This paper provides a systematic review of the current state of research on generative artificial intelligence (GenAI) in higher education (HE). The review aims to address the lack of agreed guidelines for using GenAI systems in HE and to identify effective ways to incorporate the technology into teaching and learning practices. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study searched Scopus for relevant studies, resulting in 625 papers, of which 355 were included. The findings reveal the current state and future trends in document citations, sources/ authors, keywords, and co-authorship. The research gaps identified suggest that while some studies focus on detecting AI-generated text, there is a need to understand how GenAI can support educational curricula, assessments, and teaching delivery. The study also highlights the importance of interdisciplinary, multidimensional research in HE through collaboration to strengthen awareness and understanding among students, tutors, and stakeholders. This will facilitate the formulation of guidelines, frameworks, and policies for GenAI usage in HE. - **Keywords**: artificial intelligence; generative AI; higher education; PRISMA; systematic literature review; teaching and learning; topic modelling The paper aims to provide an overview of the current state of research on GenAI for teaching and learning in HE, synthesize findings, and offer insights into future research directions. The study formulates two research questions (RQs) to be answered: - RQ1: What is the evolutionary productivity in the field in terms of the most influential journals, most cited articles, and authors, including geographical distribution of authorship? - RQ2: What are the main trends and core themes emerging from the extant literature? The systematic literature review was conducted using the PRISMA guidelines. Scholarly articles (conference proceedings and journal papers) over the last 7 years were reviewed and analyzed. The search strategy used key terms related to GenAI, teaching, and HE. The initial search generated 625 papers, and after applying inclusion and exclusion criteria, 355 papers were selected for analysis. Data quality assessment was conducted using Cohen’s kappa inter-rater reliability, and bibliometric and topic modelling approaches were employed to analyze the data. The results are presented in two sections: - **Bibliometric Analysis Results**: This section provides insights into the publication patterns, research progress, and impact of academic literature. It includes analysis of publication types, citations per source title, citations per first author, publications/citations per year, co-authorship, and co-occurrence of keywords. - **Topic Modelling Results**: This section presents the results from the Latent Dirichlet Allocation (LDA) model, which identifies 10 core themes in the research documents, including implications of GenAI, GenAI for education and research, support systems, bias and inclusion, intelligent tutoring systems, machine learning/AI applications, performance evaluation on exam questions,This paper provides a systematic review of the current state of research on generative artificial intelligence (GenAI) in higher education (HE). The review aims to address the lack of agreed guidelines for using GenAI systems in HE and to identify effective ways to incorporate the technology into teaching and learning practices. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study searched Scopus for relevant studies, resulting in 625 papers, of which 355 were included. The findings reveal the current state and future trends in document citations, sources/ authors, keywords, and co-authorship. The research gaps identified suggest that while some studies focus on detecting AI-generated text, there is a need to understand how GenAI can support educational curricula, assessments, and teaching delivery. The study also highlights the importance of interdisciplinary, multidimensional research in HE through collaboration to strengthen awareness and understanding among students, tutors, and stakeholders. This will facilitate the formulation of guidelines, frameworks, and policies for GenAI usage in HE. - **Keywords**: artificial intelligence; generative AI; higher education; PRISMA; systematic literature review; teaching and learning; topic modelling The paper aims to provide an overview of the current state of research on GenAI for teaching and learning in HE, synthesize findings, and offer insights into future research directions. The study formulates two research questions (RQs) to be answered: - RQ1: What is the evolutionary productivity in the field in terms of the most influential journals, most cited articles, and authors, including geographical distribution of authorship? - RQ2: What are the main trends and core themes emerging from the extant literature? The systematic literature review was conducted using the PRISMA guidelines. Scholarly articles (conference proceedings and journal papers) over the last 7 years were reviewed and analyzed. The search strategy used key terms related to GenAI, teaching, and HE. The initial search generated 625 papers, and after applying inclusion and exclusion criteria, 355 papers were selected for analysis. Data quality assessment was conducted using Cohen’s kappa inter-rater reliability, and bibliometric and topic modelling approaches were employed to analyze the data. The results are presented in two sections: - **Bibliometric Analysis Results**: This section provides insights into the publication patterns, research progress, and impact of academic literature. It includes analysis of publication types, citations per source title, citations per first author, publications/citations per year, co-authorship, and co-occurrence of keywords. - **Topic Modelling Results**: This section presents the results from the Latent Dirichlet Allocation (LDA) model, which identifies 10 core themes in the research documents, including implications of GenAI, GenAI for education and research, support systems, bias and inclusion, intelligent tutoring systems, machine learning/AI applications, performance evaluation on exam questions,