Nownify is an experimental Android application created while teaching myself Kotlin and modern Android development.
The project explored whether large AI-generated lesson databases could support personalized language learning by combining automated lesson generation, vocabulary management, prompt engineering, and a native Android application.
To support the experiment, I designed and built an automated pipeline that generated a large educational dataset using AI running on a rented AWS server. The resulting database contained thousands of generated lessons and vocabulary entries, which were then integrated into the Android application.
Although the application was never completed, the project became an important milestone in learning Kotlin, Android architecture, cloud-based AI workflows, and evaluating the practical strengths and limitations of large-scale AI-generated content.
- AI-generated lesson database
- Vocabulary management
- AI-assisted lesson generation
- Prompt engineering workflows
- Text processing and extraction
- Native Android user interface
- Experimental language-learning architecture
- Kotlin and Java interoperability
- Kotlin
- Java
- Android
- Android Studio
- Jetpack Compose
- Android SDK
- AWS
- Gradle
- Generative AI
- Prompt Engineering
- Kotlin application development
- Android application architecture
- Mobile UI development
- Object-oriented programming
- Prompt engineering
- Cloud-based AI workflows
- Large-scale dataset generation
- Learning and adopting a new programming language
- Rapid software prototyping
One of the primary goals of Nownify was to determine whether AI could generate a large enough collection of language-learning content to power a personalized learning application.
To test this idea, I rented cloud infrastructure on AWS and generated a massive lesson database through an automated AI pipeline. While technically successful, the experiment revealed an important limitation: generating large amounts of content does not guarantee useful or engaging educational material. Much of the generated content became repetitive and required significant review and filtering.
Although the experiment was costly in both cloud compute time and AI usage, it provided valuable hands-on experience designing AI workflows, managing cloud resources, evaluating generated data quality, and understanding the practical tradeoffs of AI-assisted software development.
These lessons later influenced the design of future AI-assisted projects.
July – October 2025
Archived Learning Project
Nownify was developed as a personal learning project while teaching myself Kotlin and modern Android development. The project combined application development with experimentation in AI-generated educational content and cloud-based automation.
The original AI-generated database is not included in this repository due to its size and experimental nature. This repository preserves the application architecture, interface, and development approach rather than a fully runnable production application.
This repository is provided for portfolio and educational purposes.