The effects of three different approaches to human–AI collaboration on online collaborative learning
Year: 2025
Type: article
Abstract: Online collaborative learning has the potential to help learners of all cultures and languages in the artificial intelligence (AI) age. However, studies on the use of human–AI collaboration to promote online collaborative learning are lacking. This study attempts to fill this gap by examining the effects of three approaches to human‒AI collaboration on online collaborative learning performance, namely, the AI feedforward and feedback (AIFF) approach, the AI partner (AIP) approach, and a combined AI feedforward and feedback and AI partner (AIFFP) approach. The study was conducted at a public university in Ghana. A total of 100 college students participated in the study for two months. Both quantitative and qualitative methods were employed to analyze the data. The findings imply that the AIFFP approach had more substantial impacts on collaborative knowledge building, group performance, cognitive engagement, and socially shared regulation (SSR) behaviors than the AIFF approach, the AIP approach, and the conventional online collaborative learning (COCL) approach. The findings and their practical and technical implications are discussed in depth. This research suggests that educators and institutions should use human‒AI collaboration to increase performance and efficiency.
Author: Juliana Fosua Gyasi, Lanqin Zheng, Stephen Frank Love, Francis Ohene Boateng
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6927452039