Maria Cutumisu

Affiliate Member

Associate Professor, McGill University

Research Topics

AI and Healthcare

AI Ethics

AI in Health

Applied Machine Learning

Cognition

Data Mining

Data Science

Deep Learning

Deep Neural Networks

Generative Models

Human-AI interaction

Human-Computer Interaction (HCI)

Large Language Models (LLM)

Learning

Machine Learning Theory

Memory

Multi-Agent Systems

Natural Language Processing

Reinforcement Learning

Website

Google Scholar

Biography

Maria Cutumisu is an associate professor in Learning Sciences at the Department of Educational and Counselling Psychology, Faculty of Education, McGill University. She is also an Adjunct Professor in the Department of Computing Science, Faculty of Science, University of Alberta as well as an Adjunct Professor in the Faculty of Education, University of Alberta. Previously, she was a tenured Associate Professor in the Department of Educational Psychology, Faculty of Education, at the University of Alberta, where she established her lab in 2015 in the area of Measurement, Evaluation, and Data Science affiliated with the Centre for Research in Applied Measurement and Evaluation (CRAME).

She graduated with an M.Sc. and a Ph.D. in Computing Science from the Department of Computing Science, University of Alberta and she trained as a postdoctoral scholar in Learning Sciences at the Stanford Graduate School of Education's AAA Lab. Her research draws on computing science and educational psychology and has been funded by tri-council grants and scholarships as a principal investigator (NSERC DGs, NSERC CGS-D, SSHRC IG, and SSHRC IDGs) and as a co-principal investigator (SSHRC IGs, SSHRC IDG, SSHRC PDG, NSERC CREATE, and CIHR). In 2025, she was included on the Elsevier and Stanford University list of the world's top 2% scientists based on citations from 2024.

Her research interests include feedback processing and memory (SSHRC IDGs), machine learning and educational data mining for automated feedback generation (NSERC DGs), game-based assessments that support learning and performance-based learning (SSHRC IGs), computational thinking and data literacy (CanCode Callysto grants, CCTt tests, and SSHRC IG), AI in games (reinforcement learning in computer role-playing games) and non-player character (NPC) behaviours (NSERC CGS-D), and serious games (the RETAIN game for neonatal resuscitation; FRQS). She employs learning analytics to investigate the impact of K-16 student choices (e.g., willingness to seek critical feedback and to revise) and mindset on learning outcomes in an online game-based assessment to understand how prepared students are to learn and innovate. She uses psychophysiological technology (eye-tracking and electrodermal activity wearables) to provide a comprehensive understanding of student learning and memory processes (SSHRC IDG, Killam).

Publications

A systematic review of human-LLM interactions in computational thinking empirical studies

Yimei Zhang

You Song

Doina Precup

Reihaneh Rabbany

Maria Cutumisu

2026-05-11

Computer Science Education (published)

doi.org

Seasonal Peaks and Climatic Predictors of Chronic Urticaria: A Global Google Trends Analysis

Katya Peri

Connor Prosty

Han Zhang Huang

Catherine Silotch

Gazal Javed

Maxine Joly-Chevrier

Moshe Ben-Shoshan

Elham Rahme

Ivan V Litvinov

Maria Cutumisu

Qiuyan Yu

Elena Netchiporouk

2026-04-27

Journal of Cutaneous Medicine and Surgery (published)

doi.org

Assessing Computational Thinking Skills in K–12 Education: A Systematic Review

Yimei Zhang

Yajie Song

Maria Cutumisu

2026-04-24

Technology, Knowledge and Learning (published)

doi.org

Profiling Pre-service Teachers’ Computational Thinking

Tanya Chichekian

Maria Cutumisu

Annie Savard

Yi-Mei Zhang

Computational thinking (CT) is a vital skill set for pre-service teachers who will need to foster computational literacy in K–12 classroom… (see more)s, yet the factors influencing their CT skills remain less understood than those for K–12 students or in-service teachers. This study leverages multimodal data to investigate how pre-service teachers (n=128) differ in CT skills, the predictive role of metacognitive strategies and prior coding experience, and variations in online behaviours. Using latent profile analysis, we identified three profiles based on digital literacy, problem-solving, and coding comfort (Novice, Developing, and Proficient), revealing heterogeneity in CT, and supporting non-linear skill acquisition. Linear discriminant analysis revealed that metacognitive strategies and prior coding experience significantly predict profile membership, validating the interplay of technical and cognitive factors in the development of CT skills. Behavioural data from an interactive problem-solving task showed that, compared to Novices and Developing learners, Proficient learners were more task efficient and perceived fewer challenges during task completion. Implications for designing a learning analytics dashboard to visualize profiles and behavioural metrics to support adaptive, equitable, and personalized teacher training are discussed, thereby enhancing pre-service teachers’ readiness to integrate CT into K–12 education.

2026-03-13

Journal of Learning Analytics (published)

doi.org

<b>A Systematic Literature Review of Automated Feedback Generation in Education</b><b></b>

Yajie Song

Yimei Zhang

Maria Cutumisu

Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Au… (see more)tomatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation.

2026-02-28

International Journal of Technology in Education (published)

doi.org

Using an eye tracker to capture reading skills as measured by a digital adaptation of TOWRE-2

Maria Cutumisu

Krystle-Lee Turgeon

2025-12-31

Educ. Inf. Technol. (published)

doi.org

Large Language Model Applications in the Algebra Domain: A Systematic Review

Yajie Song

Yimei Zhang

Doina Precup

Reihaneh Rabbany

Maria Cutumisu

2025-12-05

Technology, Knowledge and Learning (published)

doi.org

Employing Machine Learning to Predict Medical Trainees’ Psychophysiological Responses and Self- and Socially- Shared Regulated Learning Strategies While Completing Medical Simulations

Matthew Moreno

Keerat Grewal

Maria Cutumisu

Jason M. Harley

2025-07-17

Educational Psychology Review (published)

doi.org

Using machine learning algorithms to predict students' general self-efficacy in PISA 2018

Bin Tan

Hao-Yue Jin

Maria Cutumisu

2025-06-30