Examining artificial intelligence literacy among pre-service teachers for future classrooms
In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis.
Record details
Published: 10 April 2024
Source: OpenAlex
Category: Research
Topics: Children & education
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
The cognitive paradox of AI in education: between enhancement and erosion
OpenAlex · 14 April 2025
Towards an Ethical AI Curriculum: A Pan-African, Culturally Contextualized Framework for Primary and Secondary Education
arXiv · 30 April 2026
Adesua: Development and Feasibility Study of an AI WhatsApp Bot for Science Learning in West Africa
arXiv · 14 May 2026
Open Veins of Algorithmic Auditing: Why AI Assessment Lags Behind Its Deployment in the Global South
arXiv · 23 July 2026
"Why SuaCode?": Understanding African Students' Motivations for Taking a Smartphone-Based Online Coding Course
arXiv cs.CY · 28 July 2026
A new chapter for African Brain Child
University of Cape Town AI · 3 August 2026
How to cite this record
ethics.ai (10 April 2024), “Examining artificial intelligence literacy among pre-service teachers for future classrooms,” evidence record 9604, https://ethics.ai/record/9604 (originally published by OpenAlex).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.