How is the academic community conceptualizing and approaching the integration of AI in education, considering its potential, complexities, and challenges? This study addresses this fundamental question by employing a multifaceted approach that combines co-occurrence network analysis, latent Dirichlet allocation (LDA), and sentiment analysis on a corpus of abstracts from academic publications from 2018 to 2024. The findings reveal key themes in the scholarly discourse, including the centrality of ethical considerations, the impact of global events on AI adoption, and the practical applications of AI in educational management and policymaking. Moreover, the study identifies the main factors discussed in literature as influencing successful AI integration, the challenges and opportunities associated with AI in education, and the evolving academic perspectives on AI's role in educational settings. This comprehensive analysis of academic literature provides valuable insights into the current state of AI in education research, highlighting trends, challenges, and sentiments as they have evolved over time. By mapping the landscape of scholarly thought on this topic, this study aims to inform future research agendas, contribute to policy discussions, and provide a foundation for evidence-based decision-making in the development and implementation of AI technologies in educational contexts.