We offer EASEx (Embodied Agents to Scaffold Education, x = any data-rich domain, e.g., physics, medicine, psychology, astronomy) as a content-agnostic AIED infrastructure for scaffolding data-driven problem-solving in jupyter notebooks. With a few clicks, educational stakeholders can easily set up EASEx to provide automated, personalized and in-situ social support to students delivered via embodied pedagogical agents. EASEx draws on empirical Learning Sciences research on scaffolding students' learning and advances in embodied pedagogical agent design within educational technologies. We welcome requests for advice on integrating EASEx in your courses and educational interventions.
Documentation and tutorials can be found at https://EASEx.github.io.
If you use EASEx in your work and/or publications, we ask you to kindly cite the following work.
Sinha, T., Malhotra, S. 2022. Embodied agents to scaffold data science education. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Lecture Notes in Computer Science, vol 13356. Springer, Cham. doi: 10.1007/978-3-031-11647-6_26
Dr. Tanmay Sinha
Email: tanmay.sinha@gess.ethz.ch
Shivam Malhotra
Email: mshivam@iitk.ac.in