Publication Date
| In 2026 | 0 |
| Since 2025 | 7 |
Descriptor
Author
| Ahmet Uyar | 1 |
| Burcu Karafil | 1 |
| Chao Qin | 1 |
| Cuixin Li | 1 |
| Dawn Branley-Bell | 1 |
| Elizabeth Sillence | 1 |
| Hanife Gülhan Orhan Karsak | 1 |
| Ismail San | 1 |
| Jie Xu | 1 |
| Kaitlyn N. Stormes | 1 |
| Liang Zhou | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 7 |
| Reports - Research | 7 |
Education Level
| Higher Education | 6 |
| Postsecondary Education | 6 |
| Middle Schools | 2 |
| Elementary Education | 1 |
| Grade 6 | 1 |
| Intermediate Grades | 1 |
| Junior High Schools | 1 |
| Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkLihui Sun; Liang Zhou – Education and Information Technologies, 2025
Generative Artificial Intelligence (GenAI) has fundamentally transformed the education landscape, offering unprecedented potential for personalized learning and enhanced teaching methods. This research conducted two sub-studies aimed at exploring the influences and differences in college students' attitudes towards generative artificial…
Descriptors: Artificial Intelligence, Computer Uses in Education, Computer Attitudes, Student Attitudes
Jie Xu; Yan Li; Rustam Shadiev; Cuixin Li – Education and Information Technologies, 2025
Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students' daily life. However, limited studies paid attention to college students' use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory…
Descriptors: College Students, Technology Uses in Education, Artificial Intelligence, Intention
Hanife Gülhan Orhan Karsak; Sultan San; Ismail San – Education and Information Technologies, 2025
The objective of this study is to compare the levels of acceptance of occupational technology among police officers and middle school teachers in the Eastern Anatolia region of Turkey. Analyses based on the UTAUT2 model evaluated the impact of demographic variables, including gender, occupation, age, and tenure, on technology acceptance processes.…
Descriptors: Computer Attitudes, Positive Attitudes, Police, Teachers
Richard Brown; Elizabeth Sillence; Dawn Branley-Bell – Journal of Educational Technology Systems, 2025
We investigate perceptions of AI among university students and staff, focusing on sociodemographic predictors of use, attitudes and literacy. We follow an explanatory mixed-methods approach: an online survey (269 students and staff) capturing self-reported AI use, attitudes, and literacy, and 24 semi-structured online interviews exploring barriers…
Descriptors: Artificial Intelligence, Technology Uses in Education, College Students, Student Attitudes
Chao Qin; Mengli Zhang; Zhixin Li; Luxin Chen – Education and Information Technologies, 2025
Artificial intelligence (AI) is being deeply integrated into human society. In the future, human collaboration with AI is inevitable. Therefore, exploring the attitudes of future workers--represented by current K-12 children--towards AI has become crucial. Robots stand as typical representatives of AI. Robot programming education is an important…
Descriptors: Foreign Countries, Rural Schools, Elementary School Students, Grade 6
Burcu Karafil; Ahmet Uyar – Education and Information Technologies, 2025
This study investigates the knowledge, attitudes, and practices (KAP) of Chat Generative Pre-Trained Transformer (ChatGPT) among academics working in the field of educational science in Türkiye. Employing a mixed-methods research design, the study aimed to explore both quantitative and qualitative aspects of academics' interactions with ChatGPT.…
Descriptors: Foreign Countries, College Faculty, Teacher Attitudes, Computer Attitudes
Linda J. Sax; Kaitlyn N. Stormes; Maxx F. Pereyra – ACM Transactions on Computing Education, 2025
To cultivate more computing talent (including more diverse talent), it is important to understand how college students experience their computing courses and if such experiences vary based on students' gender and racial/ethnic identities. In this paper, we focus on course modality to understand whether taking courses in-person, online, or a hybrid…
Descriptors: Computer Science Education, Electronic Learning, Online Courses, Delivery Systems
