Educational Approaches with AΙ in Primary School Settings: A Systematic Review of the Literature Available in Scopus

• Published in: Education sciences Vol. 14; no. 7; p. 744
• Main Authors: Aravantinos, Spyridon, Lavidas, Konstantinos, Voulgari, Iro, Papadakis, Stamatios, Karalis, Thanassis, Komis, Vassilis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Active Learning; AI applications; AI education; Analysis; Artificial intelligence; Computational linguistics; Curriculum Design; Data Analysis; Digital literacy; Education; Educational Environment; Educational Objectives; Experiential learning; Individualized Instruction; kindergarten; Language processing; Learner Engagement; Learning Activities; Learning Processes; Literature Reviews; Machine learning; Methods; Natural language interfaces; preschool; Primary Education; primary school; Professional development; Project-based learning; Research Methodology; Students; Teaching; Teaching Methods; Young Children; United Kingdom; China
• ISSN: 2227-7102; 2227-7102
• DOI: 10.3390/educsci14070744

As artificial intelligence (AI) becomes increasingly prevalent, it has become a topic of interest in education. The use of AI in education poses complex issues, not only in terms of its impact on teaching and learning outcomes but also in terms of the ethical considerations regarding personal data and the individual needs of each student. Our study systematically analyzed empirical research on the use of AI in primary education, specifically for children aged 4–12 years old. We reviewed 35 articles indexed in SCOPUS, filtered them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzed them, and categorized the findings. The research focused on the studies’ objectives, learning content, learning outcomes, learning activities, and the pedagogy of activities or the AI tools. Our categorization resulted in three main categories of research objectives regarding the creation, implementation, and evaluation of AI tools and five categories for learning content: AI and ML (machine learning) concepts in STEM and STEAM, language learning, mathematics, arts, and various other subjects. The learning activities were split into four categories: apply, engage, interact, use; project-based learning with multiple activities; experience and practice; and students as tutors. The learning outcomes were split into three levels: cognitive, affective, and psychomotor. The pedagogy of AI tools falls into four categories: constructivism, experiential learning, AI-assisted learning, and project-based learning. The implications for teacher professional development are discussed.