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   Several "Artificial Intelligence for Learning" Internship Offers 
   https://aile.comin-ocw.org
   Employer and Location: LS2N UMR CNRS - University of Nantes and IMT Atlantique, France
   Duration: 3-6 months
   Starting Date: Spring/Summer 2019 
   Deadline for Application: Open until filled
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3-6 Month "Artificial Intelligence for Learning" Internship Offers
https://aile.comin-ocw.org

The University of Nantes/LS2N Lab. (ls2n.fr) and IMT Atlantique (imt-atlantique.fr) aim at recruiting several 3-6 months interns to join our teams, starting in Spring/Summer of 2019. 

The students are intended to work on research projects targeted at the Learning, Teaching and Education domains we have got running. A brief description of each internship topics is given below. The Artificial Intelligence domains concerned are Machine Learning, Data mining, Natural Language Processing, Distributed Data Management, Web Semantic, Learning analytics, User Interaction modeling.

If you are interested or have any questions, please do not hesitate to contact Olivier Aubert (olivier.aubert@univ-nantes.fr), Nicolas Hernandez (nicolas.hernandez@univ-nantes.fr) and the referrer of the internship topic.
To apply, send a curriculum vitae together with your academic results and a motivation letter and indicate the topics of interest. 

Context and objectives
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  Artificial Intelligence is sometimes described as the new electricity. As such, it is supposed to have a profound influence over many fields, of which Education has been repeatedly singled out. 

  At University of Nantes, IMT Atlantique and Ecole Centrale, researchers have taken on questions relating AI and education for some time now through several national and international projects such as COCo (coconotes.comin-ocw.org), Hubble (hubblelearn.imag.fr), PASTEL (projets-lium.univ-lemans.fr/pastel), SEDELA (sedela.cominlabs.u-bretagneloire.fr), eFIL (efil.cominlabs.u-bretagneloire.fr), X5-GON (www.x5gon.org), ClassCode (pixees.fr/classcode-v2). 

  The AILE (Artificial Intelligence for Learning Environment) project (https://aile.comin-ocw.org) aims at strengthening this eco-system, to develop some regular scientific activities (seminars, joint events), and to prepare their activities in the theme over the next 5 years.

Internship topics (sujets de stage) list
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  In addition to the topic study, the students will also participate to transversal team activities involving simultaneously all the students and the researchers (research reading groups, seminars, conference organization).

Measuring the hardness of an educational resource 
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  - Many educational resources are available on the web but it is important to evaluate automatically the age group the resource is intended for and the level required to understand it. This task requires the use of machine learning. This project is principally based
on X5-GON data.
  - Referrers: Colin de la Higuera (cdlh@univ-nantes.fr), LS2N TALN team 

Detecting the theme shifts in a lecture 
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  - In a lecture presented by a one hour video or a 50 pages pdf, the themes vary over time. We aim to use techniques from data science to study the so-called concept drift in this context. This project is based on X5-GON/PASTEL data.
  - Referrers: Nicolas Hernandez (nicolas.hernandez@univ-nantes.fr) and Colin de la Higuera (cdlh@univ-nantes.fr), LS2N TALN team

Predicting MOOC attrition
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  - Using learning traces left by MOOC users, and generalizing from previous activities, can we compute attrition indicators? This project is based on HUBBLE data.
  - Referrers: Antoine Pigeau (antoine.pigeau@univ-nantes.fr), LS2N DUKe team

Mixing AI techniques to give relevant insights on Mooc attrition 
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  - Using learning traces left by MOOC users, how can we mix AI techniques to give relevant insights and explainable results as well. This project is based on HUBBLE data. 
  - Referrers: Serge Garlatti (Serge.Garlatti@imt-atlantique.fr), IHSEV Lab-STICC

Designing feedback
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  - How can we design feedback, for example dashboards that makes those insights explicit and actionable for the users?  This project is based on HUBBLE data.
  - Referrers: Jean-Marie Gilliot (jm.gilliot@imt-atlantique.fr), IHSEV Lab-STICC

Resource evolution
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  - Analyzing user activity would allow to identify activities generating unexpected behaviour, and therefore help resource/MOOCs authors to refactor their content using this information. This project is principally based on FUN MOOCs data.
  - Referrers: Yannick Prié (yannick.prie@univ-nantes.fr), LS2N DUKe team

What next? 
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  - Identification and suggestion of personalized pedagogical paths (made of texts, videos, and other media) according to specific objectives of knowledge and competencies to be acquired by the learner. This is mainly related to X5-GON data.
  - Referrers: Hoël le Capitaine (hoel.lecapitaine@univ-nantes.fr), LS2N DUKe team

From micro-competence to professional project.
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  - Can we support learner to define their personal learning paths, in terms of objectives and aimed competencies? A meta review on AI, competencies and self development will be part of the work;
- Referrers: Jean-Marie Gilliot (jm.gilliot@imt-atlantique.fr) & Issam Rebaï (issam.rebai@imt-atlantique.fr), IHSEV Lab-STICC

Educational datahub
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  - Re-centralizing data is a powerful paradigm to enable semantic indexing, incremental data integration, and query discovery. Many resources exist in education but they are spread around the web. We propose to build a datahub for educational resources. This portal
accepts resources as RDF data and allows query processing across data.
  - Referrers: Hala Skaf (hala.skaf@univ-nantes.fr), LS2N GDD team

Who You Are
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  - Pursuing an Engineering degree or a Masters degree in Artificial Intelligence, Data Science, Natural Language Processing, Machine Learning or a related field
  - Capacity to work independently, as well as collaborate within a team
  - Solid programming skills