Katerina Christhilf Sabino

Graduate Research Associate,

Learning Engineering Institute

[email protected]

Mail code: 1104

Campus: Tempe
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Student Information

Graduate Student
Psychology
The College of Lib Arts & Sci

Long Bio

Katerina is a graduate psychology student and a research assistant in the Learning Engineering Institute. She is passionate about helping students achieve their goals and find joy in learning. Her research focuses on developing educational technologies to assess and support students' comprehension of complex expository texts. Specifically, she is interested in using generative artificial intelligence, machine learning, natural language processing, and educational games to both gauge students’ comprehension skills and provide personalized strategy support. She is also interested in discovering more about how different aspects of knowledge and text relate to comprehension performance. Katerina has experience using a variety of tools and programs to support her research: Python, R, Javascript/CSS/HTML, SPSS, JASP, G*Power, Qualtrics, TAACO, Coh-Metrix, ChatGPT, Gemini, and Canva.

Education

M.A. Psychology, Arizona State University, 2023
B.S. Cognitive Sciences, University of California, Irvine, 2021
B.A. Dance, University of California, Irvine, 2021

Curriculum Vitae

Google Scholar URL

https://scholar.google.com/citations?user=-iswAQ0AAAAJ&hl=en&oi=ao

ORCID Profile ID

https://orcid.org/0000-0003-3901-8665

Research Interests

Reading comprehension, personalization, design for education, educational technology, game-based learning, generative AI, machine learning, natural language processing

Research Group

Learning Engineering Institute

Science of Learning and Educational Technology Lab

Publications

Journal Articles

Christhilf, K., Potter, A., Magliano, J. P., McCarthy, K. S., Allen, L. K., & McNamara, D. S. (2025). Constructed responses as a window into strategic processing: The role of prompts in multiple-document reading. Discourse Processes, 1–25. https://doi.org/10.1080/0163853X.2025.2578594

McNamara, D. S., Newton, N. N., Christhilf, K., McCarthy, K., Magliano, J., & Allen, L. (2023). Anchoring your bridge: The importance of paraphrasing to inference making in self-explanations. Discourse Processes. 60(4-5), 337-362. https://doi.org/10.1080/0163853X.2023.2225757

Fang, Y., Li, T., Huynh, L. Christhilf, K., Roscoe, R. D., & McNamara, D. S. (2023). Stealth Literacy Assessments via Educational Games. Computers, 12(7). https://doi.org/10.3390/computers12070130

Conference Proceedings

Christhilf, K., Gong, J., & McNamara, D. S. (2024). Context-embedded knowledge tracing and latent concept detection in a reading game. Proceedings of the Eleventh ACM Conference on Learning @ Scale. https://doi.org/10.1145/3657604.3664674

Watanabe, M., Imundo, M., Christhilf, K., Arner, T., & McNamara, D. S. (2024). Building reading comprehension and knowledge with iSTART: An ITS to provide formative feedback in reading instruction at scale. Proceedings of the Eleventh ACM Conference on Learning @ Scale. https://dl.acm.org/doi/pdf/10.1145/3657604.3664710

Christhilf, K., Roscoe, R., & McNamara, D. S. (2024). Profiles of performance: Game-based assessment of reading comprehension skill. Proceedings of the 20th International Conference on Intelligent Tutoring Systems. https://link.springer.com/chapter/10.1007/978-3-031-63031-6_4

Christhilf, K., Newton, N., Butterfuss, R., McCarthy, K. S., Allen, L. K., Magliano, J. P., & McNamara, D. S. (2022). Using Markov models and random walks to examine strategy use of more or less successful comprehenders. In A. Mitrovic and N. Bosch, editors, Proceedings of the 15th International Conference on Educational Data Mining, pages 484–491, Durham, United Kingdom, July 2022. International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED624145.pdf

Research Activity

Active

ReQUESTA: Evaluating a multi-agent system that generates comprehension questions for expository texts. Developed an expert evaluation rubric, and currently comparing questions from ReQUESTA and base ChatGPT 5.0 to determine whether ReQUESTA represents an improvement in question quality.

AI-X Toolkit: Designing a toolkit to support novice creators of generative AI tools. Specifically focused on creating a set of resources to support, (1) exploring an educational challenge, (2) creating an AI tool, (3) implementing an AI tool, and (4) investigating the effectiveness of an AI tool in supporting user goals.