Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring Student Engagement and Challenges with ChatGPT for Educational Purposes: The Case of St. Mary's College of Meycauayan
0
Zitationen
2
Autoren
2026
Jahr
Abstract
This study explores student engagement and challenges with ChatGPT for educational purposes at St. Mary’s College of Meycauayan. Technology integration in education has reshaped traditional learning, with Artificial Intelligence (AI) emerging as a pivotal tool, exemplified by ChatGPT. Using a descriptive research method, the study examines first-year college students' perceptions and usage patterns of ChatGPT. Analysis reveals students predominantly utilize ChatGPT for idea generation and clarification, with weekly engagement prevalent. Students generally find ChatGPT beneficial, especially for writing assistance, concept clarification, and language learning. Concerns over information reliability, potential overreliance leading to superficial understanding, and the risk of promoting a copy culture were identified. Students expressed a need for enhancements such as improved context awareness, credible references, visual aids, and multi-language support. Recommendations to optimize ChatGPT usage include parental consent for minors, responsible use guidelines, curriculum integration, and regular monitoring to ensure response accuracy. Balancing AI benefits with critical thinking and independent learning is emphasized. In conclusion, ChatGPT offers significant potential to enhance learning, but careful consideration of limitations and ethical implications is crucial. Adopting prudent policies and promoting responsible usage can leverage AI's transformative power to empower students in the digital era.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.