AI-VT: An Example of CBR that Generates a Variety of Solutions to the Same Problem

Henriet, Julien; Greffier, Françoise

doi:10.1007/978-3-030-01081-2_9

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The AI-Virtual Trainer (AI-VT) is a research project that combines skill and sub skill logic with a database to store and assesses data in order to track the user's progress and learning evolution. It is an intelligent, personalized tutoring system based on case-based reasoning (Henriet & Greffier, 2018). The objective of AI-VT is not only assisting teachers in creating and managing their lessons but also to primarily encourage users (in this case student learners) to improve their skills over the course of a training cycle composed of several drill sessions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for the detection of acquired and non-acquired skills in students' algorithmic assessments

Carvalho

Henriet²,

Greffier³

et al. 2023

JEELR

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This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.

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Section: Introductionmentioning

confidence: 99%