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        <title>Artificial Intelligence in Education: A Review</title>
        <author>Chen</author>
        <author>Chen</author>
        <author>Lin</author>
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          <title>Artificial Intelligence in Education: A Review</title>
          <date>2020</date>
          <publisher>IEEE Access</publisher>
          <idno type="DOI">10.1109/access.2020.2988510</idno>
          
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        <label>artificial intelligence in education</label>
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        <term>Machine learning infers each learner&#x27;s capabilities and needs, which lets the system deliver personalised content, and that personalisation is what raises uptake and retention (i.e. improves learning).</term>
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        <term>Automating administrative paperwork frees instructor time, which is then redirected to core instructional work, raising teaching quality.</term>
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        <label>The review&#x27;s scope was deliberately restricted to three areas of education taken from a prior framework.</label>
        <note>what this limits: the three areas the review covers and nothing else: THE SCOPE OF THE STUDY WAS LIMITED TO THE APPLICATION AND EFFECTS OF AI IN ADMINISTRATION, INSTRUCTION, AND LEARNING</note>
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        <label>One cited source is reported as defining the review&#x27;s central term as two different kinds of thing.</label>
        <note>what this limits: any use of this review&#x27;s account of AI: it reports a two-faceted definition in which AI is both a field of study in computer science and a theoretical framework guiding the development of computer systems</note>
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        <term>artificial intelligence</term>
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        <label>AI - A FIELD AND A THEORY AT ONCE, ON ONE SOURCE&#x27;S ACCOUNT: reported as a study area in computer science aimed at solving cognitive problems, and also as a theoretical framework guiding the development and use of computer systems; a field of study and a theoretical framework are not the same kind of entity, and the review reports both without choosing</label>
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        <label>the definitions of AI - CALLED NEAR SIMILAR: the review gathers characterisations from several authors and describes them as bringing to the fore near similar elements, which is a judgement that differences do not matter rather than a demonstration that there are none</label>
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        <label>the H-Index - A PROXY FOR REPUTABILITY, WITH THE INFERENCE WRITTEN DOWN: the review states that the higher the H-Index the more reputable the journal and its authors, and then uses a threshold of 20 as an inclusion rule, so the corpus is bounded by a bibliometric number standing in for quality</label>
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        <label>builds-on → Chassignol et al. (2018), &#x27;Artificial intelligence trends in education: A narrative overview&#x27;</label>
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        <label>artificial intelligence —is-a→ field of study</label>
        <term>artificial intelligence</term>
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        <note>strength of the relation: 0.8</note>
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        <label>The review concludes that AI has been widely adopted across education institutions in several distinct technological forms.</label>
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        <label>The main learning benefit identified was customisation and personalisation of curriculum and content to individual learners&#x27; needs and abilities.</label>
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        <label>One reviewed study highlighted an adverse effect of AI: degraded academic integrity and cheating through AI-enabled paper-mill services.</label>
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        <label>machine learning</label>
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        <label>70% — share of the AI-and-education papers found in Web of Science and Google Scholar that were published in the 2015-2019 window</label>
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