Facilitating pre-service teachers’ instructional design and higher-order thinking with generative AI: an integrated approach with the peer assessment and concept map

The rapid development of generative artificial intelligence has created new opportunities for innovation in teacher education, dramatically changing how pre-service teachers acquire new skills and knowledge. In the information age, pre-service teachers’ information technology-enhanced instructional design competence has received widespread attention. This study introduced the generative artificial intelligence represented by ChatGPT to innovate the traditional teaching mode of pre-service education. Learners use ChatGPT to automatically generate courseware and revise courseware according to the framework provided by concept maps. To promote the depth of thinking and motivation of pre-service teachers in the revision process, we proposed an integrated approach with the peer assessment and ChatGPT. The researchers conducted a quasi-experimental study at a university to investigate the effectiveness of this learning strategy. One class of pre-service teachers (N = 46) was the experimental group using peer assessment-based concept mapping-supported ChatGPT generated content (PA-CGPT), and the other class (N = 45) was the control group using conventional concept mapping-supported ChatGPT generated content (C-CGPT). The study suggests that students in the experimental group exhibited significant improvements in practical skills, learning motivation, higher-order thinking tendencies, satisfaction, and reduced cognitive load. Furthermore, in-depth interviews were conducted with both groups of students to examine their learning outcomes and discuss implications for future research.