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Digital educational technologies offer the potential to customize students'experiences and learn what works for which students, enhancing the technologyas more students interact with it. We consider whether and when attempting todiscover how to personalize has a cost, such as if the adaptation to personalinformation can delay the adoption of policies that benefit all students. Weexplore these issues in the context of using multi-armed bandit (MAB)algorithms to learn a policy for what version of an educational technology topresent to each student, varying the relation between student characteristicsand outcomes and also whether the algorithm is aware of these characteristics.Through simulations, we demonstrate that the inclusion of studentcharacteristics for personalization can be beneficial when thosecharacteristics are needed to learn the optimal action. In other scenarios,this inclusion decreases performance of the bandit algorithm. Moreover,including unneeded student characteristics can systematically disadvantagestudents with less common values for these characteristics. Our simulations dohowever suggest that real-time personalization will be helpful in particularreal-world scenarios, and we illustrate this through case studies usingexisting experimental results in ASSISTments. Overall, our simulations showthat adaptive personalization in educational technologies can be a double-edgedsword: real-time adaptation improves student experiences in some contexts, butthe slower adaptation and potentially discriminatory results mean that a morepersonalized model is not always beneficial.

Getting too personal(ized): The importance of feature choice in online adaptive algorithms