1Departement of Analytical Chemistry, AKA Bogor Polytechnic I Bogor - Indonesia, Indonesia
2Department of Psychology, Indonesia University of Education I Bandung - Indonesia, Indonesia
3Faculty of Psychology, Gadjah Mada University I Yogyakarta - Indonesia, Indonesia
4 Department of Studies and Research in Psychology, Sri Dharmasthala Manjunatheshwara College I Karnataka - India, India
5 Departement of Food Nanotechnology, AKA Bogor Polytechnic I Bogor - Indonesia, Indonesia
6 Department of Agro-Industrial Technology, Faculty of Agro-Industrial Technology, Universitas Padjadjaran I Bandung - Indonesia, Indonesia
BibTex Citation Data :
@article{JP78028, author = {Wiwi Widarsih and Luthfiana Syachfitri and N. Maheshbabu and Muhammad Luthfan Haziman}, title = {Unpacking the negative effects of generative AI on student motivation and procrastination}, journal = {Jurnal Psikologi}, volume = {25}, number = {1}, year = {2026}, keywords = {Academic procrastination; Artificial intelligence; Human resource development; Learning motivation; Self-regulation; Behavioral mediation; AI literacy}, abstract = { Background : The rapid integration of Generative Artificial Intelligence (GenAI) in higher education has reshaped how students engage with academic work. While GenAI improves efficiency and accessibility, concerns arise regarding its effects on cognitive engagement and self regulation. Purpose : This study examined how Ease of Internet Access (EIA) and Frequency of GenAI Use (FGAI) influence Learning Motivation (LM), with Academic Procrastination (AP) as a mediating variable. Method : A total of 205 undergraduate students from Politeknik AKA Bogor completed standardized questionnaires adapted to GenAI-related learning. Data were analyzed using multiple regression, mediation analysis based on Baron and Kenny’s framework, and multi-group confirmatory factor analysis (MGCFA). Findings : The results showed EIA and FGAI did not significantly predict LM (R² = .006; p > .05). EIA significantly predicted AP (β = .146, p = .039), and AP negatively predicted LM (β = −.603, p < .001). Mediation analysis confirmed a significant indirect effect of EIA on LM through AP (Sobel = −2.075, p = .038). MGCFA supported configural and metric invariance across GenAI-use groups (ΔCFI = .003), with partial scalar invariance achieved. Implication : These findings indicate that digital accessibility may indirectly reduce motivation by increasing procrastination, emphasizing the importance of self-regulation and guided AI integration in higher education. }, issn = {2302-1098}, pages = {35--51} doi = {10.14710/jp.25.1.35-51}, url = {https://ejournal.undip.ac.id/index.php/psikologi/article/view/78028} }
Refworks Citation Data :
Background: The rapid integration of Generative Artificial Intelligence (GenAI) in higher education has reshaped how students engage with academic work. While GenAI improves efficiency and accessibility, concerns arise regarding its effects on cognitive engagement and self regulation.
Purpose: This study examined how Ease of Internet Access (EIA) and Frequency of GenAI Use (FGAI) influence Learning Motivation (LM), with Academic Procrastination (AP) as a mediating variable.
Method: A total of 205 undergraduate students from Politeknik AKA Bogor completed standardized questionnaires adapted to GenAI-related learning. Data were analyzed using multiple regression, mediation analysis based on Baron and Kenny’s framework, and multi-group confirmatory factor analysis (MGCFA).
Findings: The results showed EIA and FGAI did not significantly predict LM (R² = .006; p > .05). EIA significantly predicted AP (β = .146, p = .039), and AP negatively predicted LM (β = −.603, p < .001). Mediation analysis confirmed a significant indirect effect of EIA on LM through AP (Sobel = −2.075, p = .038). MGCFA supported configural and metric invariance across GenAI-use groups (ΔCFI = .003), with partial scalar invariance achieved.
Implication: These findings indicate that digital accessibility may indirectly reduce motivation by increasing procrastination, emphasizing the importance of self-regulation and guided AI integration in higher education.
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