Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Investigating the impacts of Heavy Reliance on AI on Students’ Critical Thinking and Originality: A Qualitative Study of Students’ Perceptions
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This qualitative study explores students' perceptions of Artificial Intelligence (AI) in the educational process, focusing particularly on its influence on their creativity and originality due to overreliance. As AI technology becomes increasingly integrated into educational landscapes, it is crucial to understand its impacts on students’ creative engagement and originality of work for skilled and effective educational practices. The research employed semi-structured interviews, conducted at Government College University (GCUF), with ten undergraduate students who were conveniently selected to be sampled. The data were analyzed by thematic method to meet the research questions. The findings show that while students find AI helpful for managing workload and improving efficiency, they are also concerned that it reduces cognitive effort and hinders creativity. Dependence on AI is driven by academic pressure, deadlines, peer influence, and easy access to quality work. Although most students are aware of the risks, few have clear strategies to preserve originality. The study concludes by recommending the integration of AI literacy programs, the redesign of assessments to encourage critical thinking, and the fostering of academic environments that prioritize creativity and originality. Although the study’s generalizability is limited by its small sample size and single-institution scope, it highlights the urgent need to balance technological assistance with students' intellectual and cognitive development.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.250 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.109 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.482 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.434 Zit.