Concept Catalyst: Exploring Scrutable Interfaces to Structure K-12 Teacher Interactions with Generative AI

Gennie Mansi, Sunni Newton, Roxanne Moore, Meltem Alemdar, Mark Riedl

Purpose: This paper explores how to align AI-based tools with teachers' classroom needs by using scrutable interfaces -- interfaces that link an easily manipulable knowledge representation to an underlying AI model, so users can change the system's outputs without understanding its details. It provides an in-depth discussion and example of a scrutable interface that structures teachers' interactions with generative AI. This study aims to expand how and where scrutable interfaces are used in AI-based tools to support teachers, who have not been historically targeted in the design of scrutable systems. Design/Methodology/Approach: This paper presents the design and evaluation of Concept Catalyst, an AI-based tool with a scrutable interface, created to support teachers' reflection while using generative AI for curriculum development. It presents the findings from an exploratory study using Wizard-of-Oz testing with middle and high school engineering teachers, resulting in 10 depth interviews lasting 55 minutes on average. Screen/audio recordings and the classroom content teachers produced during the session were also collected. Findings: The paper provides empirical insights about how scrutable interfaces can positively structure teachers' interactions with generative AI models when creating classroom content. Findings suggest that scrutable interfaces can help teachers reflect on their teaching practices while improving efficacy, efficiency, and motivation when using AI. What is original/value of the paper: This paper explores an identified need to support teachers' classroom practices and needs when using generative AI. It extends the consideration of scrutable interfaces in two ways: to support teachers as users (not just students) and to structure interactions with generative AI models.