Special Session-1
AI-Based Evaluation and Feedback for Student Work
Chairman: Prof. Thorsten Fröhlich, IU International University of Applied Sciences, Germany
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Biography: Thorsten Fröhlich is an IT Management and Big Data Professor at the IU International University of Applied Sciences. His expertise includes applied artificial intelligence, machine learning, big data, IT management and digital marketing. His research focuses on the practical application of AI in education, ethics and natural language processing (NLP). As a recognised expert, he lectures on AI and ethics in education in collaboration with UNESCO, among others. He recently published a comprehensive compendium on scientific writing with M.C. Hemmer. After studying chemistry at the University of Cologne, he completed his doctorate in physical chemistry in 1992. He has been an entrepreneur since 1987 and has founded several successful companies in software development, IT management and digital marketing for the life science industry. As a business angel, he supports student start-ups and promotes innovative technology projects.
Description: This special session focuses on AI-based methods for evaluating and providing feedback on student work across different educational levels, ranging from school-level assignments and essays to university projects, theses, and dissertations. We invite contributions that explore how artificial intelligence—particularly large language models and multimodal AI—can be used to assess, analyse, and support a wide variety of student submissions.
Topics of interest include (but are not limited to):
• evaluation of essays, theses, reports, and projects
• AI-supported feedback generation for written and technical student work
• Assessment of programming assignments and code using AI
• Detection of fabricated, hallucinated, or AI-generated references in student work
• Reliability, validity, and fairness of AI-based evaluation across educational levels
• Human–AI collaboration in assessment and grading
• Ethical, pedagogical, and practical considerations of AI-assisted assessment
The session aims to bring together researchers and practitioners working on methods, systems, and empirical studies related to AI-based evaluation and feedback for student work.