Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence in higher education learning: transferable skills and academic integrity
1
Zitationen
3
Autoren
2025
Jahr
Abstract
The advancement of Generative Artificial Intelligence (AI) chatbots, such as ChatGPT, presents significant and transformative challenges in higher education teaching and learning, such as assessment and evaluation practices. While this is acknowledged, there has been very little research into what this might look like in daily practice in higher education. This study explored these challenges in one area of higher education practice: developing students’ transferable skills, including writing, critical thinking, and information literacy among undergraduate engineering students at RMIT University, Melbourne, Australia. Using a cohort comparison design, this study evaluated the impact of ChatGPT on students' attainment of transferable skills. The effectiveness of AI tools in enhancing educational outcomes was assessed with a standardised assessment framework used by independent assessors to grade students’ reports. The results, analysed using the Mann-Whitney U test and the intraclass correlation coefficient, revealed significant improvements in critical thinking and information literacy among those students who used ChatGPT. The study also explored the ethical implications of using AI in educational settings and highlighted the need for rigorous academic standards and the implementation of measures to ensure the responsible use of AI technologies. While the preliminary findings suggest that AI tools, particularly ChatGPT in this study, can positively impact certain students’ skills, more detailed and controlled studies are necessary to validate these results and explore further the mechanisms through which AI tools influence learning and skill development.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.324 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.189 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.588 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 · 5.470 Zit.