Promoting young learners’ computational thinking with AI-based robots and the ICAP model in blended learning contexts

Fostering young learners' computational thinking in AI-based robots is a crucial issue in educational settings. However, there was the complexity of technology in supporting learning activities and the gender gap issue, so an appropriate strategy is needed to guide students to achieve the learning goals. In this study, an AI (Artificial Intelligence)-based robot with the ICAP model was proposed to investigate students' learning motivation, learning satisfaction, computational thinking, and gender issues while creating AI-based robots in a blended learning environment. To evaluate the effectiveness of the proposed approach, this study employed the quasi-experiment. The experimental group students learned with the AI-based robot learning approach with the ICAP model, while the control group students learned with the conventional AI-based robot learning approach. The results showed that the proposed learning approach significantly improved students' intrinsic and extrinsic motivation, learning satisfaction, and computational thinking. However, there was no significant interaction between group and gender across all variables. It showed that the ICAP model successfully promoted female students to improve their intrinsic and extrinsic motivation, learning satisfaction, and computational thinking in AI-based robots. So, there was no significant difference in gender in the experimental group which employed AI-based robots with the ICAP model.