@inproceedings{SchauerKatharinaFernsel, author = {Schauer, Sophie and Katharina, Simbeck and Fernsel, Linda}, title = {The System Admin's Perspective: A Discussion on AI in Education with LMS Admins}, series = {International Conference on Information Technology Based Higher Education and Training (ITHET)}, booktitle = {International Conference on Information Technology Based Higher Education and Training (ITHET)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ITHET61869.2024.10837664}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-22759}, pages = {1 -- 5}, abstract = {Many higher education institutions (HEIs) are implementing or reviewing the implementation of predictive Learning Analytics to improve learning processes, identify students at risk or provide personalized learning paths. The responsibility for selecting, reviewing, implementing and supporting those systems falls on system administrators, an under-researched stakeholder group of Artificial Intelligence in higher education. In this paper, we summarize qualitative insights from a workshop with system administrators in German HEIs. We find that the system administrators are highly aware not only of system requirements but also of the needs of various shareholder groups such as institutional leadership, learners and educators and that they put high emphasis on ethical, transparent and compliant system use. We conclude that system administrators should be involvedmore in research on the use of technology in education and that AI systems used in education need to provide possibilities to sufficiently test the system, including anonymous yet realistic test scenarios and data.}, language = {en} } @unpublished{SimbeckSchauerFernsel, author = {Simbeck, Katharina and Schauer, Sophie and Fernsel, Linda}, title = {The System Admin's Perspective: A Discussion on AI in Education with LMS Admins}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-20139}, pages = {10}, abstract = {Many higher education institutions (HEIs) are implementing or reviewing the implementation of predictive Learning Analytics to improve learning processes, identify students at risk or provide personalized learning paths. The responsibility for selecting, reviewing, implementing and supporting those systems falls on system administrators, an under-researched stakeholder group of Artificial Intelligence in higher education. In this paper, we summarize qualitative insights from a workshop with system administrators in German HEIs. We find that the system administrators are highly aware not only of system requirements but also of the needs of various shareholder groups such as institutional leadership, learners and educators and that they put high emphasis on ethical, transparent and compliant system use. We conclude that system administrators should be involved more in research on the use of technology in education and that AI systems used in education need to provide possibilities to sufficiently test the system, including anonymous yet realistic test scenarios and data.}, subject = {Moodle}, language = {en} } @article{Simbeck, author = {Simbeck, Katharina}, title = {They shall be fair, transparent, and robust: auditing learning analytics systems}, series = {AI and Ethics}, volume = {4}, journal = {AI and Ethics}, number = {2}, publisher = {Springer Nature}, issn = {2730-5953}, doi = {10.1007/s43681-023-00292-7}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-19076}, pages = {555 -- 571}, abstract = {In the near future, systems, that use Artificial Intelligence (AI) methods, such as machine learning, are required to be certified or audited for fairness if used in ethically sensitive fields such as education. One example of those upcoming regulatory initiatives is the European Artificial Intelligence Act. Interconnected with fairness are the notions of system transparency (i.e. how understandable is the system) and system robustness (i.e. will similar inputs lead to similar results). Ensuring fairness, transparency, and robustness requires looking at data, models, system processes, and the use of systems as the ethical implications arise at the intersection between those. The potential societal consequences are domain specific, it is, therefore, necessary to discuss specifically for Learning Analytics (LA) what fairness, transparency, and robustness mean and how they can be certified. Approaches to certifying and auditing fairness in LA include assessing datasets, machine learning models, and the end-to-end LA process for fairness, transparency, and robustness. Based on Slade and Prinsloo's six principals for ethical LA, relevant audit approaches will be deduced. Auditing AI applications in LA is a complex process that requires technical capabilities and needs to consider the perspectives of all stakeholders. This paper proposes a comprehensive framework for auditing AI applications in LA systems from the perspective of learners' autonomy, provides insights into different auditing methodologies, and emphasizes the importance of reflection and dialogue among providers, buyers, and users of these systems to ensure their ethical and responsible use.}, subject = {Fairness}, language = {en} }