With the rapid development of artificial intelligence (AI) in recent years, there has been an increasing number of studies on integrating AI in various educational contexts, ranging from early childhood to higher education. Although systematic reviews have widely reported the effects of AI on teaching and learning, limited reviews have examined and defined responsible AI in education (AIED). To fill this gap, we conducted a convergent systematic mixed studies review to analyze key themes emerging from primary research. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and identified 40 empirical studies that satisfied our inclusion criteria. Specifically, we used four criteria for the screening process: (1) the study's full text was available in English; (2) the study was published before April 10th, 2024 in peer-reviewed journals or conference proceedings; (3) the study was primary research that collected original data and applied qualitative, quantitative, or mixed-methods as the study methodology; and (4) the study had a clear focus on ethical and/or responsible AI in one or multiple educational context(s). Our findings identified essential stakeholders and characteristics of responsible AI in K-20 educational contexts and expanded understanding of responsible human-centered AI (HCAI). We unveiled characteristics vital to HCAI, encompassing Fairness and Equity, Privacy and Security, Non-maleficence and Beneficence, Agency and Autonomy, and Transparency and Intelligibility. In addition, we provided suggestions on how to achieve responsible HCAI via collaborative efforts of stakeholders, including roles of users (e.g., students and educators), developers, researchers, and policy and decision-makers.
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