Literaturnachweis - Detailanzeige
| Autor/in | Morrison, Ryan |
|---|---|
| Titel | Large Language Models and Text Generators: An Overview for Educators |
| Quelle | (2022), (38 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Models; Mathematics; Automation; Natural Language Processing; Technology Uses in Education; Prompting; Plagiarism; Ethics; Paragraph Composition; Sentences; Essays |
| Abstract | Large Language Models (LLM) -- powerful algorithms that can generate and transform text -- are set to disrupt language learning education and text-based assessments as they allow for automation of text that can meet certain outcomes of many traditional assessments such as essays. While there is no way to definitively identify text created by this technology, there are patterns that educators can use to adapt assessments to minimize the impact that these tools will have on academic integrity. This document provides an overview of the technology and how it is being utilized by publicly available platforms, some of which are targeting education; samples and analysis of the idiosyncrasies of text generated and transformed by LLM platforms; and suggestions on how educators can adjust their approach to language and text-based assessments to better meet the needs of students in a world where LLM tools become ubiquitous. [Written with a Generative Pre-Trained Transformer (GPT-2 & GPT-3).] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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