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Beschreibung:
Digital study assistant systems are software implementations that aim at supporting students throughout their studying endeavor at higher education institutions. In order to do so, digital study assistant systems may rely on technologies from the domain of Artificial Intelligence to maximize their assistance utility. This thesis investigates the feasibility of deploying Artificial Intelligence (AI) algorithms within a digital study assistant system for self-determined learning. This thesis guides the reader through the development process of the SIDDATA digital study assistant system and its AI-driven features. By adhering to data availability constraints and data protection regulations, a general educational resource recommendation system in the form of an artificial neural network based on Google BERT was developed and integrated into the digital study assistant’s feature set. Through a subsequent investigation into the AI-driven feature usage through quantitative and qualitative means, we discover a high perceived potential for AI technologies to incentivize student self-determined learning. Technical and boundary conditional challenges will need to be overcome to realize this potential for all users in future studies.