Accessible AI That’s Out of This World: Globalizing AI Literacy through Problem-Based Learning and Deep Learning Models in a Low Code Environment

Abstract

From phones, to advertisements, to search engines, AI is a constant presence in daily life. As AI continues to permeate our everyday behaviors, products, and relationships with the world, an urgent need for equitable, effective AI education emerges. While existing research has shown the importance of democratizing access to AI, this effort has been predominantly geared toward WEIRD (White Educated Industrialized Rich Democratic) populations which leads to biased results and false generalizations.

With the goal of helping students engage meaningfully and safely with AI; in this thesis I implement tooling to allow for interaction with complex Natural Language Processing models in a low code environment, design curriculum for a problembased approach to teaching AI, run a series of workshops with students from WEIRD (United States) and Non-WEIRD (India) countries, and analyze the results.

The research showed students’ confidence and demonstrated ability grew significantly after the workshops; students were able to demonstrate key AI literacy skills, build complex technology projects, and leverage AI to come up with original and creative solutions to specific, real-world problems. Students’ perceptions of Conversational AI agents became more positive after the workshops and notably, their trust in AI increased. When comparing students from WEIRD and Non-WEIRD countries, Non-WEIRD students were less critical of technology than their WEIRD counterparts and believed AI would be used to complete tasks for humans rather than with them.

Overall, this work showcases the possibility for gaining AI understanding and literacy skills through a problem-based approach to AI education in conjunction with hands-on tooling, as well as highlights the importance of expanding research to include populations beyond WEIRD demographics.