About Appligogiques*

*Pronounced "Appligojeek"!

At Appligogiques, we create games that help children learn to read, spell, and write. The acquisition of language is a universal development process, and it can be difficult for children either with our without development impediments. It's a worldwide problem that we tackle with passion and ingenuity!

Specifically, our gamified pedagogical tools are built upon the latest research in speech language pathology and aim to awaken and develop phonemic awareness and lexical orthography in children ages 6-10. We also offer a suite of online tools that support teachers, education specialists, and parents in the complex task of fostering child development in today's digital, delocalized world.

About this project

We are looking to implement a system based on artificial intelligence that allows classification of users of our educational applications according to the difficulties they face in their language learning (reading and writing). This system will be used to customize the interactive experience of the games according to the players, making their products much more relevant and attractive to a larger clientele.

We have already analyzed the problem from an audiology/speech language pathology and an experimental protocol has been set up to collect the data. It has set up a team in the Department of Audiology and Speech-Language Pathology at the Université de Montréal: two students will begin their doctoral project on this subject in early 2021.2020. The project is supervised and mentored by two associate professors of the department.

We've identified the ideal tech stack to realize the artificial neural network; now we are looking for help with the actual implementation of a first iteration of the system.

Our usage of artificial intelligence in the context of this project encompasses two objectives:

1. To understand the different learning styles of our users
2. To use this knowledge to provide real-time, in-app personalized recommendations

To achieve these goals, we need to populate our model with different examples of user interaction data to capture the full spectrum of learning models. The different types of data available to Application users are listed in the resources section, as well as our experimental protocol.

This project will be a collaboration involving players from several fields of expertise, whether from academia or business. It will be an exciting and rewarding research and development effort!

Deliverables and expectations

The deliverable for this project is the distributed implementation of the tech stack, fully functional and ready for demonstration.