Search (8 results, page 1 of 1)

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Abstract

ChatGPT, a general-purpose conversation chatbot released on November 30, 2022, by OpenAI, is expected to impact every aspect of society. However, the potential impacts of this NLP tool on education remain unknown. Such impact can be enormous as the capacity of ChatGPT may drive changes to educational learning goals, learning activities, and assessment and evaluation practices. This study was conducted by piloting ChatGPT to write an academic paper, titled Artificial Intelligence for Education (see Appendix A). The piloting result suggests that ChatGPT is able to help researchers write a paper that is coherent, (partially) accurate, informative, and systematic. The writing is extremely efficient (2-3 hours) and involves very limited professional knowledge from the author. Drawing upon the user experience, I reflect on the potential impacts of ChatGPT, as well as similar AI tools, on education. The paper concludes by suggesting adjusting learning goals-students should be able to use AI tools to conduct subject-domain tasks and education should focus on improving students' creativity and critical thinking rather than general skills. To accomplish the learning goals, researchers should design AI-involved learning tasks to engage students in solving real-world problems. ChatGPT also raises concerns that students may outsource assessment tasks. This paper concludes that new formats of assessments are needed to focus on creativity and critical thinking that AI cannot substitute.

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Date

1. 3.2013 14:56:22

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Date

22. 3.2015 9:30:24

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Date

29.12.2022 18:22:55

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Date

16. 3.2023 19:22:55

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Abstract

The automated categorisation (or classification) of texts into topical categories has a long history, dating back at least to 1960. Until the late '80s, the dominant approach to the problem involved knowledge-engineering automatic categorisers, i.e. manually building a set of rules encoding expert knowledge an how to classify documents. In the '90s, with the booming production and availability of on-line documents, automated text categorisation has witnessed an increased and renewed interest. A newer paradigm based an machine learning has superseded the previous approach. Within this paradigm, a general inductive process automatically builds a classifier by "learning", from a set of previously classified documents, the characteristics of one or more categories; the advantages are a very good effectiveness, a considerable savings in terms of expert manpower, and domain independence. In this tutorial we look at the main approaches that have been taken towards automatic text categorisation within the general machine learning paradigm. Issues of document indexing, classifier construction, and classifier evaluation, will be touched upon.

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Abstract

Vor 50 Jahren, im Februar 1966, wies Floyd M. Cammack auf den Zusammenhang von "Linguistics and Libraries" hin. Er ging dabei von dem Eintrag für "Linguistics" in den Library of Congress Subject Headings (LCSH) von 1957 aus, der als Verweis "See Language and Languages; Philology; Philology, Comparative" enthielt. Acht Jahre später kamen unter dem Schlagwort "Language and Languages" Ergänzungen wie "language data processing", "automatic indexing", "machine translation" und "psycholinguistics" hinzu. Für Cammack zeigt sich hier ein Netz komplexer Wechselbeziehungen, die unter dem Begriff "Linguistics" zusammengefasst werden sollten. Dieses System habe wichtigen Einfluss auf alle, die mit dem Sammeln, Organisieren, Speichern und Wiederauffinden von Informationen befasst seien. (Cammack 1966:73). Hier liegt - im übertragenen Sinne - ein Heft vor Ihnen, in dem es um computerlinguistische Verfahren in Bibliotheken geht. Letztlich geht es um eine Versachlichung der Diskussion, um den Stellenwert der Inhaltserschliessung und die Rekalibrierung ihrer Wertschätzung in Zeiten von Mega-Indizes und Big Data. Der derzeitige Widerspruch zwischen dem Wunsch nach relevanter Treffermenge in Rechercheoberflächen vs. der Erfahrung des Relevanz-Rankings ist zu lösen. Explizit auch die Frage, wie oft wir von letzterem enttäuscht wurden und was zu tun ist, um das Verhältnis von recall und precision wieder in ein angebrachtes Gleichgewicht zu bringen. Unsere Nutzerinnen und Nutzer werden es uns danken.

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Date

22. 1.2018 11:32:44