This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role‐playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness.

Artificial intelligence (AI) education is increasingly being recognized as essential at the K–12 level. For better understanding teachers’ preparedness for AI education and effectively developing relevant teacher training programs, teachers’ technological pedagogical content knowledge (TPACK) readiness and attitudes toward AI teaching must be determined. However, limited research has been conducted on this topic. To address this research gap, we recruited 1,664 K–12 teachers to obtain a comprehensive view of teachers’ readiness for and attitudes toward teaching AI in K–12 classrooms. These teachers differed in terms of their gender, teaching subject, teaching grade, teaching experience, and experience in teaching AI. The findings of this study indicated that a substantial gap exists in the AI-related content and technological knowledge of the recruited teachers. Moreover, intriguing relationships were found between the teachers’ pedagogical knowledge, content knowledge, and attitudes toward teaching AI. The effects of demographic factors on the teachers’ TPACK and attitudes were also examined. On the basis of the findings of this study, recommendations were formulated for developing effective teacher professional development programs in the field of AI education.