Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Navigating the Educational Landscape: Unveiling the impact of ChatGPT in Teacher-Student Dynamics
2
Zitationen
4
Autoren
2024
Jahr
Abstract
Objective: The study aims to explore the role of ChatGPT as an Artificial Intelligence tool in education, critically evaluate its performance, discuss potential problems for students, and propose enhancements to improve teacher-student relations. Method: The research utilizes secondary data gathered from various publications, including magazines, books, journals, and websites relevant to the subject. The study focuses on unveiling the impact of ChatGPT on teacher-student dynamics. Results: ChatGPT offers several advantages in education, including customized learning, constant availability, instant feedback, homework assistance, and support for language learning. It can also enhance engagement and provide consistent quality at a lower cost. However, the study identifies significant drawbacks, such as a lack of emotional intelligence, contextual understanding, and personalization. There are concerns about misinformation, over-reliance on technology, privacy, security, and inadvertent bias reinforcement. To address these issues, the study recommends implementing effective feedback mechanisms, conducting regular security audits, promoting collaborative learning environments, and continuously updating AI models. Conclusions: While ChatGPT has transformative potential in education, its implementation must be carefully managed to avoid depersonalization and reliance on AI at the expense of human interaction. The study emphasizes the importance of balancing AI with the human touch in education, ensuring ethical guidelines, and promoting digital literacy. A collaborative effort among educators, policymakers, and technologists is crucial for responsible and ethical integration of AI in education.
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.