AUTHOR=Beyazhancer Rumeysa , Demir Baris 

TITLE=Enhancing techno-mathematical literacy and AI self-efficacy in engineering education through artificial intelligence applications

JOURNAL=Frontiers in Education

VOLUME=Volume 10 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1695351

DOI=10.3389/feduc.2025.1695351

ISSN=2504-284X

ABSTRACT=In recent years, the rapid development of artificial intelligence technologies has been transforming the nature of engineering education and reshaping the skill sets expected from students. In this context, techno-mathematical literacy (TmL) stands out as a critical competence that enables engineering candidates to use both technology and mathematical thinking in an integrated manner. This study was conducted to examine the effect of artificial intelligence applications performed by engineering candidates on the development of their techno-mathematical literacy and to determine their self-efficacy levels regarding these applications. The study was designed with a quasi-experimental single group pre-test-post-test model from quantitative research approaches. The study group consists of 156 students, selected by simple random sampling method, studying in different programs at the engineering faculty of a state university. The data were collected with the Techno-Mathematical Literacy Scale (TMLS) and the Artificial Intelligence Self-Efficacy Scale (AILS). The data obtained from the pre-test and post-test applications were analysed with descriptive statistics, paired sample t-test, independent sample t-test, and ANOVA through SPSS 27 software. Also, the effect size was calculated. At the end of the six-week implementation process, it was found that artificial intelligence applications significantly increased the techno-mathematical literacy levels and artificial intelligence self-efficacy perceptions of engineering candidates. In addition, there was no significant difference in techno-mathematical literacy level and perception of artificial intelligence self-efficacy in terms of gender, but significant differences were found according to the department variable.