Reducing the Digital Divide: Empowering Students to Train, Evaluate, and Use AI Text Models

It is challenging for students in non-technical fields, such as law, to effectively engage with technology regulations if they lack firsthand experience with machine-learning. This limits their ability to critically assess these technologies and their compliance with legal and ethical standards, and the possibilities of producing new insights using AI. It creates a digital divide, disadvantaging students in these fields and excluding them from shaping AI’s future. 

This project aims to bridge this divide in AI education by developing a user-friendly, no-code platform that enables non-technical students, particularly in law, to train and evaluate machine-learning models. Building on the already existing Lawnotation software, the platform will allow non-technical students to experience AI's potential and limitations and contribute to responsible AI development in legal contexts and beyond, and provide the skills to meaningfully engage with regulatory and ethical debates around AI. Current no-code AI tools are neither tailored to education nor envisioned to empower non-technical students with AI knowledge. The software will align with FAIR principles, ensuring broad accessibility for other law faculties in the Netherlands (and beyond), many of them having expressed concrete interest in adopting it. 

The project, which runs from 2025 to 2028, will be led by Hannes Westermann and Gijs van Dijck from the Maastricht Law and Tech Lab and includes colleagues from the Brightlands Institute for Smart Society (BISS).

The software will be implemented in various educational programmes, including (but not limited to) the Law and AI master specialisation. The project aligns with Maastricht University’s educational goals and has the potential to contribute to society by fostering a workforce capable of responsibly developing and critically engaging with AI technologies.