Building a cross-platform application using AI tools
Al-Obaidi, Marwan (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024051813132
https://urn.fi/URN:NBN:fi:amk-2024051813132
Tiivistelmä
The point of this thesis is to show how to build a cross-platform educational application for multiple platforms while using different frameworks with the help of AI. The project spans three different platforms: web, mobile and desktop.
The application stores data on items such as courses and tutorials. The data includes information such as topic, difficulty, contents, tags, price and the link to the item's origin. The data is rendered as cards that can be viewed by the user. These cards can be placed in a list and stored online with a sharable link.
The frontend for the application includes ReactJS, React Native and Electron. The backend includes Firebase, Render and an installer. The AIs used in this project include GPT-3.5 and GitHub Copilot.
The web version of this application was commissioned by the AIoT Garage for the Finnish AI Region (FAIR). According to a representative from FAIR, the app was delivered better than requested and has given the writer of this thesis the permission to continue development on alternate versions. This development has resulted in two additional versions of the application, one for mobile devices and another for computers. The web version has had some minor stylistic changes as well.
AI has helped the project by expediting the development of each version, especially the mobile version. The time spent on the mobile version was only four hours and twenty five minutes. AI tools have proven multiple times to be efficient in file conversions and creating components with basic logic but not for entire projects. Developers will always be required to check the outputs of AI for coherence.
The application stores data on items such as courses and tutorials. The data includes information such as topic, difficulty, contents, tags, price and the link to the item's origin. The data is rendered as cards that can be viewed by the user. These cards can be placed in a list and stored online with a sharable link.
The frontend for the application includes ReactJS, React Native and Electron. The backend includes Firebase, Render and an installer. The AIs used in this project include GPT-3.5 and GitHub Copilot.
The web version of this application was commissioned by the AIoT Garage for the Finnish AI Region (FAIR). According to a representative from FAIR, the app was delivered better than requested and has given the writer of this thesis the permission to continue development on alternate versions. This development has resulted in two additional versions of the application, one for mobile devices and another for computers. The web version has had some minor stylistic changes as well.
AI has helped the project by expediting the development of each version, especially the mobile version. The time spent on the mobile version was only four hours and twenty five minutes. AI tools have proven multiple times to be efficient in file conversions and creating components with basic logic but not for entire projects. Developers will always be required to check the outputs of AI for coherence.