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Tinaye Des Kamukapa; Stellah Lubinga; Tyanai Masiya; Lerato Sono – Teaching Public Administration, 2025
There is an increasing call to include Artificial Intelligence (AI) competencies in academic disciplines such as Public Administration, which are not obviously related to Science, Technology, Engineering and Mathematics (STEM). However, the literature on the integration of AI in non-STEM curricula in South African higher education institutions…
Descriptors: Artificial Intelligence, Public Administration Education, College Curriculum, Undergraduate Study
Mohammad Saleh Torkestani; David B. Dose; Taha Mansouri – Marketing Education Review, 2025
The advancement of artificial intelligence is reshaping the marketing landscape, underscoring the need to integrate prompt engineering into marketing education. This study presents a conceptual framework for embedding prompt engineering within marketing curricula, rooted in established educational theories. An integrative literature review and…
Descriptors: Business Education, Artificial Intelligence, Computer Literacy, Marketing
Tai Ming Wut; Elaine Ah-heung Chan; Helen Shun-mun Wong; Jason K. Y. Chan – Education & Training, 2025
Purpose: Artificial intelligence (AI) has been used in the workplace for years. There are compulsory or elective AI courses in some universities for students to enroll in. With the wide adoption of AI in many industries now, employers also expect students to make good use of AI to enhance their work productivity and efficiency. Hence, the purpose…
Descriptors: Artificial Intelligence, Computer Literacy, Employment Potential, College Students
Suzanne Estaphan; David Kramer; Harry J. Witchel – Advances in Physiology Education, 2025
The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need…
Descriptors: Artificial Intelligence, Assignments, Student Evaluation, Computer Literacy
Yulu Cui; Hai Zhang – Education and Information Technologies, 2025
With the development of artificial intelligence technology, it has become increasingly difficult to distinguish between Artificial Intelligence Generated Content (AIGC) and non-AIGC. Inaccuracies in identifying AIGC in higher education may lead to academic misconduct and risks, and the credibility of AIGC is also subject to certain doubts. Users…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Yin-Chan Liao; G. Sue Kasun; Nozipho Moyo – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
This study examined the impact of a U.S. federal teacher professional learning (PL) Fulbright program on computational literacy and artificial intelligence (AI) education for K-12 teachers (n = 21) from resource-constrained countries. Occurring shortly after the rise of generative AI in November 2023, the program may have further accentuated AI's…
Descriptors: Artificial Intelligence, Computer Literacy, Computer Science Education, Teacher Attitudes
Alison Lui; Catharina Womack; Penny Orton – Education and Information Technologies, 2025
This article critically evaluates the effects of the ACU Virtual Mobility (VM) Project Digital Information Security Course (DISC) programme on improving students' awareness of cyber security skills, pre and post the educational intervention. Our selected VM tool is Collaborative Online International Learning (COIL). Building on a conceptual…
Descriptors: Cooperative Learning, Electronic Learning, Foreign Countries, Information Security
Agus Pahrudin; Irwandani; Muhammad Aridan; Muhammad Farhan Barata – Journal of Teaching and Learning, 2025
The integration of deep learning in education has the potential to enhance pedagogical practices, personalized learning, and adaptive instruction. However, Islamic schools face unique challenges in adopting AI-driven educational models due to technological limitations, digital literacy disparities, and regulatory constraints. This study assesses…
Descriptors: Religious Schools, Islam, Foreign Countries, Elementary School Teachers
Gabrielle Rabinowitz; Katherine S. Moore; Safinah Ali; Mark Weckel; Irene Lee; Preeti Gupta; Rachel Chaffee – International Journal of STEM Education, 2025
This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly…
Descriptors: High School Students, Museums, Science Teaching Centers, Artificial Intelligence
Ricardo Alberto Reza Flores; Citlali Michélle Reza-Flores; Cristinao Galafassi; Abril Acosta-Ochoa; Rosa Maria Vicari – Journal of Pedagogy, 2025
This study examines how secondary-school students recognize and relate to artificial intelligence (AI) and the meanings they attribute to it in their everyday lives. Using a quantitative, descriptive, cross-sectional design, we explore the subjectivities of a purposive sample of 576 students from both public and private schools. The analysis…
Descriptors: Secondary School Students, Artificial Intelligence, Ethics, Moral Values
Eleonora Barelli; Michael Lodi; Laura Branchetti; Olivia Levrini – Science & Education, 2025
In a historical moment in which Artificial Intelligence and machine learning have become within everyone's reach, science education needs to find new ways to foster "AI literacy." Since the AI revolution is not only a matter of having introduced extremely performant tools but has been determining a radical change in how we conceive and…
Descriptors: Artificial Intelligence, Science Education, Cognitive Development, Cultural Influences
Qing Guo; Junwen Zhen; Fenglin Wu; Yanting He; Cuilan Qiao – Journal of Educational Computing Research, 2025
The rapid development of large language models (LLMs) presented opportunities for the transformation of science and STEM education. Research on LLMs was in the exploratory phase, characterized by discussions and observations rather than empirical investigations. This study presented a framework for incorporating LLMs into Science and Engineering…
Descriptors: STEM Education, Computational Linguistics, Teaching Methods, Educational Change

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