Description:

The rise of artificial intelligence (AI) is transforming everyday lives, including education, and it requires us a deep understanding of and research insights into its applications and implications in the field. This seminar aims to equip students with the knowledge and skills needed to critically analyze AI in education and contribute to this evolving field. The seminar is jointly taught by Prof. Tomohiro Nagashima in the CS department and Dr. Sarah Malone in the Education Science department, and it targets students in both departments, as well as those from other departments!

During the seminar, students will collaboratively design and develop a “Living AI-education Dashboard,” a dynamic resource that summarizes and visualizes current research, trends, and data on AI in education. Through project-based learning, students will gain hands-on experience in data visualization, dashboard development, dashboard design, and research methods (e.g., how to conduct systematic literature review). Students would also be testing the dashboard with “real” stakeholders. They will also develop interdisciplinary thinking by integrating concepts from both computer science and education science and through collaborations across the domains. The course is taught by an interdisciplinary team that encourages collaboration between departments and prepares students to tackle complex, real-world problems

Learning objectives:

By the end of the course, students will be able to:

• Understand key concepts and applications of AI in education: Students will gain a thorough understanding of the fundamental concepts and practical applications of artificial intelligence in educational settings.
• Develop research questions and conduct independent research on AI in Education: Students learn to formulate precise and relevant research questions related to AI in education. They develop the ability to conduct systematic literature searches using academic databases and other sources, to extract essential information from the retrieved records, and to synthesize the results into coherent overviews.
• Analyze and visualize data to communicate scientific results: Students will be able to collect, manage, and analyze data from original research relevant to AI in education. They will apply advanced data visualization techniques to effectively present research findings.
• Conceptualization and collaboration on dashboard design: Students will integrate their individual research findings into a unified, interactive dashboard. They will work collaboratively to ensure the dashboard effectively communicates aggregated insights and serves as a dynamic resource. They will also test the dashboard with relevant stakeholders.
• Contribute to a living resource (dashboard): Students of successive cohorts will actively contribute to the development and continuous updating of the Living AI-ducation Dashboard. This will ensure that the dashboard remains a current and valuable resource for AI in education, reflecting the latest research and data.

Lecturer:

• Sarah Malone, malone@mx.uni-saarland.de
• Tomohiro Nagashima, nagashima@cs.uni-saarland.de
• Dominik Thüs, thues@uni-saarland.de
• Meiyi Chen, mchen@cs.uni-saarland.de 

Max number of students:  20 maximum

Location:  TBD 

Time: Mondays 10-12:00

Credit points: 7CP

Application/Enrollment: If you are in the CS department, please use the seminar portal to send your application with a motivation statement. If you are in other departments (e.g., EduTech), please complete this form by 26.09.2025.

Tentative List of Topics:

• Introduction to AI in education
  • Overview of AI applications in education
  • Key concepts of AI in education
  • Ongoing research on AI in education
• Research methods
  • Developing research questions
  • Conducting systematic literature reviews
  • Data collection and analysis
  • Aggregating results to answer research questions
  • Basics of science communication
  • Evaluation methods 
• Data visualization techniques
  • Principles of effective data visualization
  • Best practices for data representation
  • Tools and software
• Dashboard design principles
  • Overview: dashboard design
  • Best practices for dashboard design
  • Tools and software
• Group Research Projects
  • Conducting individual group research projects
  • Collecting and analyzing data
• Sharing and integrating group research findings
  • Presenting individual findings to peers
  • Group discussions and feedback
  • Integrating findings into a unified dashboard design
• Collaborative dashboard design
  • Concept and prototype
  • Peer feedback and refinement
  • Final integration of all research findings
• Dashboard Presentation
  • Presentation of prototype
  • Peer, lecturer Feedback
  • Final revision and improvements
• Publishing living AI-ducation dashboard