Pregledni rad
https://doi.org/10.15516/cje.v28i3.42889
Advancing Learning through Human-Machine Collaboration: Insights from a Meta-Analytic Structural Equation Modelling
Qinglong Zhan
; Tianjin University of Technology and Education, School of Information Technology
*
Xinjian Fu
; Tianjin University of Technology and Education, School of Information Technology
* Dopisni autor.
Sažetak
Abstract
Human-Machine Collaborative Learning (HMCL) has gained increasing attention in education due to its potential to enhance learning outcomes through the integration of artificial intelligence (AI) and advanced technologies. This study employed the meta-analytic structural equation model (MASEM) to explore the relationships among the variables in the HMCL, and the data were derived from 15 studies retrieved. The MASEM results show that ease of use, satisfaction level, and learning motivation all positively and significantly influence academic performance, while student satisfaction positively and significantly affects learning motivation. The path coefficients revealed key relationships: ease of use to academic achievement (β=0.132), satisfaction to learning motivation (β=1.354), satisfaction to academic achievement (β=0.621), and learning motivation to academic achievement (β=0.332). The MASEM results delineate a clear chain, underscoring the complex interplay between affective, motivational, and cognitive dimensions of learning. Future research should focus on developing robust theoretical frameworks and investigating long-term impacts to further advance HMCL integration into diverse educational contexts.
Keywords: artificial intelligence in education; human-machine collaborative learning; influence factor; MASEM; structural equation model
Ključne riječi
artificial intelligence in education; human-machine collaborative learning; influence factor; MASEM; structural equation model
Hrčak ID:
351229
URI
Datum izdavanja:
21.9.2026.
Posjeta: 0 *