Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

Abstract

In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

“…In a scoping review, Su et al (2023) have identified various teaching methods in AI classrooms, such as inquiry-based learning, project-based learning, and game-based learning. In another review by Yue et al (2022), direct instruction has remained a most popular pedagogy in delivering AI concepts, whereas hands-on, experiential activities are often incorporated to enhance student engagement. From the literature review, while most of current pedagogical approaches involves applying established pedagogical framework in AI classrooms, there appears to be a limited focus on the development of domain-specific pedagogy tailored for upper primary students.…”

AI literacy in K-12: a systematic literature review

“…The acquisition of knowledge in AI represents a great pedagogical challenge for both experts and teachers, and a cognitive challenge for students (Micheuz, 2020). Some countries are making significant efforts to promote AI education in K-12 (Touretzky et al, 2019b), by developing relatively comprehensive curriculum guidelines (Yue et al, 2021). Through interviews with practitioners and policy makers from three different continents (America, Asia and Europe), some studies report on continuing works to introduce AI in K-12 education (He et al, 2020).…”

“…Through interviews with practitioners and policy makers from three different continents (America, Asia and Europe), some studies report on continuing works to introduce AI in K-12 education (He et al, 2020). Some other work focuses on examining and comparing AI curricula in several countries (Yue et al, 2021). In addition, there are a growing number of AI training experiences that explore pathways to optimize AI learning for K-12 students.…”

“…However, in light of the recent need to consider how AI education should be approached at the K-12 level (Kandlhofer et al, 2016;Long & Magerko, 2020;Miao et al, 2021;Touretzky et al, 2019b), it would be of great value to structure and characterize the different approaches used so far to develop AI literacy in K-12, as well as to identify research gaps to be explored. Recently, Yue et al (2022) analyzed the main components of the pedagogical design in 32 empirical studies in K-12 AI education and Su et al (2022) examined 14 learning experiences carried out in the Asian-Pacific region. These components included target audience, setting, duration, contents, pedagogical approaches to teaching, and assessment methods.…”

“…Approaches to curriculum development differ widely, ranging from the product-centered model (technicalscientific perspective) to the process-centered model (learner perspective) (Yue et al, 2021). AI literacy can be launched in primary and secondary education depending on the age and computer literacy of the students.…”