Scholarlink

<p>Written assignments for large classes pose a far more significant challenge in the age of the GenAI revolution. Suggestions such as oral exams and formative assessments are not always feasible with many students in a class. Therefore, we conducted a study in South Africa and involved 280 Honors students to explore the usefulness of Turnitin's AI detector in conjunction with student self-reflection. Using a Mixed Methods Research (MMR) approach, we analysed data generated from the Turnitin AI reports, our grading rubrics, and qualitative student self-reflection. The findings show that incorporating self-reflection into assessments supports ethical GenAI use and improves the transparency lecturers need for decision-making. A declaration form allowed the students to be upfront about using Generative Artificial Intelligence tools. We found that students who can reflect on their learning relied less on generated content. However, students with high AI detected scores (&gt; 20%) did not adequately reflect on how the tools supported their learning and could not give credible explanations of use. We contribute to the body of knowledge by providing students and academics with examples of responsibly handling AI-detected scores in large-class settings. We present a guided self-reflection and declaration with an AI detector to support students and help lecturers make decisions when grading. We also present a decision tree that lecturers and graders can use when evaluating AI use in assessments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Student self-reflection as a tool for managing GenAI use in large class assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月26日

查看全文
  • Celeste Combrinck,
  • Nelé Loubser

摘要

Written assignments for large classes pose a far more significant challenge in the age of the GenAI revolution. Suggestions such as oral exams and formative assessments are not always feasible with many students in a class. Therefore, we conducted a study in South Africa and involved 280 Honors students to explore the usefulness of Turnitin's AI detector in conjunction with student self-reflection. Using a Mixed Methods Research (MMR) approach, we analysed data generated from the Turnitin AI reports, our grading rubrics, and qualitative student self-reflection. The findings show that incorporating self-reflection into assessments supports ethical GenAI use and improves the transparency lecturers need for decision-making. A declaration form allowed the students to be upfront about using Generative Artificial Intelligence tools. We found that students who can reflect on their learning relied less on generated content. However, students with high AI detected scores (> 20%) did not adequately reflect on how the tools supported their learning and could not give credible explanations of use. We contribute to the body of knowledge by providing students and academics with examples of responsibly handling AI-detected scores in large-class settings. We present a guided self-reflection and declaration with an AI detector to support students and help lecturers make decisions when grading. We also present a decision tree that lecturers and graders can use when evaluating AI use in assessments.