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Peer-reviewedCC BYScientific Reports

AI usage patterns and self-rated critical thinking among Chinese college students: the moderating role of academic motivation

  • Jing Zhang1
  • Yang Yang2
  1. 1Shandong Vocational Animal Science And Veterinary College
  2. 2Dongshin University
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Abstract

This study explores the associations between artificial intelligence (AI) usage patterns and perceived critical thinking among university students in China, with a focus on three types of AI use: Information Retrieval Use (IRU), Content Generation Use (CGU), and Text Revision Use (TRU). Grounded in Self-Determination Theory (SDT), the research investigates how academic motivation moderates these relationships. Cognitive Load Theory (CLT) provides an interpretive lens for understanding the observed patterns, though load was not directly measured. A cross-sectional survey was conducted with 342 undergraduate students, collecting data on their AI usage behaviors, academic motivation, and perceived critical thinking dispositions. The results reveal that IRU is positively associated with perceived critical thinking, while CGU shows a negative association. TRU showed no significant association. Regarding moderation effects, intrinsic motivation strengthened the positive association between IRU and perceived critical thinking, but, contrary to expectations, also amplified the negative association between CGU and perceived critical thinking. In contrast, extrinsic motivation buffered the negative association of CGU: at high levels of extrinsic motivation, the negative association between CGU and perceived critical thinking was entirely eliminated. These findings indicate that the cognitive correlates of AI use depend on both usage patterns and motivational factors. The study highlights the importance of considering both the type of AI use and students’ motivational orientation when designing educational strategies for AI integration in higher education.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026usage,
  title = {{AI usage patterns and self-rated critical thinking among Chinese college students: the moderating role of academic motivation}},
  author = {Jing Zhang and Yang Yang},
  journal = {Scientific Reports},
  year = {2026},
  month = jul,
  doi = {10.1038/s41598-026-62466-7},
  url = {https://doi.org/10.1038/s41598-026-62466-7},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AI usage patterns and self-rated critical thinking among Chinese college students: the moderating role of academic motivation
AU  - Jing Zhang
AU  - Yang Yang
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-62466-7
UR  - https://doi.org/10.1038/s41598-026-62466-7
AB  - This study explores the associations between artificial intelligence (AI) usage patterns and perceived critical thinking among university students in China, with a focus on three types of AI use: Information Retrieval Use (IRU), Content Generation Use (CGU), and Text Revision Use (TRU). Grounded in Self-Determination Theory (SDT), the research investigates how academic motivation moderates these relationships. Cognitive Load Theory (CLT) provides an interpretive lens for understanding the observed patterns, though load was not directly measured. A cross-sectional survey was conducted with 342 undergraduate students, collecting data on their AI usage behaviors, academic motivation, and perceived critical thinking dispositions. The results reveal that IRU is positively associated with perceived critical thinking, while CGU shows a negative association. TRU showed no significant association. Regarding moderation effects, intrinsic motivation strengthened the positive association between IRU and perceived critical thinking, but, contrary to expectations, also amplified the negative association between CGU and perceived critical thinking. In contrast, extrinsic motivation buffered the negative association of CGU: at high levels of extrinsic motivation, the negative association between CGU and perceived critical thinking was entirely eliminated. These findings indicate that the cognitive correlates of AI use depend on both usage patterns and motivational factors. The study highlights the importance of considering both the type of AI use and students’ motivational orientation when designing educational strategies for AI integration in higher education.
SN  - 2045-2322
ER  -