263-5005-00L  Artificial Intelligence in Education

SemesterAutumn Semester 2021
LecturersM. Sachan, T. Sinha
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 75.


263-5005-00 VArtificial Intelligence in Education
Online lecture: This lecture will take place online. Reserved rooms will remain blocked on campus for students to follow the course from there.
2 hrs
Thu16:15-18:00RZ F 21 »
M. Sachan, T. Sinha
263-5005-00 UArtificial Intelligence in Education
Online exercises: Will primarily take place online. Reserved rooms will remain blocked on campus for students to follow the exercises from there.
1 hrs
Thu18:15-19:00RZ F 21 »
M. Sachan, T. Sinha
263-5005-00 AArtificial Intelligence in Education1 hrsM. Sachan, T. Sinha

Catalogue data

AbstractArtificial Intelligence (AI) methods have shown to have a profound impact in educational technologies, where the great variety of tasks and data types enable us to get benefit of AI techniques in many different ways. We will review relevant methods and applications of AI in various educational technologies, and work on problem sets and projects to solve problems in education with the help of AI.
ObjectiveThe course will be centered around exploring methodological and system-focused perspectives on designing AI systems for education and analyzing educational data using AI methods. Students will be expected to a) engage in presentations and active in-class discussion, b) work on problem-sets exemplifying the use of educational data mining techniques, and c) undertake a final course project with feedback from instructors.
ContentThe course will start with a general introduction to AI, where we will cover supervised and unsupervised learning techniques (e.g.,classification and regression models, feature selection and preprocessing of data, clustering, dimensionality reduction and text mining techniques) with a focus on application of these techniques in educational data mining. After the introduction of the basic methodologies, we will continue with the most relevant applications of AI in educational technologies (e.g., intelligent tutoring and student personalization, scaffolding open-ended discovery learning, socially-aware AI and learning at scale with AI systems). In the final part of the course, we will cover challenges associated with using AI in student facing settings.
Lecture notesLecture slides will be made available at the course Web site.
LiteratureNo textbook is required, but there will be regularly assigned readings from research literature, linked to the course website.
Prerequisites / NoticeThere are no prerequisites for this class. However, it will help if the student has taken an undergraduate or graduate level class in statistics, data science or machine learning. This class is appropriate for advanced undergraduates and master students in Computer Science as well as PhD students in other departments.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersM. Sachan, T. Sinha
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationThe final assessment will be a combination of classroom participation, paper presentations, problem sets and the project.

Learning materials

Main linkInformation
Only public learning materials are listed.


No information on groups available.


Places60 at the most
Waiting listuntil 03.10.2021

Offered in

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