This paper explores the implications of artificial intelligence (AI) in education for policymakers, researchers, and practitioners. It presents findings from a Delphi study involving 33 international professionals and in-depth discussions with researchers to identify key trends and challenges in AI deployment in education. The study highlights three critical trends: privacy and ethical use of AI, trustworthy algorithms, and equity and fairness. These trends were also identified as the top challenges in implementing AI in education. The paper outlines policy recommendations and a research agenda to address identified gaps. AI in education encompasses various technologies, algorithms, and data used in formal, non-formal, and informal learning contexts. It includes techniques such as data mining, machine learning, natural language processing, and large language models. AI has the potential to enhance learning and teaching by supporting personalized learning, automating assessments, and improving student engagement. However, ethical concerns, data privacy, algorithmic bias, and equity remain significant challenges. The study emphasizes the importance of 'humans in the loop' in AI systems, where human oversight ensures ethical and responsible use. It also highlights the need for stakeholder involvement, including educators, policymakers, and researchers, in the development and implementation of AI in education. The paper recommends strategies for supporting AI in education, including the development of policies, ethical guidelines, and educational practices that promote transparency, accountability, and fairness. The study concludes that AI has the potential to transform education, but its implementation requires careful consideration of ethical, social, and technical factors. Future research should focus on addressing the identified challenges and ensuring that AI is used to enhance, rather than replace, human capabilities in education.This paper explores the implications of artificial intelligence (AI) in education for policymakers, researchers, and practitioners. It presents findings from a Delphi study involving 33 international professionals and in-depth discussions with researchers to identify key trends and challenges in AI deployment in education. The study highlights three critical trends: privacy and ethical use of AI, trustworthy algorithms, and equity and fairness. These trends were also identified as the top challenges in implementing AI in education. The paper outlines policy recommendations and a research agenda to address identified gaps. AI in education encompasses various technologies, algorithms, and data used in formal, non-formal, and informal learning contexts. It includes techniques such as data mining, machine learning, natural language processing, and large language models. AI has the potential to enhance learning and teaching by supporting personalized learning, automating assessments, and improving student engagement. However, ethical concerns, data privacy, algorithmic bias, and equity remain significant challenges. The study emphasizes the importance of 'humans in the loop' in AI systems, where human oversight ensures ethical and responsible use. It also highlights the need for stakeholder involvement, including educators, policymakers, and researchers, in the development and implementation of AI in education. The paper recommends strategies for supporting AI in education, including the development of policies, ethical guidelines, and educational practices that promote transparency, accountability, and fairness. The study concludes that AI has the potential to transform education, but its implementation requires careful consideration of ethical, social, and technical factors. Future research should focus on addressing the identified challenges and ensuring that AI is used to enhance, rather than replace, human capabilities in education.