From Plagiarism to Critical Thinking: How Students at Different Educational Levels Perceive the Ethical Challenges of Generative AI

Generative artificial intelligence presents educational opportunities alongside ethical challenges that students may perceive differently across educational levels. This study examined perceptions of general educational challenges and the perceived importance of ethical challenges associated with GenAI among 1299 students in Slovenia: 315 primary school students, 535 secondary school students, and 449 university students. Data were collected through an online questionnaire comprising eight general educational challenges and ten ethical challenges. Group differences were examined using Welch’s ANOVA and Games–Howell post hoc tests, while hierarchical multiple regression with cluster-robust inference assessed the additional contribution of educational level beyond gender, self-rated AI knowledge, and familiarity with GenAI. Significant differences emerged for 17 of the 18 challenges, with generally larger effects for ethical challenges. University students consistently assigned greater importance to ethical issues, particularly critical evaluation, plagiarism, and inaccurate AI-generated information. Item-level analyses revealed the largest differences in perceived importance for critical evaluation, plagiarism, and inaccurate AI-generated information. Supplementary exploratory regression analysis indicated that educational level accounted for an additional 15.3% of the variance in the observed composite ethical importance score. The findings highlight differences in how students prioritize GenAI-related ethical challenges, rather than demonstrated ethical competence, and suggest the value of context-sensitive approaches to responsible AI education.

Authors

Institutions

Publication Details

Journal
Education Sciences
Published
2026-11-11
DOI
https://doi.org/10.3390/educsci16111936
Primary Topic
Artificial Intelligence in Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

From Plagiarism to Critical Thinking: How Students at Different Educational Levels Perceive the Ethical Challenges of Generative AI

Marta Licardo, Aleksandra Šindić
Education Sciences
Artificial Intelligence in Education
article

From Plagiarism to Critical Thinking: How Students at Different Educational Levels Perceive the Ethical Challenges of Generative AI

Marta Licardo, Aleksandra Šindić
article en

Abstract

Generative artificial intelligence presents educational opportunities alongside ethical challenges that students may perceive differently across educational levels. This study examined perceptions of general educational challenges and the perceived importance of ethical challenges associated with GenAI among 1299 students in Slovenia: 315 primary school students, 535 secondary school students, and 449 university students. Data were collected through an online questionnaire comprising eight general educational challenges and ten ethical challenges. Group differences were examined using Welch’s ANOVA and Games–Howell post hoc tests, while hierarchical multiple regression with cluster-robust inference assessed the additional contribution of educational level beyond gender, self-rated AI knowledge, and familiarity with GenAI. Significant differences emerged for 17 of the 18 challenges, with generally larger effects for ethical challenges. University students consistently assigned greater importance to ethical issues, particularly critical evaluation, plagiarism, and inaccurate AI-generated information. Item-level analyses revealed the largest differences in perceived importance for critical evaluation, plagiarism, and inaccurate AI-generated information. Supplementary exploratory regression analysis indicated that educational level accounted for an additional 15.3% of the variance in the observed composite ethical importance score. The findings highlight differences in how students prioritize GenAI-related ethical challenges, rather than demonstrated ethical competence, and suggest the value of context-sensitive approaches to responsible AI education.

Education SciencesVol. 16(11)
University of Banja Luka (BA), University of Maribor (SI)
Artificial Intelligence in 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.

Laika access coming soon