Investigating the correlation between candidate teachers’ acceptance of generative artificial intelligence and artificial intelligence literacy across various disciplines
Berker Kurt, Gözdegül Arık Karamık, Ali Özkaya, Andrea Cioffi, Andrea Cioffi, Andrea Cioffi, Andrea Cioffi
TL;DR
This study explores how future teachers from different fields accept and understand generative AI, identifying factors that influence their acceptance and AI literacy.
Contribution
The study introduces a mixed-methods approach to analyze GenAI acceptance and AIL among prospective teachers across disciplines.
Findings
GenAI acceptance varies by department, grade level, AI tool usage, and self-perceived proficiency.
AIL is significantly influenced by gender, department, grade, and AI training background.
Qualitative insights reveal factors like problem-solving, mentor usage, and ethical understanding as key to AI literacy.
Abstract
This study examines Generative Artificial Intelligence (GenAI) acceptance and Artificial Intelligence Literacy (AIL) levels among prospective teachers, using variables for comparative analysis and identifying influencing factors. The research uses an explanatory sequential mixed methods approach. Quantitative data were obtained from 723 prospective teachers and qualitative data from 48 prospective teachers. Data collection included an Information Form, GenAI Acceptance Scale, and AIL Scale for quantitative data, with interview forms for qualitative data. Parametric tests, independent samples t-test, ANOVA, and Pearson correlation analyzed quantitative data, while factors influencing GenAI acceptance and AIL were identified through themes using MAXQDA. Acceptance levels showed no significant differences by gender or daily internet use; however, differences emerged regarding department,…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Teaching and Learning Programming