| 22. Artificial tutors | |
|---|---|
Offered by | M&CS and IE&IS |
Available in timeslot | D |
Target student major |
|
Preferred entrance knowledge / skills | We welcome anybody with interest, knowledge, or skills in one or more of Human Technology Interaction, Software engineering or User-interface design. Students who also follow the educational minor would be very valuable to the project teams. |
Student capacity | 60 |
Group size | 6 |
Contact person | Jim Portegies, j.w.portegies@tue.nl |
Project description
Artificial Tutors are digital tools that should help students with their learning. Recent advances in artificial intelligence, such as large language models, make these tools even more promising. The possibility to collect students’ interaction data from these systems, potentially combined with data from other learning platforms like Canvas, opens a doorway to thorough evaluation of the uses. The question remains, however, how to use all these possibilities for measuring and improving effective learning? How can we encourage students to use the tool? How to ensure that students learn, that the feedback that the tool provides is effective? To answer these questions, the design, development and evaluation of Artificial Tutors require a holistic, multidisciplinary approach, that draws from a variety of disciplines, including artificial intelligence, human-technology interaction, educational theory and domain knowledge from the particular field that is being taught.
An example of a challenge that we offer in this project will specify to Mathematics education, in the particular context of the Artificial Tutor Waterproof. A specific challenge in this context would be to design, implement and evaluate an addition to the Waterproof software, that gives students effective, personalized hints at the right moments.
