Chat GPT challenges in Higher Education assessment methodologies

Date

2023-11-28

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The International Academic Forum (IAFOR)
Language
English

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Abstract

We are currently experiencing one of the most challenging revolutions, the technological one, in which universities, traditionally seen as the center of innovation, have difficulty keeping up. Chat GPT (Generative Pre-Trained Transformer) is based on a language model based on deep learning, one of the branches of artificial intelligence (AI). This platform uses an algorithm based on neural networks, which allows computers to be taught to process data in a way that is “analogous” to the human brain, enabling conversations with the user by processing a large volume of data. In this context, there are several challenges that (Chat GPT) poses to higher education. We will certainly have to reinvent ourselves in teaching and assessment methodologies. We intend to carry out a reflection on the use of this technology in Higher Education in Law, seeking to understand the performance of this tool in “teaching to learn” and in the assessment of the skills that are intended to be acquired. We will take a look at the main uses of GPT Chat in Higher Education, namely as a “learning assistant”, allowing us to understand concepts and answer specific questions, helping to consolidate knowledge; as a “writing tool”, improving writing skills; as a “source of information” enhancing research and, consequently, the expansion of knowledge on a given subject; and as a “translation tool”.

Keywords

Learn, GPT Chat, Skills, University Education, Technology

Document Type

Conference paper

Citation

Mimoso, M. J., & Caramelo-Gomes, J. (2023). Chat GPT challenges in Higher Education assessment methodologies. In The Barcelona Conference on Education 2023: Official Conference Proceedings. Barcelona, Spain, 19-23 september 2023., (pp. 1-13). The International Academic Forum (IAFOR). https://doi.org/10.22492/issn.2435-9467.2023.26. Repositório Institucional UPT. https://hdl.handle.net/11328/5830

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