AI-Generated Rubric Interfaces: K-12 Teachers' Perceptions and Practices

TL;DR

This study explores K-12 teachers' perceptions of AI-supported rubric tools, highlighting their benefits in structuring and clarifying criteria, while emphasizing the need for teacher oversight and customization to ensure alignment and usability.

Contribution

It provides insights into teachers' experiences with AI-generated rubrics, emphasizing the importance of customization, oversight, and workflow integration for effective adoption.

Findings

AI-generated rubrics improve structure and clarity.

Teachers value ease of customization and control.

Perceived as more detailed but sometimes harsher.

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.