing the AI literacy gap is crucial to prevent exacerbating existing inequalities, as underrepresented groups may be disproportionately affected by AI-driven changes . UNESCO
existing solutions
Current Resources: While some resources aim to demystify AI, there is a scarcity of materials specifically designed for non-technical audiences that combine plain language explanations with interactive learning .
Elements of AI: This free online course (a collaboration between the University of Helsinki and Reaktor, launched in 2018) was one of the pioneers in plain-language AI education. It requires no programming and uses everyday metaphors to explain AI concepts. It has seen massive uptake – over 1,000,000 people from 170+ countries have taken the Elements of AI course to learn the basics fcai.fi.
outlining opportunities
Opportunities for Development: Address material and resource gap for non-technical audiences gap by providing accessible, interactive content tailored to the general public, thereby fostering greater understanding and trust in GenAI technologies.
Informed Decision-Making and Participation: As AI influences everything from what news we see to what medical treatments we’re offered, people need knowledge to make informed choices. Public awareness is “a first step toward engagement in debates about AI’s appropriate role and boundaries”, notes Pew pewresearch.org.
understanding
our audience
Explainable is designed to meet the needs of diverse users interested in generative AI, whether they are just starting to build foundational knowledge, exploring AI behavior without technical jargon, or simply curious about what AI can and cannot do. The platform guides learners through key concepts using interactive demos and clear explanations, encouraging hands-on experimentation and critical reflection.
process highlights
feature highlights
status + reflection
project status
Explainable’s initial version—a fully interactive Kaggle notebook—was completed within a two-week capstone project timeframe. The notebook successfully demonstrates core generative AI concepts through modular, hands-on examples.
Currently, the project is transitioning from a notebook format to a dedicated web-based learning platform. This ongoing development phase focuses on enhancing user experience, accessibility, and scalability to reach a wider and more diverse audience.
Future plans include incorporating user feedback, expanding content coverage, and adding personalized learning pathways.
reflection
This project was my first deep dive into building an interactive GenAI educational tool. It challenged me to learn and integrate advanced AI models and APIs while keeping user clarity front and center. Though the Kaggle notebook format limits some interactivity for non-technical users, the ongoing web platform development aims to broaden accessibility and engagement.
I’m proud to have translated complex AI concepts into approachable, hands-on learning experiences, balancing technical rigor with design empathy.
