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- PaperarXiv — AI in Education (cs.CY)21 Jul 2026
Assessment in Team Problem-Solving Exercises in Computing Education
Valdemar Švábenský, Jan Vykopal, Sukrit Leelaluk, Pavel Čeleda et al.
This paper compares clustering and large language models (LLMs) for assessing student teams in tabletop exercises (TTXs), which are used to practice crisis response in computing education. Clustering grouped teams by task approach, enabling faster feedback and showing high reliability with low computational cost. GPT-5.2 outperformed GPT-4o in scoring teams against instructor rubrics, and both methods were integrated into an open-source TTX platform.
Original abstract
This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents. While assessment is essential for determining how well teams achieve learning objectives, the complex, open-ended nature of TTXs often leads to delayed or incomplete feedback. TTX learning platforms can record teams' actions and communication; yet, leveraging these data to assess performance is underexplored. To address this gap, we compared two post-TTX team assessment methods -- clustering and large language models (LLMs) -- using an original dataset from 81 participants across two countries. We evaluated these methods against instructor-assigned scores based on standardized rubrics. Clustering grouped teams that approached TTX tasks similarly, enabling instructors to deliver faster, targeted feedback to teams within a cluster. This method was valid and reliable, with low computational requirements. LLMs used the standardized rubrics to assess teams' communication. While GPT-4o frequently disagreed with instructor scores, GPT-5.2 demonstrated considerably lower error. The researched methods have been integrated into INJECT, an open-source TTX learning platform, to support scalability and teaching practice. To encourage community adoption, we publicly share all datasets, software tools, and a full-fledged TTX scenario.