Publications

Main Current Funded Project

March 2024 – August 2027

Development of Tools and Methods to Support Writing Learning and Generate Personalized Feedback for its Assessment

Coordinator: Dr. Eliseo Reategui

This project aims to design and evaluate AI-based technological solutions to support students’ writing development and enhance formative assessment practices. It combines text mining, natural language processing, and generative AI to analyze large volumes of student writing, identify patterns of strengths and difficulties, provide personalized feedback, and inform teaching strategies.

Positioned within the field of writing analytics, the project goes beyond traditional automated correction by emphasizing the understanding of the writing process and the active role of teachers in AI-mediated assessment. It also explores the use of both large language models and small language models (SLMs), considering SLMs as efficient, customizable, and scalable alternatives for specific educational tasks.

The project integrates ongoing technological developments and is supported by national and international collaborations.

Institutional partners: Researchers and faculty from the University of Toronto, University College London, Universidad de la República, Instituto Federal do Rio Grande do Sul, and Universidade de Caxias do Sul.

Industry partners: GoMining, an AI company focused on text revision services; and IAdocs, an AI startup focused on the development of educational platforms to support the creation, assessment, and feedback of learning activities.

Publications Related to the Project and Research Agenda

2022 – 2026

Santos, R. A., Beni, P. F., Reategui, E. B., & Barone, D. A. C. (2026). Fairness, Accountability, Transparency, and Ethics (FATE) in Recommendation Systems in Education: A Co-occurrence Analysis of Words. Revista Ibero-Americana de Ciências da Informação. Accepted for publication.

Antunes dos Santos, R., & Reategui, E. (2025). Using generative artificial intelligence and keyword analysis to support the design of research projects in higher education. Latin American Journal of Technology in Education (RELATEC - Spain), 24, 87–104.
https://relatec.unex.es/index.php/relatec/article/view/4911/3002

França, A. B. C., Reategui, E., Mintz, J., Meira, R. R., & Motz, R. (2024). Writing analytics and AI for special education: Preliminary results on students with autism spectrum disorder. In A. M. Olney et al. (Eds.), Communications in Computer and Information Science (pp. 192–199). Springer.
https://link.springer.com/subjects/writing-skills-development-in-children-with-autism-spectrum-disorder

Weiand, A., Reategui, E., & Motz, R. (2025). Using artificial intelligence to guide the learning analytics process. In Lecture Notes in Educational Technology (pp. 481–491). Springer Singapore.
https://doi.org/10.1007/978-981-96-3698-3

Reategui, E. B., Pacheco, G. V., de Mattos, I. P., Prass, L. R., & Bigolin, M. (2025). RIANA: Development of an assistant for ENEM essay correction using Large Language Models (LLMs). In Proceedings of the Brazilian Congress on Informatics in Education – Best Apps Contest (pp. 248–251).
https://sol.sbc.org.br/index.php/cbie_estendido/article/view/38931/38704

Meira, R. R., & Reategui, E. (2025). ATHOS: Computational tool for automated assessment of research project writing. In Proceedings of the Brazilian Congress on Informatics in Education – Thesis and Dissertations Contest (Honorable Mention) (pp. 61–74).
https://sol.sbc.org.br/index.php/cbie_estendido/article/view/38895/38668

Bigolin, M., Reategui, E., García Cabeza, S., & Motz, R. (2023). Student perception of peer review in the digital age. In Lecture Notes in Educational Technology (pp. 242–253). Springer.
https://link.springer.com/chapter/10.1007/978-981-99-7353-8_19

Motz, R., Porta, M., & Reategui, E. (2023). Building resilient educational systems: The power of digital technologies. In Lecture Notes in Educational Technology (pp. 370–383). Springer.
https://link.springer.com/chapter/10.1007/978-981-99-7353-8_28

Galafassi, C., Galafassi, F., Vicari, R. M., & Reategui, E. (2022). EvoLogic: Toward an ITS for teaching propositional logic. International Journal of Artificial Intelligence in Education.
https://link.springer.com/article/10.1007/s40593-021-00287-7

Mazzuco, A., Krassmann, A. L., Reategui, E., & Gomes, R. S. (2022). A systematic review of augmented reality in chemistry education. Review of Education, 10.

Oliveira, S., Reategui, E., Barcellos, P. S. C. C., Bigolin, M., & Carniato, M. (2022). Improving academic writing with a method for text revision supported by text mining. International Journal of Emerging Technologies in Learning, 17, 150–163.
https://online-journals.org/index.php/i-jet/article/view/31249/12211

Reategui, E., Bigolin, M., Carniato, M., & Santos, R. A. (2022). Evaluating the performance of SOBEK text mining keyword extraction algorithm. In A. Holzinger et al. (Eds.), Machine Learning and Knowledge Extraction. Springer.
https://link.springer.com/chapter/10.1007/978-3-031-14463-9_15

Publications Under Review

Collaboration with Northwest A&F University

Reategui, E., & Hu, P.
ChatGPT in language assessment: A comparative study of model performance and prompt specificity.
Submitted to Computer Assisted Language Learning. Under review.

Collaboration with University College London

Mintz, J., França, A. B. C., McCrory, A., Reategui, E., & Rivas, C.
Using holistic classroom interaction analysis to support teacher decision making: The case of teacher communication strategy selection in autism education.
Submitted to Technology, Knowledge and Learning (Springer). Under review.

Co-authored with PhD Students

Antunes dos Santos, R., & Reategui, E.
Co-word analysis for research training in higher education: A systematic review.
Submitted to Education for Information. Under review.

Zarth, A. M. F., & Reategui, E.
Graphic organizers in developing writing skills beyond early literacy stage: A systematic review and emerging directions.
Submitted to Review of Education. Under review.