From Plagiarism to Critical Thinking: How Students at Different Educational Levels Perceive the Ethical Challenges of Generative AI
Year: 2026
Type: article
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.
Source: Education Sciences
Authors: Marta Licardo, Aleksandra Šindić
Institutions: University of Maribor, University of Banja Luka
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7225800323