Toward privacy-focused personalization: Designing a learning experience to facilitate privacy-personalization trade-off

In recent years, as a result of the emergence of innovative technologies and applications, several solutions aiming to improve the quality of education have been appearing. However, mainly due to the human factor of lack of understanding and trust in these applications, adoption by higher education institutions remained low. In my doctoral thesis, I aim to contribute to overcoming this barrier in two main steps. First, I try to understand more deeply the nature of the existing contrast between perceived benefits and concerns of higher education students regarding one specific application area, artificial intelligence-based personalized learning. Building on these results, I will then design and evaluate a new personalized learning tool, developed specifically with user-centered data privacy and ethical considerations in mind that specifically respond to the identified concerns. The uniqueness of this tool is the transparency and controllability of different levels of personalization, which ensures that each student can freely choose the extent to which they are willing to make their data available in order to receive a personalized learning experience. The effects of this design principle on privacy concerns and learning outcomes will then be tested in multiple lab- and field studies. I believe this project fits well into both the “Personalizing Learning Experiences through User Modeling” and the “Fairness, Transparency, Accountability, and Privacy” tracks of the UMAP conference, which is why I have decided to apply for the Doctoral Consortium.