Promoting conceptual changes of artificial intelligence with technology-facilitated situational exploration and alternative thinking: a dual-situated learning model-based two-tier test approach

Abstract

ABSTRACT In the artificial intelligence (AI) era, K-12 AI education has attracted great attention. With the increasing prevalence of AI, students are likely to have misconceptions about AI starting in elementary school. Hence, it is crucial to explore an effective approach to change elementary students' misconceptions and facilitate their scientific understanding of AI. The two-tier test is an effective method to diagnose students' misconceptions, but it needs to be combined with specific learning models to further promote conceptual change in teaching practice. The dual-situated learning model (DSLM) is an instructional approach used to facilitate students' conceptual change. Therefore, this study proposed a DSLM-based two-tier test (DSLM-TT) approach for AI courses, and explored its effects on elementary students' conceptual change, learning motivation, and self-efficacy in AI courses. 160 sixth-grade students participated in this experiment: the experimental group adopted the DSLM-TT approach, while the control group adopted the conventional learning model-based two-tier test approach (CLM-TT). Results showed that the DSLM-TT group performed better on conceptual change of AI, learning motivation, and self-efficacy than the CLM-TT group. Moreover, the interview results revealed two groups' learning experiences and perceptions. Accordingly, this study can provide a baseline for future research on conceptual change in AI education.