The article reviews the current state and future prospects of Artificial Intelligence (AI) applications in higher education (HE). Despite growing interest and excitement over advanced AI technologies like large language models (LLMs), such as OpenAI's ChatGPT, the actual impact of AI on HE institutions and participants remains largely unknown. The authors highlight the need for more comprehensive and collective research to understand the real-world implications of AI in education. A systematic literature review conducted in 2019 identified four key areas of AI applications in teaching and learning: profiling and prediction, assessment and evaluation, intelligent tutoring systems, and adaptive systems and personalization. However, the review found low participation from educators and educational scientists, and many studies focused on technological and methodological issues rather than pedagogical and ethical aspects. Since 2019, the field of AI in education has seen rapid growth, with a significant increase in published studies. The authors were commissioned to conduct an updated review, which revealed a tenfold increase in studies on AI in education compared to the previous decade. The aim of this special issue is to revisit the realities of AI in HE in the post-Covid-19 era, focusing on the impact of AI on different aspects of HE institutions, learning and teaching, and various stakeholders. The articles in this issue address the influence of generative AI on institutional policies, student programming behaviors, feedback perceptions, and the overall impact on student learning. They also discuss the importance of developing skills to deal with generative AI critically and creatively. Despite the rapid development, the authors caution against premature conclusions, noting that research still lags behind the dynamic technical and methodological advancements in AI. In conclusion, while the articles in this issue are strongly influenced by the current development of generative AI tools, more research is needed to explore the system, organizational, and administrative levels, and to understand the effects and implications for different stakeholders in HE.The article reviews the current state and future prospects of Artificial Intelligence (AI) applications in higher education (HE). Despite growing interest and excitement over advanced AI technologies like large language models (LLMs), such as OpenAI's ChatGPT, the actual impact of AI on HE institutions and participants remains largely unknown. The authors highlight the need for more comprehensive and collective research to understand the real-world implications of AI in education. A systematic literature review conducted in 2019 identified four key areas of AI applications in teaching and learning: profiling and prediction, assessment and evaluation, intelligent tutoring systems, and adaptive systems and personalization. However, the review found low participation from educators and educational scientists, and many studies focused on technological and methodological issues rather than pedagogical and ethical aspects. Since 2019, the field of AI in education has seen rapid growth, with a significant increase in published studies. The authors were commissioned to conduct an updated review, which revealed a tenfold increase in studies on AI in education compared to the previous decade. The aim of this special issue is to revisit the realities of AI in HE in the post-Covid-19 era, focusing on the impact of AI on different aspects of HE institutions, learning and teaching, and various stakeholders. The articles in this issue address the influence of generative AI on institutional policies, student programming behaviors, feedback perceptions, and the overall impact on student learning. They also discuss the importance of developing skills to deal with generative AI critically and creatively. Despite the rapid development, the authors caution against premature conclusions, noting that research still lags behind the dynamic technical and methodological advancements in AI. In conclusion, while the articles in this issue are strongly influenced by the current development of generative AI tools, more research is needed to explore the system, organizational, and administrative levels, and to understand the effects and implications for different stakeholders in HE.