A generative artificial intelligence-enhanced multiagent approach to empowering collaborative problem solving across different learning domains

Abstract

Collaborative problem-solving skills are among the most important skills in the 21st century. However, learners exhibit significant deficiencies in terms of their collaborative problem-solving skills. Emerging artificial intelligence (AI) technologies have given rise to transformative opportunities to facilitate collaborative problem solving through the introduction of adaptive learning mechanisms in educational settings. This study proposes a generative artificial intelligence (GenAI)-enhanced multiagent approach that aims to promote collaborative problem solving across different learning domains. The study also examines the effectiveness of this GenAI-enhanced multiagent approach to collaborative problem solving. In total, 234 college students participated in two empirical studies that focused on different tasks but had the same purpose and procedure. Experimental group 1 engaged in collaborative problem-solving with the assistance of a GenAI-enhanced multiagent approach. Experimental group 2 engaged in collaborative problem solving via a chatbot-based approach. The control group engaged in traditional collaborative problem-solving without any support. Data were collected through the pretest, posttest, and collaborative problem-solving process records and interview records. Both quantitative and qualitative methods were employed to analyze the data. The results indicated that compared with chatbot-based and traditional approaches, the GenAI-enhanced multiagent approach had more significant effects on learning achievements, knowledge elaboration, and collaborative problem-solving performance and skills. The implications of these findings are discussed in depth with the goal of advancing the use of GenAI to empower collaborative problem solving.