(Re)Defining Ethical Assessment with the Advent of GenAI

The advent of generative artificial intelligence (GenAI) has further intensified concerns about academic integrity, raising fundamental questions about ethical behavior in assessment practices. Given that most of the conversation is currently focused on what students’ ethical behavior should be, we extend the conversation in this chapter to propose a theoretical framework for ethical assessment from the educator’s side. By adopting a student-centered perspective and considering the evolving role of GenAI in education, we redefine ethical assessment in a way that prioritizes meaningful learning and consequently promotes academic integrity. This framework aims to guide educators in developing assessments that align with ethical principles, ensuring fairness, transparency, and relevance in the GenAI era. The release of generative artificial intelligence (GenAI) has sparked a wave of conversations across higher education, with concerns about cheating, academic integrity, and plagiarism dominating headlines and faculty discussions. Two years after its introduction, the focus remains on how to detect and prevent student misuse of these tools. This reaction is understandable—GenAI disrupts long-standing practices in teaching and learning. Yet in this paper, we argue that the issue does not entirely lie in the technology or the students’ behaviors, but also in the design of assessments. We highlight the role of instructors in ensuring the integrity of assessments through their own design and administration.

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Publication Details

Published
2025-09-28
DOI
https://doi.org/10.35542/osf.io/zp458_v1
Primary Topic
Evaluation and Performance Assessment
Type
article
Field-Weighted Citation Impact
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article

(Re)Defining Ethical Assessment with the Advent of GenAI

Enas Aref, Eliana El Khoury, Eman Nasr
Evaluation and Performance Assessment
article

(Re)Defining Ethical Assessment with the Advent of GenAI

Enas Aref, Eliana El Khoury, Eman Nasr
article en

Abstract

The advent of generative artificial intelligence (GenAI) has further intensified concerns about academic integrity, raising fundamental questions about ethical behavior in assessment practices. Given that most of the conversation is currently focused on what students’ ethical behavior should be, we extend the conversation in this chapter to propose a theoretical framework for ethical assessment from the educator’s side. By adopting a student-centered perspective and considering the evolving role of GenAI in education, we redefine ethical assessment in a way that prioritizes meaningful learning and consequently promotes academic integrity. This framework aims to guide educators in developing assessments that align with ethical principles, ensuring fairness, transparency, and relevance in the GenAI era. The release of generative artificial intelligence (GenAI) has sparked a wave of conversations across higher education, with concerns about cheating, academic integrity, and plagiarism dominating headlines and faculty discussions. Two years after its introduction, the focus remains on how to detect and prevent student misuse of these tools. This reaction is understandable—GenAI disrupts long-standing practices in teaching and learning. Yet in this paper, we argue that the issue does not entirely lie in the technology or the students’ behaviors, but also in the design of assessments. We highlight the role of instructors in ensuring the integrity of assessments through their own design and administration.

Openalex Percentile: Top 69%
Evaluation and Performance Assessment
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