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        <identifier>oai:figshare.com:article/33978106</identifier>
        <datestamp>2026-09-30T00:29:13Z</datestamp>
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          <dc:title>Huican Huo: When AI gives advice, who decides what happens next?</dc:title>
          <dc:creator>Huican Huo (19741768)</dc:creator>
          <dc:subject>Educational technology and computing</dc:subject>
          <dc:subject>Generative AI</dc:subject>
          <dc:subject>academic writing</dc:subject>
          <dc:subject>learner judgement</dc:subject>
          <dc:subject>evaluative control</dc:subject>
          <dc:subject>feedback</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;When AI offers writing advice, how do learners decide what to trust? This study followed 27 learners through a 12-week academic writing course in New Zealand. We analysed 1,127 episodes of AI interaction alongside journals and interviews to understand learners’ decisions. Explicit checking occurred in 50.1% of episodes. Learners described comparing AI advice with assessment criteria, teacher guidance and prior knowledge, often when they felt unsure. These accounts show why understanding learners’ judgement requires looking at their reasons for checking and what they did next. Learner Evaluative Control provides a framework for asking whether learners’ own judgements guide how they use, question or reject AI advice. For teachers, this means asking learners to explain their choices and giving them time to weigh advice against other evidence.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T00:29:13Z</dc:date>
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