# The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

arXiv: https://arxiv.org/abs/2312.07086 · HTML: https://mlsanity.com/paper/2312.07086/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Cases](https://mlsanity.com/paper/2307.07411/) (2023)
- [Game of Tones: Faculty detection of GPT-4 generated content in university assessments](https://mlsanity.com/paper/2305.18081/) (2023)
- [Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education](https://mlsanity.com/paper/2305.18617/) (2023)

## Built on by (most cited first, 12 of 12)

- [GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education](https://mlsanity.com/paper/2403.19148/) (2024)
- [Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration](https://mlsanity.com/paper/2407.16887/) (2024)
- [Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback](https://mlsanity.com/paper/2406.15808/) (2024)
- [Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis](https://mlsanity.com/paper/2411.00631/) (2024)
- [Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment](https://mlsanity.com/paper/2506.02046/) (2025)
- [The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes](https://mlsanity.com/paper/2408.01075/) (2024)
- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)
- [AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains](https://mlsanity.com/paper/2604.19751/) (2026)
- [A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production](https://mlsanity.com/paper/2604.25346/) (2026)
- [Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows](https://mlsanity.com/paper/2604.25389/) (2026)
- [Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning](https://mlsanity.com/paper/2605.27400/) (2026)
- [Education-centered critical policy analysis of AI: Ghana's AI strategy as a case](https://mlsanity.com/paper/2608.16910/) (2026)

## Abstract (arXiv)

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.