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Abstract Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality education, creating personalized learning experiences, and supporting data-driven decision-making. One outcome of using AI and Information Technology (IT) systems in sustainable education is the ability to provide students with personalized learning experiences that cater to their unique learning styles and preferences. Additionally, AI systems can provide teachers with data-driven insights into student performance, emotions, and engagement levels, enabling them to tailor their teaching methods and approaches or provide assistance or intervention accordingly. However, the use of AI and IT systems in sustainable education also presents challenges, including issues related to privacy and data security, as well as potential biases in algorithms and machine learning models. Moreover, the deployment of these systems requires significant investments in technology and infrastructure, which can be a challenge for educators. In this review paper, we will provide different perspectives from educators and information technology solution architects to connect education and AI technology. The discussion areas include sustainable education concepts and challenges, technology coverage and outcomes, as well as future research directions. By addressing these challenges and pursuing further research, we can unlock the full potential of these technologies and support a more equitable and sustainable education system.

Human-guided machine learning can improve computing intelligence, and it can accurately assist humans in various tasks. In education research, artificial intelligence (AI) is applicable in many situations, such as predicting students’ learning paths and strategies. In this study, we explore the benefits of repetitive practice of short-answer questions could enhance students’ long-term memory for subsequent improvements in learning performance. However, frequent authoring questions and grading requires teachers’ professionalism, experience, and considerable efforts. Therefore, this study using modern AI technologies, specifically natural language processing, to provide Automatic question generation (AQG) solution, a combined semantics-based and syntax-based question generation system: Hybrid automatic question generation (Hybrid-AQG) was proposed in this study. We assessed its functionality and student learning performance by asking 91 students to complete short-answer questions and then applied a process similar to the Turing test to evaluate the question and grading quality. The results demonstrated that modern AI technologies can generate highly realistic short-answer questions because: (1) Compared with the control group, the experimental group exhibited significantly better learning performance, implying that students acquired long-term memory of course knowledge through repetitive practice with machine-generated questioning. (2) The experimental group could better distinguish machine-generated and expert-authored questions. Nevertheless, both groups in distinguishing questions presented like guessing. (3) Machine grading was deficient in some respects; but the way students answer questions can be adapted for machine understanding through repetitive practice.