Effects of integrating self-regulation scaffolding supported by chatbot and online collaborative reflection on students’ learning in an artificial intelligence course

Artificial intelligence (AI) includes complex concepts and could be difficult for non-computer or information students, and they may experience difficulties in an AI course. In order to develop students' AI skills, regulate their academic stress, and reduce student loneliness, the researchers in this study integrated self-regulation scaffolding (supported by a chatbot designed in this study) with online collaborative reflection, and investigated their effects on students' learning. The researchers conducted a quasi-experiment to explore the effects of self-regulation scaffolding and online collaborative reflection. The participants in this experiment were 116 undergraduates from three classes (groups) of a non-computer department taking a compulsory course titled ‘Introduction to Artificial Intelligence’. The experimental groups in this study included the first class (G1) simultaneously receiving the interventions of self-regulation scaffolding and online collaborative reflection, as well as the second class (G2) only receiving the intervention of self-regulation scaffolding. The last class (G3), that received a traditional teaching method (non-self-regulation scaffolding and non-online collaborative reflection) in an online AI course, served as the control group. According to the statistical analysis in this study, the self-regulation scaffolding approach in G2 significantly promoted participants' AI skills and fostered their ability to regulate academic stress compared to the control group. However, G1 students who received concurrent online collaborative reflection did not demonstrate the expected effects of enhancing their learning compared with those (G2) who did not receive it. This study represents an early attempt to implement self-regulation scaffolding and online collaborative reflection integrated with chatbot in an online AI course. The results of this study could serve as a reference for online course designers, educators, and scholars planning to adopt these teaching methods, especially for those focusing on implementing online AI education and educational technologies (such as chatBot).