Artificial intelligence dependency in college students: a critical narrative review
Xuehua He, Shan Li, Rongping Cha, Li Wang, Yunxia Shen, Ruijuan Wu, Lili Wu, Xiaoyan Gong, Jingbang Liu · 2026
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Description
He and co-authors review a risk for college students: leaning too hard on AI tools for study. Their 2026 review pulls together ten years of work on this habit. The team is clear that AI dependency, as they use the term, is not a formal health diagnosis. It means a pattern where students ask AI first, hand over their thinking, and feel stress when no tool is at hand. Rates vary a lot by place, age, and course.
The review finds that the tests used to gauge this habit vary a lot. Some new scales show promise, but the field lacks one shared yardstick. Causes span weak self-control, high work load, peer norms, and tools built to be hard to leave. The team backs skills that teach students to check AI output, not just take it. It also calls for tests and tasks that keep students thinking for themselves.
Catalog record
| CREATOR | Xuehua He, Shan Li, Rongping Cha, Li Wang, Yunxia Shen, Ruijuan Wu, Lili Wu, Xiaoyan Gong, Jingbang Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |