The use of digital technologies has been evolving and (re)placing in many disciplines, including education. The questions and the concerns related to the quality and accessibility of education for all made smart technologies visible to discuss, situate, and refine (Andriessen & Sandberg, 1999). Recently, a considerable amount of attention has been given to Artificial Intelligence (AI) applications in education as creating smart learning environments to include all children (Drigas & Ioannidou, 2012; Organisation for Economic Co-operation and Development [OECD], 2018; United Nations Educational, Scientific and Cultural Organization [UNESCO], 2019).

Artificial Intelligence (AI) is a broad term having a fifty-years past (Buchanan, 2005). Although there is no precise definition of the AI, the researchers give some explanation to provide a common-sense about AI. For example, Baker and Smith (2019) define AI as “the computers which perform cognitive tasks, usually associated with human minds, particularly learning and problem-solving” (p. 10). Similarly, Luckin and colleagues (2016) claim that “AI involves computer software that has been programmed to interact with the world in ways normally requiring human intelligence.” (p. 18). Therefore, AI may include a range of models and technologies rather than a single form of technology (Baker & Smith, 2019). To explain, some technologies and models such as Machine Learning (ML), data mining, Natural Language Processing (NLP), neural networks, agents, and robotics are considered as the associated fields in AI (Alimisis et al., 2007; Baker & Smith, 2019; Baylor, 1999; McAlister & Wermter, 1999; Popenici & Kerr, 2017; Russel & Norvig, 2010).

Even though the idea of using AI was started fifty years ago, the history of research on AI in Education (AIEd) started 30 years ago (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019; Luckin, Holmes, Griffiths, & Forcier, 2016). As awareness and the use of AI applications in the education system, the related discussions have been emerging to explore the possible opportunities and risks of AI in/for education. For example, some researchers focus on the ethical, social, and economic aspects of AI, as wells as the possible opportunities of AI brought (Bundy, 2017). For example, Accordingly, for connecting people interested in AI in Education, international societies have been gathered, conferences have been organized, and journals have been published (Nye, 2015; OECD, 2018). Therefore, it seems crucial to critically review the research studies conducted in the last decade. To serve this purpose, three research questions were formed to present a synthesis of state of the art regarding the use of artificial intelligence for teaching and learning of children (from kindergarten to high school) in the last ten years (between 2010-2020).

1)    What is the distribution of the publication years, geographical location, subject matter domains of the studies in AIEd research for children?

2)    What are the sample characteristics of the studies focused on AIEd research for children?

3)    What is the nature and scope of AI systems AIEd research for children?

In order to answer the questions mentioned above, a systematic review of the recent and relevant studies was conducted. In the end, further discussions and recommendations will be presented.

This study employs a systematic review identifying inclusion and exclusion criteria so that one can replicate in a systematic way (Gough, 2015). In this systematic review, electronic databases such as Web of Science, ERIC, and EBSCO were used. Keywords were determined as ((TOPIC: (artificial intelligence) AND TOPIC: (children)) AND TOPIC: (education)). The timespan was set as 2010-2020. The initial search revealed 49 papers. Moreover, exclusion criteria were applied for the studies if they are not in English and not a journal article. Then, 24 journal articles were obtained and reviewed in detail. After reading each article carefully, it was seen that three articles were conducted to college students/adult learners, four articles were review articles, two were position statement papers, and three were reflection papers on AI in education. As a result, this contribution syntheses a total of 12 journal articles published on artificial intelligence in or for the education of children.

Findings revealed a growth in the articles was observed from 2013 to 2019 and most of the articles were published in 2019 (n=5). All AIEd research articles come from USA, China, Morocco, and India. However, the USA (n=9) contributed to 75% of the AIEd research. Moreover, six main areas such as language development (e.g., reading skills and vocabulary learning), artistic skills (e.g., music), STEM, cognition or cognitive development (e.g., reasoning skills, behavioral functioning, and metacognition), social and emotional development, and self-care skills were contributed by the AIEd research. Giving importance to the AI in children’s education, the number of the related studies is expected to increase. Moreover, although the USA is leading the AI research on children’s education, the countries in the EU may create funds and support the AI researchers for their projects. Second, some researchers worked with children without special needs (n=5) while six studies included children with special needs such as hearing difficulties, Autism Spectrum Disorder (ASD), Learning Disabilities (LDs), Attention-Deficit/Hyperactivity Disorder (ADHD). One study included both groups. It is exciting and promising to be able to report that during the last decade, a considerable number of studies have been addressing the use AI in the education of children including with and without special needs. Finally, some trend technologies such as AI-supported Virtual Learning Environments (VLE), machine learning, Support Vector Machine (SVM), social robot, smart-glasses technology, and smart-glasses based augmented reality system were benefited in AIEd research. Moreover, two categories have emerged for the scope of the AI research: using AI tools for educating/training children OR using AI tools and systems to accomplish some tasks such as assessment of children’s progression and diagnosing of children’s developmental levels.

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