Building a Free English Learning Mini Program from Scratch with AI Coding: A Complete Development Journal

It Started with a Hiking Trip

A content creator was hiking through Tiger Leaping Gorge when he encountered a large number of foreign tourists. He wanted to strike up a conversation but couldn't — his English simply wasn't good enough. That experience became his direct motivation to start learning English. Back home, he downloaded one of the English learning apps commonly promoted on Douyin and began his language learning journey.

The experience started out great — content progressed from easy to challenging, and he even found himself getting hooked. But problems surfaced quickly: each lesson was short, followed by ads, and most critically, the number of daily lessons was capped. His enthusiasm evaporated instantly. A membership was available (over 500 yuan per year), but that sparked a bold idea: if AI coding has gotten this powerful, why not just build a free English learning mini program himself?

Why Build It Yourself with AI Coding

This project was driven by two core motivations:

Solving a Real English Learning Need

The creator's English use cases were very specific: foreign trade communication, overseas travel, and everyday conversation. These were genuine, pressing needs — not a product built for the sake of building one. When the first user of a product is the developer themselves, the feature design tends to be far more grounded in reality.

Sharpening AI Coding Skills with Cursor

In the comments section of his previous video, someone recommended an AI coding tool called "Cursor." This gave him the perfect opportunity to put it into practice: use AI-assisted coding to build a complete mini program project from scratch. It wasn't just about learning English — it was also about learning AI coding. Two birds, one stone.

Cursor is an AI-native code editor built on a deeply modified VS Code architecture, developed by Anysphere and rapidly gaining traction in the developer community around 2023. Unlike plugin-based AI coding assistants such as GitHub Copilot, Cursor embeds AI capabilities directly into the editor's core, supporting multi-file context understanding, natural language code generation, intelligent refactoring, and conversational debugging. Under the hood, it integrates top-tier large language models including GPT-4 and Claude, enabling even users without a formal programming background to drive code generation through natural language descriptions. For the "non-professional developer working on a side project" use case, Cursor's core value lies in dramatically lowering the barrier from idea to working code — users play the role of product manager and requirements writer rather than line-by-line engineer.

Product Planning: A Layered Learning Path

After a full night of research and AI consultation (going past 2 a.m.), he mapped out the overall product framework. Notably, he chose WeChat Mini Programs as the platform — a lightweight app format officially launched by Tencent in 2017 that runs inside WeChat without any installation. For an English learning tool, this format has natural advantages: extremely low user acquisition friction (scan a QR code and go), push notification support for building daily study habits, and easy social sharing within the WeChat ecosystem. Combined with WeChat Cloud Development, an individual developer can build a fully functional learning app without setting up complex servers, while AI coding tools like Cursor further compress the learning curve of this tech stack.

Intelligent Leveling System

When users enter the mini program, a short assessment determines their English level and routes them into the appropriate learning path:

• Complete beginners: Starting with the simplest words and sentences
• Some foundation: Scenario-based content like everyday conversation and travel English
• Advanced learners: Practical scenarios like foreign trade communication and overseas interactions

This leveling design maps onto a well-established theoretical framework in language education — the Common European Framework of Reference for Languages (CEFR), which divides language proficiency into six levels from A1 to C2 and is widely adopted by leading apps like Duolingo and Babbel. From a cognitive science perspective, the core logic of leveled learning comes from Krashen's Comprehensible Input Hypothesis (the i+1 theory): learning materials should be slightly above the learner's current level, as content that is too difficult or too easy both reduce learning efficiency. Accurate initial level assessment is therefore the critical prerequisite for the entire leveling system to function effectively — and this is precisely where AI intervention can significantly improve precision.

Parent-Child English Learning Module

One interesting design choice is a dedicated parent-child section, where parents and kids can study and check in together. This expands the product's user base from individual adults to family scenarios, allowing young children to start engaging with English through simple words and sentences.

Practical Reflections on Building a Mini Program with AI Coding

This project is a textbook example of an "AI coding in practice" case:

First, the requirements are clear and well-scoped. The core features of an English learning mini program aren't complex — content display, progress tracking, and leveling assessments are all modules that AI coding tools like Cursor can assist with effectively.

Second, it starts from a personal pain point. The best side projects often come from the scenario of "I want this but there's no perfect solution on the market." Existing English learning apps are decent, but their ads and paywalls create room for differentiation.

Third, the development process is public. He plans to continuously document the entire journey from 0 to 1 through video diaries — which is itself an excellent way to learn, since the act of publishing forces deeper understanding. This approach is known in the indie developer community as "Building in Public," a product development and communication methodology that has gained significant traction in recent years. Its core philosophy: share the entire process of building a product from scratch — the setbacks, the key decisions, the pivots — rather than waiting until the product is finished to go public. Consistent output keeps developers on track and forces them to organize their thinking, while early transparency helps gather real user feedback and avoids building in a vacuum. For the emerging field of AI coding, publicly documenting the complete journey of "a non-programmer building a product with AI" carries enormous reference value and viral potential.

What to Watch Going Forward

This project is still in the planning stage, but there are a few threads worth following:

1. How Cursor performs in a real project: For non-professional programmers, how much can AI coding actually help?
2. How to ensure English learning content quality: Content is the heart of any language learning product — are AI-generated learning materials accurate and systematic enough?
3. The sustainability of a free model: No monetization pressure is an advantage, but long-term maintenance requires ongoing motivation.

For anyone looking to build their own project with AI coding, this is an excellent reference: identify a real need, choose the right tools, document the process publicly, and learn by doing. Looking forward to seeing how the development unfolds.

Key Takeaways

• The creator decided to build a free English learning mini program with AI coding tools after being unable to communicate with foreign tourists during a hiking trip
• Ads and daily lesson limits in existing English learning apps became the pain points that inspired the idea of building independently
• The product design includes an intelligent leveling system covering multiple learning scenarios from beginner to advanced, grounded in the CEFR international language proficiency framework
• Plans to build from scratch using AI coding tools like Cursor, which dramatically lower the development barrier for non-programmers through deep integration of large language models
• The entire development process will be documented publicly through video diaries, embodying the "Building in Public" methodology