Stakeholder-Driven Contextual Evaluation of Language Models in Education

With the increasing reliance of AIED on opaque, black-box scaffolds such as large language models to support student learning, there is a growing concern about their limitations when used in diverse pedagogical contexts. This opacity often undermines stakeholders' trust and shapes their perceptions, contributing to resistance toward the adoption of AI scaffolds in schools.

To address these challenges, we developed AIBAT, a workflow and system designed to support stakeholders in auditing and critically evaluating the potential benefits and harms of AI systems within their specific pedagogical contexts (e.g., subject matter, grade level, English proficiency). With AIBAT, stakeholders can specify expected behaviors — i.e., what they anticipate the AI scaffold should do — and test the system against those expectations.

In this half-day tutorial, participants will use AIBAT to identify and make sense of AI-related risks and use evidence to calibrate their trust in AI scaffolds. At the end of the tutorial, we will deliberate on AI auditing processes and discuss broader implications for promoting responsible and effective stakeholder participation in the evaluation and deployment of AI systems in educational settings.

Submission guidelines

To participate in this tutorial, please submit a short statement of interest via EasyChair. Submissions will be reviewed by the organizers to ensure a productive and focused group experience.

• Submissions should be written in English.
• Use the AIED 2026 paper template (Springer LNCS format).
• Submit via EasyChair using the link below.