Generative AI in Assessment and Feedback Activities of Higher Education
A special issue of Education Sciences (ISSN 2227-7102).
Deadline for manuscript submissions: 31 October 2025 | Viewed by 246
Special Issue Editors
Interests: digital learning technology; interaction design and UX; data science; generative AI; academic writing; higher education; scientific publishing; assessment and feedback
Interests: math education; curriculum development; teacher education; E-learning; AIED
Interests: adaptive learning technology; problem-based learning; interaction design and UX; learning analytics; generative AI
Special Issue Information
Dear Colleagues,
The integration of Generative Artificial Intelligence (GenAI) in the assessment and feedback activities of higher education has disrupted pedagogical methodologies, organizational norms, and academic practices. As GenAI technologies continue to evolve, their application, particularly in the assessment activities of higher education, extends beyond content creation to revolutionizing assessment frameworks, automating feedback mechanisms, and personalizing learning experiences. These advancements hold immense potential, but have been met with concerns and controversies among researchers and practitioners interested in improving student engagement, streamlining grading processes, and ensuring improved learning outcomes with timely, adaptive feedback.
This Special Issue encourages cross-disciplinary collaborations among educators, trainers, researchers, and practitioners.
The themes covered include, but are not limited to, the following: (1) GenAI-enabled personalized assessment and feedback: adaptive assessment strategies, individualized learning pathways, and AI-driven student support. (2) Automated grading and feedback mechanisms: real-time evaluation, rubric-based grading, and AI-assisted formative feedback. (3) GenAI in academic writing and research evaluation: GenAI-assisted peer review, academic integrity considerations, and bibliometric applications. (4) Ethical and policy challenges in GenAI-Driven assessment: issues of fairness, bias, transparency, and responsible AI implementation. (5) Innovative tools, platforms, and emerging technologies: novel AI-driven solutions for assessment and feedback enhancement. (6) Case studies and cross-disciplinary collaborations: empirical studies showcasing practical GenAI applications across diverse educational settings.
Dr. Md Saifuddin Khalid
Dr. Soheil Salha
Dr. Tobias Alexander Bang Tretow-Fish
Dr. Nafees Mansoor
Guest Editors
Manuscript Submission Information
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Keywords
- generative AI
- higher education
- training
- research
- assessment
- feedback
- scientific database
- content generation
- personalization
- adaptive learning
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