How Do Personal Attributes Shape AI Dependency in Chinese Higher Education Context? Insights from Needs Frustration Perspective

The adoption of Generative AI in education presents both opportunities and challenges, particularly regarding its potential to foster student dependency. However, the psychological drivers of this dependency remain unclear. This study addresses this gap by applying the Interaction of Person-Affect-C...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-11, Vol.19 (11), p.e0313314
Main Authors: Zhong, Wenjun, Luo, Jianghua, Lyu, Ya
Format: Article
Language:English
Subjects:
Academic achievement
Addictive behaviors
Adolescent
Adult
Analysis
Anxiety
Artificial Intelligence
Biology and Life Sciences
Chatbots
China
Cognition
Cognition & reasoning
Colleges & universities
Coping
East Asian People
Education
Education, Higher
Educational aspects
Emotions
Female
Forecasts and trends
Frustration
Generative artificial intelligence
Higher education
Humans
Impulsive Behavior
Impulsivity
Influence
Innovations
Male
Mediation
Medicine and Health Sciences
Neurosis
Neuroticism
Perfectionism
Personality
Personality traits
Social Sciences
Stress
Students
Students - psychology
Technology application
Universities
Well being
Young Adult
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The adoption of Generative AI in education presents both opportunities and challenges, particularly regarding its potential to foster student dependency. However, the psychological drivers of this dependency remain unclear. This study addresses this gap by applying the Interaction of Person-Affect-Cognition-Execution (I-PACE) model and Basic Psychological Needs (BPN) theory to explore how specific personality traits-neuroticism, self-critical perfectionism, and impulsivity-contribute to AI dependency through needs frustration, negative academic emotions, and reinforced performance beliefs. Data were collected from 958 university students (Mage = 21.67) across various disciplines. Structural equation modeling (SEM) was used to analyze the relationships among the variables. Neuroticism, self-critical perfectionism, and impulsivity were found to be significantly associated with increase needs frustration and negative academic emotions, which in turn reinforced students' positive beliefs about performance of AI tools, deepening their dependency. The study also uncovered complex serial mediation effects, highlighting intricate psychological pathways that drive maladaptive AI use. This research provides a critical insight into the interplay between personality traits and technology use, shedding light on the nuanced ways in which individual differences influence dependency on generative AI. The findings offer practical strategies for educators to promote balanced AI use and support student well-being in educational settings.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0313314