Vocab

Preface

At the start of this year, I felt disconnected from my work at Stockfeel despite its challenges. The workplace culture and lack of meaningful impact on society prompted me to leave and reevaluate my career and goals.Since I’ve always want to join an international corporation, I started focus on improving my English proficiency, again.

Though I had invested in various English learning materials and standardized test prep, I realized these methods were ineffective. In conversations with friends facing similar struggles, I found we shared the frustration of learning and forgetting without practical application. This collective experience inspired me to create a solution not only for my own improvement but also to help others overcome the same obstacles.

Motivated by these insights, I began researching AI-enhanced language learning systems (saas, software as a service) and have already crafted a first prototype, now in the alpha testing phase.

About this project

Vocab is an AI-powered language learning platform that transforms how people learn and retain language skills. Going beyond traditional content delivery, it combines AI technology with proven learning methods to create a comprehensive experience featuring smart spaced repetition, personalized learning paths, and diverse practice options including listening, writing, chatting, and reading exercises. The platform enables users to build personal content collections, engage in group learning, and practice through real-world scenarios, while the AI system continuously adapts to individual learning patterns for optimal retention. Currently in alpha testing, Vocab addresses key challenges in self-regulated learning by helping users track their mastery, maintain consistent review schedules, and effectively apply their knowledge in practical situations.

Vocab is focus on solving the following problems

• How to really know you have learned all the content?
• How to not only prepare for test but also improve the general ability
• How to review a content?
• How to only practice the content that is important to you
• How to find out the content you might forgotten

The whole lifecycle of vocab

Since I'm the sole developer behind the app, I've become familiar with every step of the Software Development Life Cycle (SDLC). I realized the importance of each stage and didn't want to get overwhelmed by the complexity, so I simplified the process into my personal version of the SDLC. Additionally, I created a "team building" system for myself to remind me which role I'm playing at each stage of the project.

With this setup, I’ve been able to create a clear product roadmap and always know what task comes next (a skill I credit to my past experience as a Project Manager). If something goes wrong, I can easily backtrack to the previous step and investigate what happened. I firmly believe that simplicity is best, so every feature of Vocab has to solve a clear problem for the user.

Here’s a breakdown of my process:

1. Literature Research (Problem Definition and Solution Search): In this phase, I focused on understanding the core problems faced by English language learners and explored existing solutions. I dived deep into academic papers, EdTech case studies, and user feedback to refine the problem definition. My goal was to identify gaps in current solutions, particularly in terms of personalization, memory retention, and the overall learning experience.
2. Feature Planning: Based on the research findings, I brainstormed and prioritized features that would best address the identified problems. Each feature had to pass a strict test: Does it solve a real user problem? I documented everything from major functionalities like smart spaced repetition to smaller elements like how users could engage in peer learning.
3. UI/UX Design: For this phase, I relied on ClaudeAI to help me design a prototype. The focus was on ensuring a user-friendly interface that maximized engagement and minimized cognitive load. This is where I applied my knowledge of Cognitive Load Theory (CLT) to keep the design simple, intuitive, and optimized for learning.
4. System Analysis and Design (SA/SD): Once the design was completed, I translated it into detailed system specifications, outlining the data structures, APIs, and workflows that would bring the features to life. Since I was handling both front-end and back-end, having a well-structured blueprint made the development process much smoother.
5. Software Development: Here, the app truly started to take shape. The development phase was broken down into three key areas:

• Prompt Development: This involved refining the AI’s ability to generate relevant prompts for users, helping them practice language skills in ways tailored to their individual needs.
• Frontend Development: I built the user interface and experience, ensuring that the design elements from the prototype were implemented effectively and were responsive across devices.
• Backend Development: I set up the architecture, databases, and API integrations to handle user data and enable seamless interactions with the AI model, ensuring that the system could scale as needed.

Some product screenshots

Closing Thoughts

As Vocab continues to evolve, the key principles behind its development remain the same: simplicity, adaptability, and solving real-world problems. The platform is more than just another language learning tool—it’s a solution designed for people like me and my friends, who need a personalized, practical approach to language acquisition that goes beyond standard methods.

I’ve built Vocab to be scalable, user-centered, and driven by research. The road ahead involves refining the product through ongoing alpha testing, collecting user feedback, and exploring partnerships that could bring Vocab to a larger audience. But at its core, Vocab’s mission will always be the same: to make language learning more accessible, enjoyable, and effective for everyone.