AI Competence Trees for Honours Bachelor Tracks
Background Information
The Honours Bachelor programme currently offers eleven distinct tracks that provide a two-year, open-ended, complex, and multidisciplinary Challenge-Based Learning setting. Every year, approximately one hundred and twenty-five students enrol in these extracurricular tracks, which are specifically designed for individuals seeking an additional academic challenge entirely separate from their standard degree programmes. Within their chosen thematic track, students enjoy the profound freedom to design their own collaborative projects and establish highly personalised learning trajectories. While this inherently student-led approach presents unique educational opportunities, it simultaneously creates significant hurdles for students who often struggle to clearly articulate their learning goals, describe their developmental gains, and construct a structured path within such a broad framework. Furthermore, writing a mandatory Personal Development Plan is generally considered a highly difficult step, as students are unaccustomed to designing their own educational journey and often experience feelings of uncertainty. Consequently, the Honours Academy has held a long-standing ambition to develop detailed competence trees for each individual track to provide much-needed support for students in this process of self-directed learning. These competence trees would make the acquisition of specific knowledge, transversal and technical skills far more visible and accessible. However, accurately mapping these complex competencies manually across an extremely flexible and diverse environment is overwhelmingly difficult and resource-intensive. Establishing these visualisations remains crucial for guiding students through their initial orientation phase, assisting them in drafting their required plans, and helping them continuously reflect upon their long-term achievements.
Aim of the Project
The primary objective of this project is to investigate whether artificial intelligence tools, specifically Microsoft Copilot Analyst, can efficiently and effectively generate visual competence trees for all eleven Honours Bachelor tracks by processing existing educational materials. By thoroughly analysing over five thousand diverse documents spanning five academic years, including track descriptions, student development plans, project proposals, and assessment rubrics, the initiative seeks to accurately map the typical learning trajectories within these unstructured environments. If the artificial intelligence processing proves successful, the resulting competence trees will substantially enhance the overall student learning experience in several crucial ways. Firstly, these visualisations will directly assist prospective students in making highly informed track selections by clearly outlining potential learning goals, typical project contents, and any required prerequisite knowledge they might need to acquire. Secondly, they will provide vital inspiration and a structured framework for enrolled students as they shape their open-ended collaborative projects and write their formal developmental plans. Thirdly, these robust visualisations will offer a consistent, shared language for teaching staff and academic coaches to better guide student reflection, evaluate continuous progress, and formally assess overall learning gains at the end of each academic year. Furthermore, the project intentionally aims to conduct a longitudinal analysis of these generated competence trees to actively identify shifting trends in student educational needs. This broader analytical insight could ultimately inform future pedagogical improvements in both the extracurricular Honours programme and the regular university curricula.
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