Important information regarding participation to the course:

1. Due to teaching methods, we have a limit of 30 students for this class (first come, first served). To register, please fill out the REGISTRATION FORM by March 31st, and provide the following details: UDE email address, student ID, and study program. If the maximum number of participants is reached, we will use a waiting list. The registration form is also accessible via the Moodle module (https://moodle.uni-due.de/course/view.php?id=49202)

2. Participation in the lab sessions is required for attending the course. You are enrolled in the ILE lab automatically once you register for the course.

COURSE DESCRIPTION

Teaching Form: Blended learning: Face-to-face lectures and practical sessions with online learning modules, and group work.

Working Language: English

Description: Computers and 'machine-intelligence' are frequently discussed as the means for addressing today's critical educational challenges: learning remotely, learning at one's own pace, learning according to one's needs and background, providing quality education to all and for all. In this course, we welcome all master-level students with technical or non-technical backgrounds. Through the semester, we will cover topics on the intersection of Artificial Intelligence in Education, Educational Technologies, and Human-Computer Interaction and we will carry out hands-on exercises to deepen our understanding of intelligent learning technologies. Specifically, we will go over the following:

• Introduction to educational technologies
• Artificial intelligence in education (AIED)
• Fairness, Accountability, Transparency, and Ethics in AIED
• Student Modeling
• Intelligent Tutoring Systems (ITS)
• Collaborative learning environments / MOOCs
• Learning Management Systems / Learning Dashboards 

Learning Objectives: Students will learn about the state-of-the-art research in Educational Technologies focusing on Artificial Intelligence in Education. They will familiarize themselves with algorithmic techniques for modeling cognition and knowledge, and they will explore how these representations are used in practice. Students will explore various learning environments supported by "intelligent" algorithms and will learn about using technology as a tool and means for orchestrating learning.

Literature:

- Course's Textbook [edition 2024]

- How People Learn: Brain, Mind, Experience, and School: Expanded Edition (2000), National Research Council. (download /read online here: https://nap.nationalacademies.org/catalog/9853/how-people-learn-brain-mind-experience-and-school-expanded-edition)

- Handbook of design in educational technology, edited by Rosemary Luckin, Sadhana Puntambekar, Peter Goodyear, Barbara Grabowski, Joshua Underwood, and Niall Winters.

- selected publications (research/news articles)

Moodle module: https://moodle.uni-due.de/course/view.php?id=49202

Pre-qualifications: Good knowledge of English