AfricAIED 2024: 2nd Workshop on Artificial Intelligence in Education in Africa
Project Overview
The document explores the transformative role of generative AI in education, particularly within the African context, emphasizing the necessity for AI solutions that cater to the continent's unique educational challenges. It highlights the AfricAIED 2024 workshop, which seeks to democratize education in Ghana through an innovative online AI Hackathon designed for the National Science and Maths Quiz (NSMQ). This initiative aims to foster collaboration and creativity by encouraging participants to develop open-source AI tools that enhance science and math education. The document underscores the importance of addressing the barriers faced by African students in accessing quality educational resources, showcasing how tailored AI applications can facilitate improved learning outcomes and greater educational equity. Ultimately, it advocates for the integration of generative AI as a means to empower educators and learners in Africa, driving positive change in the educational landscape.
Key Applications
Open-source AI tools for NSMQ preparation
Context: Educational context for senior secondary school students in Ghana preparing for the National Science and Maths Quiz.
Implementation: Participants in the AI Hackathon will create AI tools using datasets, code, and models from the Brilla AI project.
Outcomes: The tools aim to level the academic playing field and enhance science and math education across Africa, providing personalized learning support.
Challenges: Challenges include ensuring equitable access to resources, developing AI that understands local dialects, and technical limitations of open-source tools.
Implementation Barriers
Equity barrier
Inequity in resources and preparation for the NSMQ, where only well-resourced schools can compete effectively.
Proposed Solutions: Crowdsourcing AI-powered tools to democratize preparation and improve access to quality educational materials.
Technical barrier
Need for AI systems that can accurately process and respond to local dialects and accents, including Ghanaian accents in speech-to-text and text-to-speech applications.
Proposed Solutions: Development of models specifically designed for local dialects and accents in Ghana.
Project Team
George Boateng
Researcher
Victor Kumbol
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: George Boateng, Victor Kumbol
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai