mlsanityData to 7 Oct 2026

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

arXiv 2312.07086 ↗First posted 12 Dec 2023cs.AIcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 8 of its 61 references that are in the map. First posted on arXiv 12 Dec 2023.

Builds on

8 of 61 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Can AI-Generated Text be Reliably Detected?cited by 372
  3. 2023GPT detectors are biased against non-native English writerscited by 175
  4. 2023Testing of Detection Tools for AI-Generated Textcited by 86
  5. 2023Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Educationcited by 41
  6. 2023Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Casescited by 32
  7. 2023Game of Tones: Faculty detection of GPT-4 generated content in university assessmentscited by 15
  8. 2023Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Educationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 5.

12 papers.