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<p>As generative artificial intelligence increasingly shapes pedagogical practice, automated feedback in higher education requires careful evaluation that extends beyond technical performance to students’ learning experiences. In this context, the present study compared students’ perceptions of instructor feedback and feedback generated by an instructor-designed, rubric-aligned chatbot within the same course context. The chatbot was developed by the course instructor and configured to provide qualitative, criterion-referenced feedback aligned with a course rubric, without grades. One hundred and six undergraduate students in a third-year Digital Marketing course received iterative, formative feedback from I-GenF-Bot, followed by summative feedback from the instructor using the same rubric. Students completed a questionnaire assessing functional dimensions of feedback quality (clarity and usefulness), an emotional–personal dimension (supportiveness), and overall satisfaction with the chatbot. In the present course context, comparisons between the two feedback sources did not reach statistical significance for clarity or usefulness. However, instructor feedback was perceived as more supportive in the present course context. Clarity, usefulness, and supportiveness were each positively associated with students’ overall satisfaction with I-GenF-Bot. An exploratory two-step cluster analysis suggested stronger differentiation for instructor-related items, as compared to chatbot-related items. Qualitative comments highlighted the chatbot’s strengths in immediacy, clarity, and accessibility, alongside limitations related to contextual sensitivity, repetition, and empathy. Overall, the findings suggest that, in the present course context, rubric-aligned AI-based feedback was perceived as approaching instructor feedback on functional quality dimensions, while an empathy gap remained a key constraint. These findings support a hybrid feedback model in which AI-supported guidance and instructor judgment serve complementary feedback functions.</p>

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Comparing instructor designed rubric aligned chatbot feedback and instructor feedback in higher education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月2日

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  • Iris Gertner Moryossef,
  • Christina Emmanouil

摘要

As generative artificial intelligence increasingly shapes pedagogical practice, automated feedback in higher education requires careful evaluation that extends beyond technical performance to students’ learning experiences. In this context, the present study compared students’ perceptions of instructor feedback and feedback generated by an instructor-designed, rubric-aligned chatbot within the same course context. The chatbot was developed by the course instructor and configured to provide qualitative, criterion-referenced feedback aligned with a course rubric, without grades. One hundred and six undergraduate students in a third-year Digital Marketing course received iterative, formative feedback from I-GenF-Bot, followed by summative feedback from the instructor using the same rubric. Students completed a questionnaire assessing functional dimensions of feedback quality (clarity and usefulness), an emotional–personal dimension (supportiveness), and overall satisfaction with the chatbot. In the present course context, comparisons between the two feedback sources did not reach statistical significance for clarity or usefulness. However, instructor feedback was perceived as more supportive in the present course context. Clarity, usefulness, and supportiveness were each positively associated with students’ overall satisfaction with I-GenF-Bot. An exploratory two-step cluster analysis suggested stronger differentiation for instructor-related items, as compared to chatbot-related items. Qualitative comments highlighted the chatbot’s strengths in immediacy, clarity, and accessibility, alongside limitations related to contextual sensitivity, repetition, and empathy. Overall, the findings suggest that, in the present course context, rubric-aligned AI-based feedback was perceived as approaching instructor feedback on functional quality dimensions, while an empathy gap remained a key constraint. These findings support a hybrid feedback model in which AI-supported guidance and instructor judgment serve complementary feedback functions.