Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?

Contributions in the Special Issue: The special issue assembles papers centring around log data analysis, natural language processing, and machine learning used to advance educational assessment. They demonstrate how semi‐ and unstructured data such as log and text data can, despite their challengin...

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Publicado en:Journal of Computer Assisted Learning Vol. 39; no. 3; pp. 695 - 703
Autores principales: Zehner, Fabian, Hahnel, Carolin
Formato: editorial pictorial Journal Article
Publicado: Wiley-Blackwell Jun2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jun2023
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      pub: Wiley-Blackwell
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        atl: Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?
      aug:
        au:
          Zehner, Fabian
          Hahnel, Carolin
        affil: Centre for Technology‐Based Assessment (TBA), DIPF Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany
      sug:
        subj:
          Artificial Intelligence
          Social Responsibility
          Serial Publications
          Natural Language Processing
          Machine Learning
          Problem Solving
      ab: Contributions in the Special Issue: The special issue assembles papers centring around log data analysis, natural language processing, and machine learning used to advance educational assessment. They demonstrate how semi‐ and unstructured data such as log and text data can, despite their challenging nature, be handled appropriately to benefit educational assessment. In this editorial, we contextualize the special issue's contributions within the diverse field of modern technology‐based assessments. Reflection on Terminology: Moreover, we raise concerns about nowadays' use of the term artificial intelligence (AI) in scientific communication. While the contribution of AI to scientific progress is indisputable, the mere use of methods that have evolved within AI research does not necessarily render tools or studies AI‐related. We argue that academics have the social responsibility to adopt accurate terminology, given it is integral to scientific rigour and proper scientific communication. Implications: In view of the inflationary use of the term AI in science, we propose a scheme to locate one's research in the field by focusing on (1) the type of data, (2) the processing involved, and (3) the output of a study and the actions derived from it, which are situated within the (4) scope of a study.
      pubtype: Academic Journal
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        pictorial
        Journal Article
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    language: English
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