REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908450753085440 |
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| author | Tang, Xiaohang Wong, Sam He, Zicheng Yang, Yalong Chen, Yan |
| author_facet | Tang, Xiaohang Wong, Sam He, Zicheng Yang, Yalong Chen, Yan |
| contents | This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. REVA's usefulness and effectiveness in improving feedback quality and the overall feedback review process were evaluated through a within-subjects lab study with 12 participants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation Tang, Xiaohang Wong, Sam He, Zicheng Yang, Yalong Chen, Yan Human-Computer Interaction This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. REVA's usefulness and effectiveness in improving feedback quality and the overall feedback review process were evaluated through a within-subjects lab study with 12 participants. |
| title | REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.11470 |