AI-Powered Meal Planning App to Support Good Health
Ajayi, Temitope (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052315305
https://urn.fi/URN:NBN:fi:amk-2025052315305
Tiivistelmä
This product-based thesis documents the development of an AI-Powered Meal Planning App to Support Good Health. The project explores the educational implementation of React Native with TypeScript, Expo, and Firebase, integrated with an external pre-trained AI model for meal rec-ommendations to create a cross-platform mobile application prototype.
The application addresses meal planning challenges by integrating with an external AI service that provides meal suggestions based on user dietary preferences. Through a mobile interface, users can create weekly meal plans with AI-suggested recipes and generate corresponding shopping lists. The development process follows a structured approach to TypeScript migration and component-based architecture, focusing on educational outcomes and the practical integra-tion of external AI services rather than developing custom machine learning algorithms.
The technical framework encompasses React Native with Expo for cross-platform develop-ment, Firebase for user data management, and API integration with an external meal recom-mendation service. The implementation demonstrates how existing AI technologies can be lev-eraged within an educational project context to enhance application functionality without requir-ing specialized AI development expertise.
The theoretical foundation examines the role of AI-enhanced digital approaches to meal plan-ning, exploring how third-party AI services can be effectively integrated into mobile applications to support dietary management. The project demonstrates how external AI services can be in-corporated into a meal planning application while maintaining implementation feasibility within an educational context.
The research spans from December 2024 to May 2025, focusing on developing a prototype that demonstrates API integration with external AI services. Development and testing are conducted in a local environment using Expo Go and Firebase Emulator, with targeted testing involving a small group of users to evaluate the effectiveness of the AI-powered meal suggestions and overall usability.
The resulting application demonstrates how third-party AI services can be effectively integrated into a React Native application to provide enhanced meal planning capabilities. The project con-tributes to understanding practical approaches for incorporating external AI functionality into mo-bile applications within an educational development context.
The application addresses meal planning challenges by integrating with an external AI service that provides meal suggestions based on user dietary preferences. Through a mobile interface, users can create weekly meal plans with AI-suggested recipes and generate corresponding shopping lists. The development process follows a structured approach to TypeScript migration and component-based architecture, focusing on educational outcomes and the practical integra-tion of external AI services rather than developing custom machine learning algorithms.
The technical framework encompasses React Native with Expo for cross-platform develop-ment, Firebase for user data management, and API integration with an external meal recom-mendation service. The implementation demonstrates how existing AI technologies can be lev-eraged within an educational project context to enhance application functionality without requir-ing specialized AI development expertise.
The theoretical foundation examines the role of AI-enhanced digital approaches to meal plan-ning, exploring how third-party AI services can be effectively integrated into mobile applications to support dietary management. The project demonstrates how external AI services can be in-corporated into a meal planning application while maintaining implementation feasibility within an educational context.
The research spans from December 2024 to May 2025, focusing on developing a prototype that demonstrates API integration with external AI services. Development and testing are conducted in a local environment using Expo Go and Firebase Emulator, with targeted testing involving a small group of users to evaluate the effectiveness of the AI-powered meal suggestions and overall usability.
The resulting application demonstrates how third-party AI services can be effectively integrated into a React Native application to provide enhanced meal planning capabilities. The project con-tributes to understanding practical approaches for incorporating external AI functionality into mo-bile applications within an educational development context.