Paper
AI-Generated Rubric Interfaces: K-12 Teachers' Perceptions and Practices
Authors
Bahare Riahi, Sayali Patukale, Joy Niranjan, Yogya Koneru, Tiffany Barnes, Veronica Cateté
Abstract
This study investigates K--12 teachers' perceptions and experiences with AI-supported rubric generation during a summer professional development workshop ($n = 25$). Teachers used MagicSchool.ai to generate rubrics and practiced prompting to tailor criteria and performance levels. They then applied these rubrics to provide feedback on a sample block-based programming activity, followed by using a chatbot to deliver rubric-based feedback for the same work. Data were collected through pre- and post-workshop surveys, open discussions, and exit tickets. We used thematic analysis to analyze the qualitative data. Teachers reported that they rarely create rubrics from scratch because the process is time-consuming and defining clear distinctions between performance levels is challenging. After hands-on use, teachers described AI-generated rubrics as strong starting drafts that improved structure and clarified vague criteria. However, they emphasized the need for teacher oversight due to generic or grade-misaligned language, occasional misalignment with instructional priorities, and the need for substantial editing. Survey results indicated high perceived clarity and ethical acceptability, moderate alignment with assignments, and usability as the primary weakness -- particularly the ability to add, remove, or revise criteria. Open-ended responses highlighted a ``strictness-versus-detail'' trade-off: AI feedback was often perceived as harsher but more detailed and scalable. As a result, teachers expressed conditional willingness to adopt AI rubric tools when workflows support easy customization and preserve teacher control.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.10773v1</id>\n <title>AI-Generated Rubric Interfaces: K-12 Teachers' Perceptions and Practices</title>\n <updated>2026-03-11T13:49:32Z</updated>\n <link href='https://arxiv.org/abs/2603.10773v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.10773v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>This study investigates K--12 teachers' perceptions and experiences with AI-supported rubric generation during a summer professional development workshop ($n = 25$). Teachers used MagicSchool.ai to generate rubrics and practiced prompting to tailor criteria and performance levels. They then applied these rubrics to provide feedback on a sample block-based programming activity, followed by using a chatbot to deliver rubric-based feedback for the same work.\n Data were collected through pre- and post-workshop surveys, open discussions, and exit tickets. We used thematic analysis to analyze the qualitative data. Teachers reported that they rarely create rubrics from scratch because the process is time-consuming and defining clear distinctions between performance levels is challenging.\n After hands-on use, teachers described AI-generated rubrics as strong starting drafts that improved structure and clarified vague criteria. However, they emphasized the need for teacher oversight due to generic or grade-misaligned language, occasional misalignment with instructional priorities, and the need for substantial editing.\n Survey results indicated high perceived clarity and ethical acceptability, moderate alignment with assignments, and usability as the primary weakness -- particularly the ability to add, remove, or revise criteria. Open-ended responses highlighted a ``strictness-versus-detail'' trade-off: AI feedback was often perceived as harsher but more detailed and scalable. As a result, teachers expressed conditional willingness to adopt AI rubric tools when workflows support easy customization and preserve teacher control.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-03-11T13:49:32Z</published>\n <arxiv:comment>20 pages, 2 figures</arxiv:comment>\n <arxiv:primary_category term='cs.HC'/>\n <author>\n <name>Bahare Riahi</name>\n </author>\n <author>\n <name>Sayali Patukale</name>\n </author>\n <author>\n <name>Joy Niranjan</name>\n </author>\n <author>\n <name>Yogya Koneru</name>\n </author>\n <author>\n <name>Tiffany Barnes</name>\n </author>\n <author>\n <name>Veronica Cateté</name>\n </author>\n </entry>"
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