[2604.07102] The Impact of Steering Large Language Models with Persona Vectors in Educational Applications

Computer Science > Computation and Language arXiv:2604.07102 (cs) [Submitted on 8 Apr 2026] Title:The Impact of Steering Large Language Models with Persona Vectors in Educational Applications Authors:Yongchao Wu, Aron Henriksson View a PDF of the paper titled The Impact of Steering Large Language Models with Persona Vectors in Educational Applications, by Yongchao Wu and Aron Henriksson View PDF HTML (experimental) Abstract:Activation-based steering can personalize large language models at inference time, but its effects in educational settings remain unclear. We study persona vectors for seven character traits in short-answer generation and automated scoring on the ASAP-SAS benchmark across three models spanning two architectures. Persona steering lowers answer quality overall, with much larger effects on open-ended English Language Arts (ELA) prompts than on factual science prompts; interpretive and argumentative tasks are up to 11x more sensitive. On the scoring side, we observe predictable valence-aligned calibration shifts: evil and impolite scorers grade more harshly, while good and optimistic scorers grade more leniently. ELA tasks are 2.5-3x more susceptible to scorer personalization than science tasks, and the Mixture-of-Experts model shows roughly 6x larger calibration shifts than the dense models. To our knowledge, this is the first study to systematically examine the effects of activation-steered persona traits in educational generation and scoring, and the res...