Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Vocational, Informatics and Computer Education Journal of Education Psychology and Social Development
Yundong Wu
Higher Education, Psychology, and Policy Analysis Laboratory, Naju, South Korea
Humanities Computer Science & IT Education Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Artificial intelligence and university students' well-being: An integrative narrative review of pathways, benefits, risks, and future directions Yundong Wu
Journal of Education Psychology and Social Development Vol 2, No 2 (2026): December 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/jepsi.v2i2.1244

Abstract

This integrative narrative review synthesizes research published primarily between 2010 and 2026 on artificial intelligence (AI) in higher education and its potential implications for university students' well-being. Student well-being is conceptualized across emotional, psychological, social, academic, and digital dimensions, and evidence is interpreted by distinguishing direct mental-health outcomes from indirect pathways involving academic self-efficacy, access and help-seeking, social connection, agency and digital well-being, and institutional inclusion. The evidence indicates potential benefits, including adaptive learning support, low-threshold psychoeducation, and improved navigation of support, but these benefits are unevenly demonstrated and often rely on short-term, self-report, feasibility, or indirect outcomes. Risks include technostress, clinical ambiguity, privacy breaches, dependency, algorithmic bias, surveillance, and social displacement. Building on, but conceptually distinct from, recent systematic reviews of AI and college student mental health, this review develops an author-generated, context-sensitive pathway framework that makes positive and negative branches, contextual moderators, human oversight, and feedback processes explicit. Future research should prioritize longitudinal and comparative designs, direct well-being outcomes, culturally responsive evaluation, equity, and responsible non-use.
Generative AI-assisted instruction and college students’ academic self-efficacy, computational thinking, and critical thinking disposition: A six-week pretest-posttest study Jiani Wu; Yundong Wu; Ruofan Lin; Ying Chen; Shun Guo; Tingting Lv; Weijian Kong
Journal of Vocational, Informatics and Computer Education Vol 4, No 3 (2026): September 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i3.1412

Abstract

Purpose – Generative artificial intelligence (AI) is increasingly used in higher education, but evidence about how guided generative AI use influences broad student competencies remains limited. Methods – This study examined a six-week generative AI-assisted instructional program designed around generation, inspection, verification, and independent application, focusing on academic self-efficacy, computational thinking, and critical thinking disposition among 286 college students in Fujian Province, China. A one-group pretest-posttest design was used. Academic self-efficacy was assessed with a 28-item scale, computational thinking with the 29-item Computational Thinking Scales, and critical thinking disposition with the California Critical Thinking Disposition Inventory. Descriptive statistics, reliability analysis, Pearson correlations, and paired-samples t-tests were conducted.Results – All three major outcomes increased significantly. Academic self-efficacy increased from 3.31 to 3.61, computational thinking from 3.28 to 3.57, and critical thinking disposition from 3.36 to 3.63, with moderate paired effect sizes (d = 0.47–0.54).Originality/Value – The study applies a structured four-stage AI-learning process to examine self-efficacy, computational thinking, and critical thinking disposition together rather than treating AI use as a simple exposure condition. Research Implications – The findings support further examination of guided AI use as a form of technology-assisted scaffolding while highlighting the need to distinguish AI-supported learning from non-AI structured scaffolding. Because the study used a one-group design without a control group, the observed changes cannot be attributed to generative AI alone.
Co-Authors Jiani Wu Ruofan Lin Shun Guo Tingting Lv Weijian Kong Ying Chen