COMPARING TURKISH PRE-SERVICE STEM AND NON-STEM TEACHERS' ATTITUDES AND ANXIETY TOWARD ARTIFICIAL INTELLIGENCE
COMPARING TURKISH PRE-SERVICE STEM AND NON-STEM TEACHERS' ATTITUDES AND ANXIETY TOWARD ARTIFICIAL INTELLIGENCE
Author(s): Ozgun Uyanik Aktulun, Koray Kasapoglu, Bulent AydogduSubject(s): Education, ICT Information and Communications Technologies
Published by: Scientia Socialis, UAB
Keywords: artificial intelligence; anxiety toward artificial intelligence; attitude toward artificial intelligence; pre-service STEM teacher; pre-service non-STEM teacher; pre-service teacher;
Summary/Abstract: Identifying student teachers’ attitudes and anxiety toward artificial intelligence (AI) in regard to their field of study might be helpful in determining whether and how AI will be employed in their future classrooms. Hence, this study aims to compare pre-service STEM and non-STEM teachers’ attitudes and anxiety toward AI. In this quantitative research, the causal-comparative research design was adopted. The study involved 520 pre-service teachers from a faculty of education at a public university in Türkiye. Among all, 51.5% were pre-service non-STEM teachers while 48.5% were pre-service STEM teachers. Data were collected through the Turkish versions of “the General Attitudes toward Artificial Intelligence Scale” and “the Artificial Intelligence Anxiety Scale”. Diagnostic analytics were performed, and descriptive statistics and MANOVA were performed to analyse the data. As a result, pre-service teachers, in general, were mostly positive about AI, but undecided to be anxious about AI. STEM student teachers had more positive attitudes toward AI than non-STEM student teachers, and non-STEM student teachers were more anxious toward AI than STEM student teachers. The results imply that non-STEM teacher education curricula should be redesigned to be AI-integrated to better prepare teachers of the future as teachers with TPACK integrated with AI.
Journal: Journal of Baltic Science Education
- Issue Year: 23/2024
- Issue No: 5
- Page Range: 950-963
- Page Count: 14
- Language: English