# Investigating the correlation between candidate teachers’ acceptance of generative artificial intelligence and artificial intelligence literacy across various disciplines

## 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.

## Key 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, grade level, AI tools used, and self-perceived proficiency. AIL showed significant differences in gender, department, grade, tool usage, and proficiency level, with higher scores among those trained in artificial intelligence. Qualitative data clarify the quantitative findings. Factors affecting GenAI acceptance include daily use, problem-solving, learning applications, mentor usage, assistance from others, proficiency, productivity, discipline-specific skills, and task efficiency. Factors influencing AIL include understanding AI importance, ethical considerations, AI support in daily life, explaining AI, understanding deep learning and machine learning relationships, big data knowledge, AI decision-making processes, knowledge of AI tools, interpretation of AI technologies, critical evaluation, data privacy importance, machine learning knowledge, and evaluation of AI applications in their discipline.