🎉 NEWS 🎉

(April 2024) AlgeSPACE project on SIC news and university news

(Mar 2024) Our lab will offer two new seminars in SS2024 – check them out here.

(Mar 2024) We will be at the Tag der digitalen Bildung (The Digital Education Day) in Saarland!

(Feb 2024) We have launched AlgeSPACE — a collection of free interactive exercises for teaching and learning how to solve systems of equations.

(Oct 2023) New grant (with Sarah Malone) on co-designing a learning dashboard on AI in Education with university students at Saarland.

(Sep 2023) Won the PRESTO (“Sakigake”) grant! (€300K) We will work on participatory design research with school students and teachers in Germany and Japan to design an intelligent system with a focus on student agency.

(Aug 2023) Echo has joined the lab as a postdoc from ASU! Learn more about her.

(Apr 2023) Starting a new role as a Visiting Professor at Hokkaido University in Japan!

(Mar 2023) Gave a talk at CAIS in Bochum

(Feb 2023) Gave a talk at ETH ZĂĽrich (FLI Colloquia)

(Dec 2022) Launched the “Future+Learning” working group with Lis Sylvan and Sandra Cortesi @BKC Harvard

(Nov 2022) Moved to Germany to start my TT position at Saarland University!

(June 2021) Our ISLS paper was nominated for Best Design Paper!

(April 2021) We were at the ED Games Expo! Here’s our entry.

(Dec 2020) Won the Fred Mulder Open Education Practice Award!

(Nov 2020) Gave a talk at Keio Univ.

(Nov 2020) Won an AECT award!

(Nov 2020) Presented at OpenEd20

(Oct 2020) Made a tape diagram template (available under CC-BY-NC)

Seminar: Intelligent Systems for Supporting Human Learning (WS 2023-24)

Description: Intelligent systems (e.g., Intelligent Tutoring Systems) have been developed and empirically evaluated to support human learning in a variety of domains (e.g., STEM). Recent advancement in AI technologies has allowed such systems to become even more effective through, for instance, adaptive tutoring (e.g., adaptive problem selection, and affect detection). In this block seminar, we will review key papers on intelligent systems that support human learning and discuss future opportunities for further enhancing such systems. We will also pretend to be secondary-school learners and use some of the established intelligent systems to get hands-on experiences. The seminar is open to both master’s and bachelor’s students. The seminar doesn’t require any prerequisite knowledge and is therefore open to students with any background, but knowledge or experience in one or more of these fields will help: cognitive science, learning sciences, and Human-Computer Interaction. This seminar is open to students in all departments/faculties across UdS campus. Active participation in class discussions and activities is expected and encouraged. I rather want to design learning experiences in this seminar with students – your active participation is the key to the success of the seminar! SIC Seminar page

Learning objectives: By completing this seminar, you will be able to discuss the benefits and limitations of various intelligent systems designed for supporting human learning. You will also be able to generate new research and development ideas for the future of intelligent learning systems.

Lecturer:  Tomohiro Nagashima (nagashima[at], Man “Echo” Su (mansu[at]

The number of students:  12 maximum

Location:  TBD (in-person attendance required; however, if remote participation is desired due to special reasons, please consult in advance)

Time: block seminar in March; we will meet on March 1, 4, 6, 8, 11, 13, and 15th. All 11:00-13:00 except for 13th (14:00-16:00).

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 (by October 24, 2023). If you are in other departments (e.g., EduTech), please email Tomo and Echo a brief motivational statement on why you are interested in this seminar by October 22, 2023


  • 20% daily reading posts (summary and your thoughts on papers we read)
  • 30% discussion lead (present 10min summary of the paper, once for every student)
  • 40% final report (2000-3000 words on the future of intelligent learning systems)
  • 10% contributions to discussion during the class

Tentative List of Topics:

  • Day 1: Introduction, History of intelligent learning systems 
  • Day 2: Cognitive principles in designing intelligent systems for supporting learning
  • Day 3: Efficacy studies of intelligent tutoring systems
  • Day 4: Hands-on exercise or learning analytics/data mining with intelligent systems
  • Day 5: Supporting affect, motivation, and engagement with intelligent systems
  • Day 6: Self-regulated learning with intelligent systems (guest lecture: Daryn Dever from the University of Central Florida)
  • Day 7:  Participatory design for learning technology