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.

Middle leadership development in a Higher Education context can be understood as a complex endeavor, influenced by interactions and interrelations within multiple systems. We explored the perspectives of thirty-five male and female middle leaders on the conditions influencing their leadership development at one university in Qatar. Q methodology was applied to collect and analyze data quantitatively and qualitatively. A 40-statement Q-sample was developed based on a proposed conceptual framework inspired by complexity thinking, and consisting of three dimensions; intrapersonal, relational, and institutional. Q factor analysis revealed four significantly different viewpoints regarding the conditions that participants perceived were most influential for their leadership development, namely (1) institutional goals, (2) a culture of trust, (3) senior leadership support, and (4) personal ambitions. Despite the differences in viewpoints, three common constraints to leadership development were also identified. The study has implications for middle leadership development, with its distinctive methodological, theoretical, and contextual contributions.

Taking a human-centered socio-cultural perspective, this study explored the manifold individual and structural processes that contribute to researchers’ AI readiness. Forty-three graduate students and faculty at one university in Qatar took part in this Q methodology study. The results represented participants’ collective perspectives on what they considered relevant to their AI readiness. A 5 + 1-factor solution was accepted, illustrating diverse perspectives and no consensus. The factors were termed based on their main foci, as follows, (F-1) how technical skills are acquired, (F-2) when it is all about ethics, (F-3) when technical skills meet ethical considerations, (F-4a and F-4b) when opposites concede, and (F-5) how collaborations reflect AI readiness. The results revealed the diversity of viewpoints among participants, and the interrelations among some factors. This study recommended a holistic approach to enhance AI readiness. It suggested integrating targeted educational initiatives and developing localized ethical frameworks to promote responsible AI use across various research disciplines.

University Teachers’ Ambivalence about the Digital Transformation of Higher Education

The study adopted a sequential mixed-methods approach to investigate student teachers’ learner agency in a teamwork setting at Qatar University. In the qualitative phase, a total of 10 student teachers enrolled in a course adopting a STEAM pedagogical approach participated in in-depth interviews. Qualitative analysis identified seven sources for practicing learner agency in teams. Using this data, the Learner Agency in Teamwork Settings survey was constructed. The survey was distributed to 108 student teachers taking the same course, which utilized an inquiry, problem solving, and digital-based learning design. Using exploratory and confirmatory factor analyses, four factors emerged, including: interest and motivation, teamwork self-efficacy, socio-cultural support, and team support. The findings have important educational implications for promoting student teachers’ agency in similar educational contexts.