Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study
46
Zitationen
1
Autoren
2024
Jahr
Abstract
This study examined and categorized the views of students and teachers in Philippine Higher Education Institutions (HEIs) and correlated those with similar patterns of views regarding ChatGPT usage. While previous studies explored ChatGPT's risks and benefits, a significant gap remains in understanding how college students and educators perceive this technology for its culturally relevant and equitable integration into teaching and learning. Using Q-Methodology, 27 participants (15 students and 12 professors) were surveyed, interviewed, and invited to rank-order 36 statements about using ChatGPT. Three factors emerged from using factor analysis: Ethical Tech Guardians, Balanced Pedagogy Integrators, and Convenience-Embracing AI Enthusiasts. Findings revealed diverse perceptions among college students and teachers regarding ChatGPT integration, identifying distinct factors reflecting ethical, innovative, and useful viewpoints. Recommendations are put forth to optimize ChatGPT and similar AI technologies in educational settings, including clear guidelines for assessment, cross-cultural studies, policy advocacy for ethical regulations, and further research on ethical considerations and cultural sensitivity. • Ethical ChatGPT use requires addressing moral issues, institutional support, cultural sensitivity, and technological hurdles. • Striking a harmonious balance between innovation and traditional pedagogical approaches is paramount. • Considering local perspectives on AI's impact promotes fair access to digital learning tools across diverse communities. • ChatGPT integration offers pros and cons in education, affecting teaching, assessment, and tech access across many fields.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 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 · 5.478 Zit.