GenAI as a Design Variable: Assessment Governance Framework

This paper proposes a principled framework grounded in Evidence-Centered Design (ECD) that treats Generative AI as a design variable within STEM assessment arguments rather than an external threat. This represents a significant evolution beyond the binary debate of 'ban AI vs. allow AI' that has dominated discussions about Academic Integrity in education.

Three Governance Stances

The framework articulates three context-dependent governance stances based on how GenAI interacts with the assessment's validity argument:

1. Restrict — warranted when GenAI would contaminate the inferential chain between student work products and targeted unaided proficiency. This preserves the validity of assessments designed to measure independent competence.

2. Scaffold — warranted when bounded GenAI support can assist with peripheral demands without revealing the target construct, preserving inferential interpretability. This aligns with Scaffolding approaches in Intelligent Tutoring systems.

3. Require — warranted when the target construct is disciplinary AI interaction competency itself. Tasks elicit process artifacts (prompts, critiques, revisions) that make student reasoning observable and scorable, distinguishing it from AI-generated output.

Empirical Validation

Two task designs deployed in an introductory physics course demonstrated that disciplinary AI interaction competencies are observable in student response artifacts and can be scored using defensible rubrics grounded in student data and expert knowledge. This connects to Automated Grading and Automatic Short Answer Grading research on making student reasoning visible and scorable.

Implications for Policy

By situating GenAI governance within validity arguments, the framework offers actionable guidance for preserving learning integrity while preparing students for AI-enabled workplaces. This has direct relevance for Educational Policy AI decisions about assessment design and AI Literacy standards. The framework also complements emerging work on AI tutor safety by providing structured decision-making about when AI interaction is educationally appropriate.