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The human touch of feedback: students’ experiences of CARE in peer versus AI-generated feedback
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DOI：10.1080/02602938.2026.2679629.png)
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Abstract
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The integration of AI-generated feedback into higher education has increased feedback volume and efficiency. Yet concerns persist that it lacks the ‘human touch’, a construct that remains undertheorised and empirically unexamined. To examine what constitutes the human touch, this study compared AI and peer feedback in an interpreting course, capturing 41 university students’ immediate responses through the think-aloud method across seven weeks. Analysis revealed that whereas AI provided comprehensive, criterion-based commentary with a more positive tone, peer feedback demonstrated greater developmental sensitivity and relational grounding. Students showed emotional indifference to AI feedback but valued the contextual understanding and authentic support that peer feedback provided. These patterns informed the empirically grounded CARE framework: Care respect, Attainable goals, Relational recognition, and Emphasised problem identification. Each CARE dimension depends on qualities emerging from shared participation in learning communities that algorithmic systems struggle to replicate. Theoretically, CARE offers concrete dimensions for understanding feedback effectiveness beyond content coverage. Productive AI integration requires not simulating human touch but designing complementary systems that leverage the strengths of different feedback sources. The presence of human feedback providers does not guarantee the human touch, either. The CARE dimensions demand deliberate assessment design and invite further exploration.
Keywords:
AI feedback
peer feedback
human touch
assessment for learning
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