AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study

BACKGROUND: Artificial intelligence (AI) can be beneficial in tailoring learning experiences to each student's learning style, needs, and preferences. But the integration of AI into education has also raised a host of concerns, including AI dependency and brain rot. Thus far, there has been limited research on the relationship between the use of AI and brain rot. PURPOSE: This study aimed to explore the relationship between AI dependency and brain rot, specifically among nursing students. METHODS: This mixed-methods study involved undergraduate nursing students at a university in Türkiye. Quantitative data were collected via an online survey that included a sociodemographic questionnaire, the eight-item Brain Rot Scale (BRS-8), and the Dependence on Artificial Intelligence (DAI) Scale. Qualitative data were collected via semistructured interviews. Quantitative data analysis included the use of descriptive statistics, independent samples t tests, one-way analysis of variance, Pearson correlational analysis, and simple linear regression analysis. Qualitative data were analyzed via thematic analysis. RESULTS: A total of 222 nursing students participated in the quantitative phase and 11 also participated in the qualitative interviews. Of the 222 nursing students in the quantitative phase, 90.1% were female; their mean age was 20.63 years. Quantitative analysis found a moderate, positive, and statistically significant correlation between DAI Scale and BRS-8 scores (r = 0.39). Qualitative analysis indicated that students often relied heavily on AI tools in preparing for or taking examinations, with some reporting that the overuse of AI led to reduced focus and dulled thinking. CONCLUSIONS: This study found a moderate, positive, and statistically significant relationship between AI dependency and brain rot among nursing students. The findings indicate that the excessive use of AI tools may contribute to cognitive fatigue and mental exhaustion, and can negatively impact foundational cognitive skills such as critical thinking and decision-making. It is essential that nursing programs ensure the AI literacy of their students and develop a balance of traditional and AI-related pedagogical strategies that best support students' brain functions and cognitive abilities.

Authors

Institutions

Publication Details

Journal
AJN American Journal of Nursing
Published
2026-08-20
DOI
https://doi.org/10.1097/ajn.0000000000000364
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study

Reyhan Sağlam, Bilge Kalanlar
AJN American Journal of Nursing
Artificial Intelligence in Healthcare and Education
article

AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study

Reyhan Sağlam, Bilge Kalanlar
article en

Abstract

BACKGROUND: Artificial intelligence (AI) can be beneficial in tailoring learning experiences to each student's learning style, needs, and preferences. But the integration of AI into education has also raised a host of concerns, including AI dependency and brain rot. Thus far, there has been limited research on the relationship between the use of AI and brain rot. PURPOSE: This study aimed to explore the relationship between AI dependency and brain rot, specifically among nursing students. METHODS: This mixed-methods study involved undergraduate nursing students at a university in Türkiye. Quantitative data were collected via an online survey that included a sociodemographic questionnaire, the eight-item Brain Rot Scale (BRS-8), and the Dependence on Artificial Intelligence (DAI) Scale. Qualitative data were collected via semistructured interviews. Quantitative data analysis included the use of descriptive statistics, independent samples t tests, one-way analysis of variance, Pearson correlational analysis, and simple linear regression analysis. Qualitative data were analyzed via thematic analysis. RESULTS: A total of 222 nursing students participated in the quantitative phase and 11 also participated in the qualitative interviews. Of the 222 nursing students in the quantitative phase, 90.1% were female; their mean age was 20.63 years. Quantitative analysis found a moderate, positive, and statistically significant correlation between DAI Scale and BRS-8 scores (r = 0.39). Qualitative analysis indicated that students often relied heavily on AI tools in preparing for or taking examinations, with some reporting that the overuse of AI led to reduced focus and dulled thinking. CONCLUSIONS: This study found a moderate, positive, and statistically significant relationship between AI dependency and brain rot among nursing students. The findings indicate that the excessive use of AI tools may contribute to cognitive fatigue and mental exhaustion, and can negatively impact foundational cognitive skills such as critical thinking and decision-making. It is essential that nursing programs ensure the AI literacy of their students and develop a balance of traditional and AI-related pedagogical strategies that best support students' brain functions and cognitive abilities.

AJN American Journal of NursingVol. 126(9)
Yüksek İhtisas Üniversitesi (TR)
Quality Education
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.