Although there has been some progress, the integration of artificial intelligence into higher education remains far from sufficient. The demand for teachers will persist for some time; however, with the introduction of AI-based robots into classrooms, the role of teachers has reduced to a minimum. The purpose of the current study was to evaluate Chinese higher education students’ intentions to adopt AI-based robots for educational purposes. Based on the TAM3 model, the current study proposes 14 hypotheses to evaluate students’ intention to adopt AI-based robots in education. The students’ data were collected and analyzed using PLS-SEM. The study findings revealed that 12 hypotheses were accepted and two were rejected. The results indicate that students are willing to accept AI-based robots in their education. However, the findings revealed an insignificant influence of job relevance and robot anxiety on perceived usefulness and ease of use, respectively. The findings of this study will provide insight into university administrations regarding the significance of AI-based robots in education. Moreover, the findings will help robot developers, policy makers, and university administrators design and implement AI-based robots that fulfill contemporary education needs.

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

The purpose of this study was to determine what difficulties King Saud University students encounter when learning to write academic English and to differentiate between students’ learning needs and objectives. The sample consisted of 50 postgraduate students enrolled in King Saud University during the academic year 2009-2010. Analysis of the data showed that English as a second language (ESL) students face many difficulties and stresses in their academic writing, such as difficulty distinguishing between spoken and written English, making an outline before writing a draft, identifying the skills needed for successful writing, and avoiding plague words and phrases.

Despite recognition that language can best be learned in an authentic context, and a growing emphasis on the importance of using technology to improve listening skills, only limited research in this area exists in a Saudi context. To add to this research, this quantitative study examines the influence of podcasting on the listening comprehension of 46 female Saudi EFL students in higher education. The randomly selected participants were divided into two groups: a control and an experimental group, and given a six week treatment. A T-test and questionnaire were employed, and the results indicate significant differences between the two groups, favoring the experimental group.