Speaker
Description
The rise of digital technology in educational settings from its formative years in the 1980s to its current iteration has seemed inexorable and, in recent months, exponential. Natural Language Processing and Large Language Models are now ubiquitous topics of debate when discussing ethical and moral concerns in education. This presentation will begin by examining the ethical and moral issues associated with the use of generative AI in academic writing. It will address concerns related to academic integrity, the potential for AI to facilitate plagiarism, and the cognitive impact on students' critical thinking and learning processes. Subsequently, it asks two key questions: firstly, is it possible to create 'AI-proof' writing assignments, and secondly, is it ultimately more far-sighted to focus on higher order thinking skills and a scaffolded approach when dealing with generative AI? Lastly, the presentation considers the situation in Japan, where rote-learning and memorization are key concepts in learning. The recent decision by Japan’s minister of education, culture, sports, science, and technology to permit content from any source to be used for “information analysis'' means that Japan is an outlier in its approach to fair use and associated copyright and tort law issues. If all input is considered fair game for training generative models, irrespective of content and method, it is prudent to consider whether all output will also be similarly considered acceptable. From this presentation, it is hoped that educators will gain a comprehensive understanding of the ethical considerations surrounding the use of generative AI in academic writing as well as practical strategies to promote academic integrity and prevent misconduct in student assignments.
| Keywords | Generative AI, ethics, |
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