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EvoLogic: Toward an ITS for Teaching Propositional Logic

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Abstract

This work presents the intelligent tutoring system, EvoLogic, developed to assist students in problems of natural production in propositional logic. EvoLogic has been modeled as a multiagent system composed of three autonomous agents: interface, pedagogical and specialist agents. It supports pedagogical strategies inspired by the theory of example-based learning based on the use of an automated model tracing mechanism. This mechanism eliminates the necessity of the teacher to manually design every possible interaction between students and EvoLogic. It also includes several examples for each propositional rule. A specific study carried out with a set of 10 exercises taken from a real-world scenario is then presented to evaluate EvoLogic’s performance and compare it to that of an earlier ITS (Heráclito) developed for the same purpose. The study showed that EvoLogic was able to provide different (and correct) solutions for the exercises analyzed. By having these solutions available, the model tracing mechanism was able to follow the students’ steps, providing help that was consistent with the line of reasoning pursued by each student. A direct comparison between EvoLogic and Heráclito, which operates using one single line of reasoning for each theorem proof, showed that the model tracing mechanism of EvoLogic was more accurate in guiding students in the exercises. In addition to this positive outcome, EvoLogic’s model tracing mechanism is novel in its use of genetic algorithms to build solutions on the fly for propositional logic exercises started by the students.

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Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001

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Authors and Affiliations

  1. Cristiano Galafassi – Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Cristiano Galafassi

  2. Fabiane Flores Penteado Galafassi, Federal University of Pampa, Bagé, Brazil

    Fabiane Flores Penteado Galafassi

  3. Rosa Maria Vicari – Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Rosa Maria Vicari

  4. Eliseo Berni Reategui, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Eliseo Berni Reategui

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  1. Cristiano Galafassi
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  2. Fabiane Flores Penteado Galafassi
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  3. Rosa Maria Vicari
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  4. Eliseo Berni Reategui
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Correspondence to Cristiano Galafassi.

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Galafassi, C., Galafassi, F.F.P., Vicari, R.M. et al. EvoLogic: Toward an ITS for Teaching Propositional Logic. Int J Artif Intell Educ 33, 35–58 (2023). https://doi.org/10.1007/s40593-021-00287-7

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