# Positioning Generative Artificial Intelligence in STEM Assessment: When to Require, Scaffold, or Restrict Its Use

arXiv: https://arxiv.org/abs/2608.07475 · HTML: https://mlsanity.com/paper/2608.07475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance](https://mlsanity.com/paper/2503.18238/) (2025)
- [Examining the Potential and Pitfalls of ChatGPT in Science and Engineering Problem-Solving](https://mlsanity.com/paper/2310.08773/) (2023)
- [Evidence-centered Assessment for Writing with Generative AI](https://mlsanity.com/paper/2401.08964/) (2024)

## Abstract (arXiv)

Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted access can enable task outsourcing that undermines the validity of traditional assessments, while blanket prohibitions are difficult to enforce, may drive use underground, and do little to prepare students for workplaces where GenAI supported workflows are increasingly common. This paper proposes a student focused framework grounded in Evidence Centered Design (ECD) that specifies when to restrict, scaffold, or require GenAI use in STEM assessment. The framework extends existing AI use taxonomies by providing decision rules that link target constructs, evidence requirements, and task characteristics to governance regimes. Restriction is warranted when GenAI threatens construct relevant evidence for unaided proficiency, particularly for foundational knowledge and routine skills. Scaffolding is appropriate when bounded GenAI support reduces peripheral demands while maintaining interpretability. Requiring GenAI is appropriate when the target construct involves human AI collaboration and AI literacy. Using examples from introductory physics, we illustrate how tasks can be designed under different GenAI use policies. The framework provides guidance for preserving learning integrity while supporting preparation for AI enabled environments.