DREAN Teach Me: An Intelligent Tutoring System with NLP and Adaptive Feedback for Elementary School Students

Betty Cotrina; Bryan Perez; Sandra Wong-Durand; Pedro Castaneda; Alejandra Onate-Andino

doi:10.48084/etasr.16487

Authors

Betty Cotrina Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, San Isidro, Lima, Peru https://orcid.org/0009-0008-8829-5347
Bryan Perez Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, San Isidro, Lima, Peru https://orcid.org/0009-0002-2521-4366
Sandra Wong-Durand Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, San Isidro, Lima, Peru https://orcid.org/0000-0002-6154-2124
Pedro Castaneda Faculty of Systems Engineering and Electrical Mechanics, Universidad Nacional Toribio Rodriguez de Mendoza, Amazonas, Peru https://orcid.org/0000-0003-1865-1293
Alejandra Onate-Andino Escuela Superior Politecnica de Chimborazo (ESPOCH), Riobamba, Ecuador

Volume: 16 | Issue: 3 | Pages: 34764-34772 | June 2026 | https://doi.org/10.48084/etasr.16487

Received: 25 November 2025 | Revised: 21 January 2026, 19 February 2026, 27 February 2026, and 6 March 2026 | Accepted: 8 March 2026 | Online: 6 June 2026

Corresponding author: Sandra Wong-Durand

Abstract

Automated instructional reinforcement is a promising approach to improving learning in elementary education, particularly when personalized and continuous feedback is required. This work presents a web-based intelligent tutoring application leveraging Natural Language Processing (NLP) that delivers automatic feedback based on students' responses to subject-area assessments. The system was applied in Mathematics, Language Arts, and Science & Technology. Effectiveness was evaluated using a quasi-experimental single-group design with pretest and posttest measurements, comparing performance before and after the intervention. The tutoring system adapts reinforcement to baseline performance and operates through graduated levels of support. To estimate the impact, we employed a paired t-test and Cohen's d effect size. The results indicate a mean gain of 0.56 points, p < 0.001, and d = 0.73 (moderate to large), with the greatest improvement in Language Arts. These findings show that automated reinforcement contributed significantly to improved academic performance, being especially effective for communicative competencies. This underscores the potential of intelligent technologies to transform traditional instruction into a more adaptive and effective process.

Keywords:

Intelligent Tutoring System (ITS), Natural Language Processing (NLP), educational reinforcement, adaptive learning, elementary education

References

H. B. Essel, D. Vlachopoulos, A. Tachie-Menson, E. E. Johnson, and P. K. Baah, "The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education," International Journal of Educational Technology in Higher Education, vol. 19, no. 1, Nov. 2022, Art. no. 57.

OECD, PISA 2022 Results (Volume I): The State of Learning and Equity in Education. Paris, France: OECD Publishing, 2023.

W. E. Villegas-Ch, J. Govea, R. Gutierrez, and A. Mera-Navarrete, "Improving Interaction and Assessment in Hybrid Educational Environments: An Integrated Approach in Microsoft Teams With the Use of AI Techniques," IEEE Access, vol. 12, pp. 93723–93738, 2024.

K. S. Suryanarayana, V. S. P. Kandi, G. Pavani, A. S. Rao, S. Rout, and T. Siva Rama Krishna, "Artificial Intelligence Enhanced Digital Learning for the Sustainability of Education Management System," The Journal of High Technology Management Research, vol. 35, no. 2, Nov. 2024, Art. no. 100495.

L. Jürgensmeier and B. Skiera, "Generative AI for scalable feedback to multimodal exercises," International Journal of Research in Marketing, vol. 41, no. 3, pp. 468–488, Sept. 2024.

J. Meyer et al., "Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions," Computers and Education: Artificial Intelligence, vol. 6, June 2024, Art. no. 100199.

R. Schiller, J. Fleckenstein, U. Mertens, A. Horbach, and J. Meyer, "Understanding the effectiveness of automated feedback: Using process data to uncover the role of behavioral engagement," Computers & Education, vol. 223, Dec. 2024, Art. no. 105163.

J. Han and M. Li, "Exploring ChatGPT-supported teacher feedback in the EFL context," System, vol. 126, Nov. 2024, Art. no. 103502.

M. Jukiewicz, "The future of grading programming assignments in education: The role of ChatGPT in automating the assessment and feedback process," Thinking Skills and Creativity, vol. 52, June 2024, Art. no. 101522.

H. Li, "Effects of a ChatGPT-based flipped learning guiding approach on learners’ courseware project performances and perceptions," Australasian Journal of Educational Technology, vol. 39, no. 5, pp. 40–58, Dec. 2023.

Y.-H. Hu, C.-L. Hsieh, and E. S. N. Salac, "Advancing freshman skills in information literacy and self-regulation: The role of AI learning companions and Mandala Chart in academic libraries," The Journal of Academic Librarianship, vol. 50, no. 3, May 2024, Art. no. 102885.

R. Zhao, Y. Zhuang, Z. Xie, and P. L. H. Yu, "Facilitating self-directed language learning in real-life scene description tasks with automated evaluation," Computers & Education, vol. 219, Oct. 2024, Art. no. 105106.

K. A. Aldriwish, "Empowering Learning through Intelligent Data-Driven Systems," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12844–12849, Feb. 2024.

F. Naseer, M. N. Khan, M. Tahir, A. Addas, and S. M. H. Aejaz, "Integrating deep learning techniques for personalized learning pathways in higher education," Heliyon, vol. 10, no. 11, June 2024, Art. no. e32628.

A. Bressane et al., "Understanding the role of study strategies and learning disabilities on student academic performance to enhance educational approaches: A proposal using artificial intelligence," Computers and Education: Artificial Intelligence, vol. 6, June 2024, Art. no. 100196.

M. Lohakan and C. Seetao, "Large-scale experiment in STEM education for high school students using artificial intelligence kit based on computer vision and Python," Heliyon, vol. 10, no. 10, May 2024, Art. no. e31366.

J. Divasón, F. J. Martínez-de-Pisón, A. Romero, and E. Sáenz-de-Cabezón, "Artificial Intelligence Models for Assessing the Evaluation Process of Complex Student Projects," IEEE Transactions on Learning Technologies, vol. 16, no. 5, pp. 694–707, Oct. 2023.

X. Chen, D. Zou, H. Xie, G. Cheng, and C. Liu, "Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions," Educational Technology & Society, vol. 25, no. 1, pp. 28–47, Jan. 2022.

S. Wang, F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du, "Artificial intelligence in education: A systematic literature review," Expert Systems with Applications, vol. 252, Oct. 2024, Art. no. 124167.

D. Lee et al., "The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives," Computers and Education: Artificial Intelligence, vol. 6, June 2024, Art. no. 100221.

T. K. F. Chiu, "The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney," Interactive Learning Environments, vol. 32, no. 10, pp. 6187–6203, Nov. 2024.

R. F. Kizilcec et al., "Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States," Computers and Education: Artificial Intelligence, vol. 7, Dec. 2024, Art. no. 100269.

A. C. Graesser, K. VanLehn, C. P. Rose, P. W. Jordan, and D. Harter, "Intelligent Tutoring Systems with Conversational Dialogue," AI Magazine, vol. 22, no. 4, pp. 39–39, Dec. 2001.