Measuring What Matters: AI and the Future of Fair Assessment

Dear Colleagues,

Scope and Purpose of the Special Issue

The Special Issue "Measuring What Matters: AI and the Future of Fair Assessment" seeks to explore the evolving intersection of artificial intelligence (AI) and educational assessment with a deliberate emphasis on fairness, equity, and inclusivity. As AI technologies become increasingly integrated into educational systems, it is essential to critically examine how these tools can both mitigate and exacerbate existing biases in assessment practices. This Issue brings together interdisciplinary perspectives, empirical studies, conceptual frameworks, and practical innovations aimed at reimagining assessment through the lens of technological advancement and social justice.

1. Focus
   The central focus of this issue is the use of AI to enhance fairness in educational assessments across diverse contexts, including K–12, higher education, vocational training, and informal learning environments. Contributors will address key questions such as the following: How can AI help identify and reduce bias in testing? What design principles ensure AI-assisted assessments are inclusive and representative of all learners? What ethical considerations arise in the deployment of algorithmic evaluation tools?
2. Scope
   The Issue will include a wide range of contributions, from theoretical analyses and design-based research to case studies and technical innovations. Topics may include, but are not limited to the following: bias detection and correction in AI models; AI for adaptive testing and personalized feedback; fairness metrics in automated scoring systems; explainability and transparency in algorithmic decision-making; and policy implications for equitable AI deployment in education. This broad scope encourages interdisciplinary dialogue among educators, computer scientists, psychometricians, policymakers, and ethicists.
3. Purpose
   The primary purpose of the Special Issue is to highlight actionable, evidence-based approaches that harness AI to create more just and equitable assessment practices. It aims to catalyze further research and practical implementation while fostering critical reflection on the limitations and responsibilities of using AI in educational measurement.

Relation to Existing Literature

This Special Issue complements and extends existing literature on educational technology, fairness in assessment, and algorithmic bias by centering AI as both a tool and a site of scrutiny. While prior research has examined digital assessments and equity separately, this issue uniquely situates AI-driven assessment as a space where technical innovation and ethical accountability must co-evolve. It contributes to a growing body of work calling for responsible, human-centered AI in education and aims to provide both conceptual clarity and practical guidance for the field.

Prof. Dr. Joseph Kush
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• educational assessment
• fairness
• equity in education
• algorithmic bias
• adaptive testing
• ethical AI
• inclusive assessment