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The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage. [ABSTRACT FROM AUTHOR]

In higher education, it is imperative to maintain educational quality, which requires necessitates a thorough evaluation of teaching methods. The advent of mixed reality (MR) technology, combined with interactive mobile capabilities, introduces innovative possibilities for traditional educational assessment frameworks. This study is dedicated to investigating the application of MR and interactive mobile technologies in higher education teaching evaluations, with a focus on assessing their effectiveness and implementation outcomes. Research to date has explored MR's educational applications. However, the integration of interactive mobile technologies alongside MR in developing specific methodologies and evaluative tools for teaching evaluations has not been fully realized. This underutilizes the combined potential of these technologies. This paper is anchored in two principal research endeavors. Initially, it delves into the construction of a meta-model facilitated by MR-aided and interactive mobile-enhanced teacher-student interactions, utilizing a hierarchical remote interaction pyramid model. This process involves developing extensible associative functions, determining index weights, and establishing evaluation levels, which collectively enhance the evaluation's multidimensionality, interactivity, and scientific precision. Subsequently, the analytic hierarchy process (AHP) is employed to quantitatively assess the educational impact of MR and interactive mobile experiences on students. This approach provides support for customized and precise teaching evaluations. The findings reveal that integrating MR technology with interactive mobile capabilities significantly enhances the interactivity and systematic scientific approach of teaching evaluations. This fosters a more multidimensional and interactive framework for educational evaluation. [ABSTRACT FROM AUTHOR]

The evaluation and ranking of educational institutions are of paramount importance to a wide range of stakeholders, including students, faculty members, funding organizations, and the institutions themselves. Traditional ranking systems, such as those provided by QS, ARWU, and THE, have offered valuable insights into university performance by employing a variety of indicators to reflect institutional excellence across research, teaching, international outlook, and more. However, these linear rankings may not fully capture the multifaceted nature of university performance. This study introduces a novel clustering analysis that complements existing rankings by grouping universities with similar characteristics, providing a multidimensional perspective on global higher education landscapes. Utilizing a range of clustering algorithms—K-Means, GMM, Agglomerative, and Fuzzy C-Means—and incorporating both traditional and unique indicators, our approach seeks to highlight the commonalities and shared strengths within clusters of universities. This analysis does not aim to supplant existing ranking systems but to augment them by offering stakeholders an alternative lens through which to view and assess university performance. By focusing on group similarities rather than ordinal positions, our method encourages a more nuanced understanding of institutional excellence and facilitates peer learning among universities with similar profiles. While acknowledging the limitations inherent in any methodological approach, including the selection of indicators and clustering algorithms, this study underscores the value of complementary analyses in enriching our understanding of higher educational institutions' performance. [ABSTRACT FROM AUTHOR]