• The power of beliefs: How Gene...

Treffer: The power of beliefs: How Generative Artificial Intelligence (GenAI) influences academic performance.

Title:
The power of beliefs: How Generative Artificial Intelligence (GenAI) influences academic performance.
Authors:
Zhu W; Business School, Qingdao University of Technology, Qingdao 266520, China., Li T; Business School, Qingdao University of Technology, Qingdao 266520, China., Li Q; School of Economics and Management, Weifang University, Weifang 261061, China. Electronic address: liqj@wfu.edu.cn.
Source:
Acta psychologica [Acta Psychol (Amst)] 2026 Mar; Vol. 263, pp. 106218. Date of Electronic Publication: 2026 Jan 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: North Holland Publishing Country of Publication: Netherlands NLM ID: 0370366 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6297 (Electronic) Linking ISSN: 00016918 NLM ISO Abbreviation: Acta Psychol (Amst) Subsets: MEDLINE
Imprint Name(s):
Publication: Amsterdam : North Holland Publishing
Original Publication: The Hague.
MeSH Terms:
Academic Performance*/psychology , Students*/psychology , Artificial Intelligence*, Humans ; Female ; Male ; Young Adult ; Adult ; Surveys and Questionnaires ; Generative Artificial Intelligence
Contributed Indexing:
Keywords: Belief-Action-Outcome (BAO) model; Generative artificial intelligence (GenAI); Higher education; Mixed-methods approach; Multimotive Information System Continuance (MISC) model
Entry Date(s):
Date Created: 20260113 Date Completed: 20260218 Latest Revision: 20260218
Update Code:
20260218
DOI:
10.1016/j.actpsy.2026.106218
PMID:
41529359
Database:
MEDLINE

Weitere Informationen

The application of Generative Artificial Intelligence (GenAI) in higher education is expanding rapidly. However, little research focusses on the influence of students' beliefs on the application effectiveness of GenAI and their academic performance. This study combines the Belief-Action-Outcome (BAO) model and the Multimotive Information System Continuance (MISC) model to develop a theoretical framework. A mixed-methods approach, specifically the explanatory sequential design, was employed. In the quantitative stage, 724 valid questionnaires from students with GenAI usage experience were collected and the structural equation modeling technology was used. In the qualitative stage, 15 students from diverse backgrounds are interviewed. Three valuable findings are identified. First, design expectation fit has a significant positive impact on beliefs. Second, beliefs have a significant positive influence on continuous use. Third, continuous use has a significant positive effect on task performance and technical performance. The theoretical contribution of this study is that the theoretical model constructed by the research can reflect the whole process of the influence of students' beliefs in new information technology on their academic performance, which can provide practical implications for optimizing GenAI applications in education.
(Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)

Declaration of competing interest No potential conflict of interest was reported by the authors.