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Portail > Offres > Offre UMR5205-NATGUI-002 - Contrat post-doctoral H/F : IA explicable, IA de confiance, IA pour l'éducation

Post-doctoral contract M/F : Explainable AI, Trusted AI, AI for Education

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : vendredi 16 décembre 2022

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General information

Reference : UMR5205-NATGUI-002
Date of publication : Thursday, November 10, 2022
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 3 January 2023
Proportion of work : Full time
Remuneration : Between 2833€ and 4003€ gross monthly salary depending on experience
Desired level of education : PhD
Experience required : 1 to 4 years


This position is part of the COMPER ANR project, whose objective is to design models and tools to implement a competency-based approach to support personalized learning.
Bringing together researchers in computer science, humanities and social sciences and practitioners, this project proposes a model for representing competency frameworks that makes it possible to link the pedagogical activities proposed to learners to their competencies, and to develop a competency profile for each learner. These profiles are used to personalize the activities and learning paths, as well as to help the learner to regulate his or her learning, by including motivational levers.
The project is based on experiments at different levels (high school, university, CAP) involving skills of different granularity in different disciplines, in order to assess the generality of the proposed models and tools.
The personalization process implemented in the project implements a personalization strategy defined by the project team and based on rules, to provide each learner with learning resources adapted to the competency profile developed from their interactions with the learning environment.
The objective of the proposed position is to enable teachers to understand the personalization strategy implemented by the system and to set it up to best suit their needs. The aim is therefore to design a parameterization interface and a process for explaining the system's behavior. These explanations should enable the teacher to understand why a resource is proposed, depending on the learner's mastery of skills and objectives, by exploiting the skills repository.

Reference : Louis Sablayrolles, Marie Lefevre, Nathalie Guin, Julien Broisin. Design and Evaluation of a Competency-based Recommendation Process. Intelligent Tutoring Systems, Jul 2022, Bucharest, Romania. ⟨hal-03642155⟩


- understand the personalization process implemented
- design and develop an interface for teachers to set up the personalization strategy
- design and develop a process for explaining the recommendations provided by the system
- participate in the writing of scientific articles and the presentation of research results


We are looking for a person :
- with a PhD in computer science in one of the following fields: AI in Education, Knowledge Engineering, HCI, AI
- who is interested in digital technologies for education;
- who has - strong web development skills
- practical experience in the field of software development
- knowledge in the field of AI explainability or user appropriation of digital tools;
- who is used to collaborating with other researchers;
- who has a good level of English (reading, writing and speaking).

Work Context

The person recruited will join the TWEAK team of the LIRIS laboratory, located on the Doua campus in Lyon-Villeurbanne and will work under the direction of Nathalie Guin and Marie Lefevre, in collaboration with Julien Broisin (IRIT).

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