Abstract
This chapter presents a state of the art in the design of digital environments for mathematics education, with a particular focus on artificial intelligence techniques. A review of the work done in this area over the last few decades highlights current challenges and distinguishes between symbolic approaches and machine learning. About symbolic approaches, we review automatic reasoning tools in geometry and their potential. We also consider the design and research work around the Casyopée environment and the use of logic programming in the QED-Tutrix intelligent tutoring system. With respect to machine learning, four classes of techniques constitute contemporary AI in computer science. Two examples are discussed: a deep learning system of monument analysis for learning situations in mathematics, technology and art, and a computer classroom simulator that provides a new approach to training teachers.
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Notes
- 1.
This state of the art relies on the current research literature. We draw particularly from Lagrange and Abboud (2018) for sections “Introduction” and “A CAS Kernel in a Digital Learning Environment: The Case of Casyopée” and from Richard et al. (2022) for the remaining sections.
- 2.
The most advanced version is at https://github.com/kovzol/geogebra-discovery/
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Third author is partially supported by the grant PID2020-113192GB-I00 (Mathematical Visualization: Foundations, Algorithms and Applications) from the Spanish MCIN/AEI
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Lagrange, JB., Richard, P.R., Vélez, M.P., Van Vaerenbergh, S. (2023). Artificial Intelligence Techniques in Software Design for Mathematics Education. In: Pepin, B., Gueudet, G., Choppin, J. (eds) Handbook of Digital Resources in Mathematics Education. Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-030-95060-6_37-1
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