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Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Authors Miguel Ferreira , José Paulo Leal

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OASIcs.ICPEC.2026.2.pdf
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Subject Classification

ACM Subject Classification
  • Applied computing → Interactive learning environments
  • Computing methodologies → Natural language generation
Keywords
  • Intelligent Tutoring Systems
  • Large language Models

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Abstract

The research described in this paper explores the integration of Generative Artificial Intelligence (GenAI) into Intelligent Tutoring Systems (ITS) with the aim of improving programming education. Specifically, it extends Agni, a web-based programming learning platform, by automating the construction of key ITS components that are traditionally built manually. The methodology leverages Large Language Models (LLMs) to extract programming concepts from educational materials, map their relationships via concept graphs and diagnose specific student knowledge gaps. Additionally, GenAI provides real-time, contextual feedback during programming exercises, mimicking the personalized support typically offered by a human tutor. The work evaluates whether GenAI can effectively replace or augment the manual creation of domain models while preserving the pedagogical benefits of ITS. The obtained results suggest the potential of combining AI with ITS to improve scalability and reduce manual workload.

Cite As Get BibTex

Miguel Ferreira and José Paulo Leal. Exploiting Generative AI to Scale up Intelligent Tutoring Systems. In 7th International Computer Programming Education Conference (ICPEC 2026). Open Access Series in Informatics (OASIcs), Volume 145, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.ICPEC.2026.2

Author Details

Miguel Ferreira
  • DCC - FCUP, Porto, Portugal
José Paulo Leal
  • CRACS - INESC TEC, Portugal
  • DCC - FCUP, Porto, Portugal

Funding

This work is funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the support UID/50014/2025 (https://doi.org/10.54499/UID/50014/2025).

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