Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ChatGPT and Assessment in Higher Education: A Magic Wand or a Disruptor?
17
Zitationen
2
Autoren
2024
Jahr
Abstract
There is a current debate about the extent to which ChatGPT, a natural language AI chatbot, can disrupt processes in higher education settings. The chatbot is capable of not only answering queries in a human-like way within seconds but can also provide long tracts of texts which can be in the form of essays, emails, and coding. In this study, in the context of higher education settings, by adopting an experimental design approach, we applied ChatGPT-3 to a traditional form of assessment to determine its capabilities and limitations. Specifically, we tested its ability to produce an essay on a topic of our choice, created a rubric, and assessed the produced work in accordance with the designed rubric. We then evaluated the chatbot’s work by assessing ChatGPT’s application of its rubric according to a modified version of Paul’s (2005) Intellectual Standards rubric. Using Christensen et al.’s (2015) framework on disruptive innovations, our study found that ChatGPT was capable of completing the set tasks competently, quickly, and easily, like a “magic wand”. However, our findings also challenge the extent to which all of the ChatGPT’s demonstrated capabilities can disrupt this traditional form of assessment, given that there are aspects of its construction and evaluation that the technology is not yet able to replicate as a human expert would. These limitations of the chatbot can provide us with an opportunity for addressing vulnerabilities in traditional forms of assessment in higher education that are subject to academic integrity issues posed by this form of AI. We conclude the article with implications for teachers and higher education institutions by urging them to reconsider and revisit their practices when it comes to assessment.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.436 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.311 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.753 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.523 Zit.