Autumn school Artificial Intelligence and Education

17-25 October, Nancy, France

Overview

With the generalization of Artificial Intelligence, the field of education is changing fundamentally. AI contributes to the appearance of new ways to teach and learn, which can make the educational experience more efficient and engaging. For example, it is a way to solve the « one size fits all » problem faced by traditional teaching. Artificial Intelligence is thus becoming a hot topic in education. Keywords as customisation, dashboards, intelligent tutors, learning analytics, quality improvement, student motivation, engagement, etc. are now buzz words in the domain.

However, introducing AI in education cannot come down to designing and using AI tools. Dimensions such as pedagogy, social and educational sciences, psychology also have to be considered. AI in education is thus a multidisciplinary domain.

As part of the “AI and Education: Modeling real-world problems” autumn school, we aim to understand, foster and support the development of AI in education, by considering several disciplines, topics and points of view, from theoretical to practical problems.

To this end, the school provides basic concepts, state-of-the-art and new insights in several domains, such as educational data mining, statistical learning, knowledge management, user (teacher/learner) experience, co-design, personalised stakeholders support, acceptance, adoption, evaluation, etc., considering obviously the links between them.

The summer school will proceed in three successive phases:

From 17th October to 21st October, researchers in the field of education and AI will give lectures covering a broad range of the previously mentioned topics. Interaction between instructors and participants will highly be encouraged during these lectures.

22nd October and 23rd October, participants will attend the LSAC conference, dedicated to – talks by researchers, practitioners and industrials of practical experiments as well as theoretical proposals, - discussions about privacy, ethics and policies.

24th October and 25th October participants will take part in a two-days datathon to apply the notions presented in the preceding week. Multidisciplinary teams will compete on an innovative real-world problem with real data. The winning team will receive a prize worth 300 euros.


The ‘AI and Education: Modeling real-world problems’ summer school will be held as a co-located event of the LSAC conference from Thursday the 17th of October to Friday the 25th of October, in Nancy, France.

Target Audience

Any practitioner, Ph.D. student, post-doc, researcher, or Master student, interested in AI or in education is invited to attend the school, whatever is his/her main area of knowledge (education, psychology, computer science, etc.).

As it is a multidisciplinary school, no specific background is required to attend it, an interest in all disciplines will be appreciated.



Originality of the Summer School

The school involves three phases, from theoretic (first phase) to practical (datathon), through discussions and presentations of practical experiences by researchers and teachers. It will be rich and dynamic.

The school is multidisciplinary, the main discipline of the participants will range from computer science to psychology and includes education, statistics, etc. This diversity of topics and the variety of participants will make this school full of fruitful discussions and works.

Social events

Four social events the autumn school will take place during the autumn school.

On Thursday the 17th: an opening cocktail that will be the opportunity for the attendees to start knowing each other.

On Sunday the 20th: a visit to Nancy City. You will discover our nice city, UNESCO classified for its 18th century Place Stanislas.

On Tuesday, the 22nd: a gala dinner with attendees of the LSAC conference.

On Wednesday the 23rd: a dinner with all the participants of the datathon. New attendees will join us, you will meet each of them, and start discussions with your teammates and other teams.



How to Pre-register and register, registration fees

Participation at the autumn school requires pre-registration via: http://enquetes.univ-lorraine.fr/index.php/883718?lang=en.

The autumn school has a limited number of places, so the allocation of places will be on a first-come, first-served basis.

Pre-registration will remain open until the capacity limit is reached.

Reserving an accommodation lies in the responsibility of participants.

Recommended dormitories and hotels are on the website.

Registration fees

Registration fees include coffee breaks (twice each day) and lunch for each day (except Sunday the 20th), and the four social events.

Registration closes on 1st October.


Students (PhD Thesis and Master): 350 €

Academic (Post Doc, Researchers): 400 €

Private attendee: 600 €

Reduced fees for Université de Lorraine students.

If you require any further information, feel free to contact Armelle Brun (armelle . brun at loria . fr)



Program

Thursday, 17th October

Location: Pôle Lorrain de Gestion - 13 rue Michel Ney 54000 Nancy - Room 225/226 (2nd Floor)
10:00 10:30 Welcome
10:30 11:00 Coffee Break
11:00 12:30 What is AI and Education, where are we today ? Evolution of the domain, already tackled and remaining challenges
Vanda Luengo, Sorbonne université-LIP6, researcher in e-learning technology-enhanced learning and computer science
12:30 14:00 Lunch Break (together)
14:00 15:30 Usability, acceptance of numeric tools by learners
Jérôme Dinet, Université de Lorraine, researcher in psychology and ergonomics, specialised in learning and memory
15:30 16:00 Coffee Break
16:00 17:30 Presentation of important learning analytics projects
Vanda Luengo, Sorbonne université-LIP6, researcher in e-learning technology-enhanced learning and computer science

Friday, 18th October

Location: Pôle Lorrain de Gestion - 13 rue Michel Ney 54000 Nancy - Room 225/226 (2nd Floor)
9:00 10:30 Personalisation and adaptative tests : design, evaluation and student performance prediction
Fabrice Popineau, CentraleSupelec, researcher in technology-enhanced learning and computer science
Part 1
10:30 11:00 Coffee Break
11:00 12:30 Personalisation and adaptative tests : design, evaluation and student performance prediction
Fabrice Popineau, CentraleSupelec, researcher in technology-enhanced learning and computer science
Part 2
12:30 14:00 Lunch Break (together)
14:00 15:30 Support for teachers and learners (design and usage of tools)
Eric Bruillard, ENS Paris Saclay, researcher in e-learning and technology-enhanced learning
Part 1
15:30 16:00 Coffee Break
16:00 17:30 Support for teachers and learners (design and usage of tools)
Eric Bruillard, ENS Paris Saclay, researcher in e-learning and technology-enhanced learning
Part 2

Saturday, 19th October

Location: Pôle Lorrain de Gestion - 13 rue Michel Ney 54000 Nancy - Room 225/226 (2nd Floor)
9:00 10:30 Learners-centered design of digital devices, focus on dashboards
Stéphanie Fleck and Geoffray Bonnin, Université de Lorraine, respectively researchers in HCI, ergonomics and in computational science
Part 1
10:30 11:00 Coffee Break
11:00 12:00 Learners-centered design of digital devices, focus on dashboards
Stéphanie Fleck and Geoffray Bonnin, Université de Lorraine, respectively researchers in HCI, ergonomics and in computational science
Part 2
12:00 13:30 Lunch Break (together)
13:30 15:30 Human-centered data science
Ilya Goldin, Phenom People (USA)
Part1
15:30 16:00 Coffee Break

Sunday, 20th October

Day off
14:00 15:30 Guided tour of the city center

Monday, 21st October

Location: Pôle Lorrain de Gestion - 13 rue Michel Ney 54000 Nancy - Room 225/226 (2nd Floor)
9:00 10:30 Collaborative learning and technology-enhanced learning
Marie-Hélène Abel, UTC (Université Technologique de Compiègne), researcher in e-learning
Part 1
10:30 11:00 Coffee Break
11:00 12:30 Collaborative learning and technology-enhanced learning
Marie-Hélène Abel, UTC (Université Technologique de Compiègne), researcher in e-learning
Part 2
12:30 14:00 Lunch Break (together)
14:00 15:30 Educational data mining, focus on prediction
Agathe Merceron. Beuth University of Applied Science, Germany, researcher in data mining and in e-learning
Part 1
15:30 16:00 Coffee Break
16:00 17:30 Educational data mining, focus on prediction
Agathe Merceron. Beuth University of Applied Science, Germany, researcher in data mining and in e-learning
Part 2

Tuesday, 22nd October and Wednesday, 23rd October

Location: LORIA Laboratory - Campus Scientifique 54500 Vandoeuvre les Nancy - Locate Us
Event : LSAC Conference

Thursday, 24th October and Friday, 25th October

Location: LORIA Laboratory - Campus Scientifique 54500 Vandoeuvre les Nancy - Locate Us
Event : Datathon - Multidisciplinary Teams

End of the school : Friday, 25th October at 17:00

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