The rapid integration of artificial intelligence (AI) and large language models (LLMs) into higher education assessment has transformed how learning outcomes are measured and feedback is delivered. Automated and AI-assisted grading systems are increasingly applied to written, computational, and multimodal student outputs, enabling scalable evaluation and timely feedback. However, this growing reliance on algorithmic assessment raises critical questions of fairness, validity, transparency, and accountability in educational practice and policy.

Recent studies highlight both the opportunities and the risks of AI-driven evaluation. While fine-tuned generative models show promising accuracy in essay or short-answer scoring, significant gaps persist in empirical validation, bias mitigation, and explainability. At the same time, institutional leaders and educators face increasing pressure to ensure that automation aligns with ethical standards, pedagogical integrity, and student trust. Understanding how fairness perceptions and algorithmic transparency affect learners’ motivation and engagement is essential to building equitable AI-supported assessment ecosystems.

This Research Topic aims to consolidate interdisciplinary research that advances conceptual, empirical, and practical understanding of fairness and transparency in automated assessment. It seeks to connect the work of education researchers, data scientists, policy makers, and practitioners to establish shared frameworks for valid, inclusive, and human-centered use of AI in higher education evaluation.

Contributions are particularly encouraged that:

(a) examine the reliability and equity of automated assessment systems across diverse learner populations, languages, and disciplines;

(b) analyze the pedagogical and policy implications of AI-mediated feedback; and

(c) propose frameworks, tools, and benchmarks for ensuring transparency, reproducibility, and trustworthiness.

We welcome comparative and cross-cultural studies, as well as evidence-based discussions aligned with international policy frameworks such as the EU AI Act, UNESCO’s AI in Education guidelines, and OECD principles for trustworthy AI.

Potential Themes

- Validity, reliability, and fairness in automated and AI-assisted grading

- Generative feedback and rubric alignment in LLM-supported learning

- Ethics, integrity, and policy implications of AI in higher education

- Accessibility and Universal Design for Learning in digital assessment

- Institutional adoption, governance, and cost-effectiveness

- Cross-disciplinary and cross-cultural perspectives on AI in assessment

- Benchmarking, transparency frameworks, and open data practices

Article Types

Original Research; Methods; Systematic Review; Brief Research Report; Conceptual Analysis; Policy and Practice Review; Perspective.

What is Out of Scope

Studies focused on K–12 education, tool demonstrations lacking evaluative rigor, or purely surveillance-based proctoring systems without ethical or pedagogical analysis.

Are you interested in contributing a manuscript to this Research Topic?

Fairness, Transparency, and Validity in Automated Assessment: Evidence, Frameworks, and Implications for Higher Education