Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States DOI Creative Commons
Mahmoud Sayed Mohamed Ali, Khaled Zaki AbuElkhair Wasel, Amr Mohamed Mahmoud Abdelhamid

и другие.

Journalism and Media, Год журнала: 2024, Номер 5(4), С. 1624 - 1645

Опубликована: Ноя. 6, 2024

This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in context media content creation. The study’s theoretical framework is grounded unified theory acceptance use technology (UTAUT2). A sample 496 was analysed using partial least squares structural equation modelling technique (PLS-SEM). results revealed BI significantly influenced by performance expectancy, effort social influence, hedonic motivation, habit, trust, with motivation having greatest impact. In terms UB, facilitation conditions, were all found have a positive significant study contributes existing on utilisation GenAI organising findings pertaining AI for

Язык: Английский

Trends and emerging themes in the effects of generative artificial intelligence in education: A systematic review DOI
Nguyen Ngoc Nhu Trang,

Hoa Thi Truong

Eurasia Journal of Mathematics Science and Technology Education, Год журнала: 2025, Номер 21(4), С. em2613 - em2613

Опубликована: Март 11, 2025

This paper systematically reviews the impact of Generative Artificial Intelligence (GenAI) in education from 2021 to 2024. The objective is explore key trends, geographical distribution research, and emerging themes educational use GenAI, while addressing ethical challenges such as algorithmic bias, data privacy, digital divide. Using a systematic review methodology guided by four research questions, study analyzes publications Scopus identify dominant leading countries field. Results indicate that United States, Kingdom, Singapore are top contributors GenAI with primary focus on personalized learning automated assessments. highlights surge publications, particularly 2023, driven advancements AI tools like ChatGPT. It emphasizes importance international collaboration proposes need for regulatory frameworks ensure integration education. offers valuable insights into current state provides recommendations educators, policymakers, researchers navigate opportunities AI-driven learning.

Язык: Английский

Процитировано

0

University Students’ Usage of Generative Artificial Intelligence for Sustainability: A Cross-Sectional Survey from China DOI Open Access
Xiao Lin, How Shwu Pyng, Ahmad Fauzi Mohd Ayub

и другие.

Sustainability, Год журнала: 2025, Номер 17(8), С. 3541 - 3541

Опубликована: Апрель 15, 2025

The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate current situations challenges Chinese university students using GenAI four typical task scenarios. This was performed a cross-sectional design. data were collected via questionnaire, with 486 undergraduates from participating. analysis methods include descriptive statistics, inferential content analysis. results show that more than 70% actively use GenAI, but nearly half them are not very proficient use. Doubao ERNIE Bot tools they prefer most. primary functions text production information retrieval. They mainly learn relevant knowledge skills through self-media knowledge-sharing platforms. Among scenarios, is widely used course learning activities, while application daily life job search relatively limited. demographic variables shows grade major have significant impact students’ GenAI. In addition, suggest universities should offer courses or lectures provide comprehensive technical support improve popularity operability study provides suggestions for universities, education administration departments, departments services. It will help optimize allocation educational resources promote equity sustainability.

Язык: Английский

Процитировано

0

AI and ChatGPT in Higher Education: Greek Students’ Perceived Practices, Benefits, and Challenges DOI Creative Commons
Απόστολος Κώστας,

Vasilios Paraschou,

Dimitrios Spanos

и другие.

Education Sciences, Год журнала: 2025, Номер 15(5), С. 605 - 605

Опубликована: Май 14, 2025

As artificial intelligence (AI) continues to evolve, its integration into higher education (HE) has sparked both enthusiasm and concern. This study examines HE students’ perceptions of ChatGPT AI tools. An online survey with closed questions was administered, a convenient sample 515 students gathered analyzed. Findings reveal dual perspective, where recognize AI’s potential enhance research efficiency, support academic tasks, personalize learning experiences, while simultaneously raising concerns regarding ethical considerations, content reliability, declines in critical thinking skills. A key insight from this is the variation based on level ICT competence. The findings reinforce importance comprehensive literacy programs, guidelines, institutional support. Additionally, highlights bridging digital divide, ensuring equitable engagement tools across different competency levels. contributes ongoing discourse by identifying areas adoption can be optimized mitigating risks. Future policy initiatives should focus striking balance between technological advancements human-centered learning, that supports integrity educational innovation.

Язык: Английский

Процитировано

0

Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States DOI Creative Commons
Mahmoud Sayed Mohamed Ali, Khaled Zaki AbuElkhair Wasel, Amr Mohamed Mahmoud Abdelhamid

и другие.

Journalism and Media, Год журнала: 2024, Номер 5(4), С. 1624 - 1645

Опубликована: Ноя. 6, 2024

This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in context media content creation. The study’s theoretical framework is grounded unified theory acceptance use technology (UTAUT2). A sample 496 was analysed using partial least squares structural equation modelling technique (PLS-SEM). results revealed BI significantly influenced by performance expectancy, effort social influence, hedonic motivation, habit, trust, with motivation having greatest impact. In terms UB, facilitation conditions, were all found have a positive significant study contributes existing on utilisation GenAI organising findings pertaining AI for

Язык: Английский

Процитировано

1