The Innovative Educational Computing Lab is hiring two Graduate Research Assistants (GRA) for a newly funded NSF project on the SimStudent project and the PASTEL project.
The GRA is awarded only for PhD students. The GRA scholarship will be available from Fall 2026 for four full years with potential continuation contingency upon future funding opportunities. Interested candidate should send a current CV along with a research statement to Dr. Noboru Matsuda <Noboru.Matsuda@ncsu.edu>
Currently we focus on the following two projects. However, the actual project will be determined based on your interest and background. The GRA project will be aligned with your dissertation project.
Project Title: Investigation of fundamental AI technologies for adaptive online learning platform
Summary:
Project Title: Learning by teaching with constructive tutee inquiry for robust learning in algebra
Summary: This project aims to advance the knowledge in how students learn robust knowledge in algebra that, by definition, allows students to not only derive answers for stereotypical problems but also draw analytical reasoning for unseen problems. Algebra is a gateway for broad STEM pathways. Yet, many students fail to achieve proficiency in algebra, which is arguably a primary cause of inability to pursue advanced STEM disciplines and further hesitancy in taking STEM pathways. The investigators hypothesize that one of the challenges in learning algebra is due to the complication of the web of algebraic knowledge students need to learn. It is argued that the web of knowledge involves conceptual and procedural knowledge and their relations, which the investigators call the connected knowledge. The investigators then propose to develop a transformative technology in the form of teachable agent to amplify the effect of learning by teaching that they call a smart teachable agent. The smart teachable agent asks students questions to justify their reasoning while solving equations. When student’s response needs to be elaborated, the smart teachable agent further provides a follow up question to solicit a response that reflects a connection between procedural operations and conceptual justifications. The smart teachable agent may ask follow-up questions two to three times. The proposed question-based dialogue between the student and the smart teachable agent is called the constructive tutee inquiry.
To implement the constructive tutee inquiry, the investigators will develop an innovative application of large language models (LLM) where multiple LLM invocations will be combined, including one for generating an ideal response to the agent’s question and another one for generating a follow up question based on the gap between the student’s response and the ideal response. The proposed dialogue system will be embedded into an existing online learning environment, called APLUS where students learn to solve linear equations by teaching a teachable agent, called SimStudent. As a learning science contribution, the investigators will study a theory of how students learn connected knowledge and how acquisition of connected knowledge facilitate robust learning in algebra. Classroom evaluation studies using APLUS and SimStudent with the proposed constructive tutee inquiry will be conducted with middle school students in their algebra classrooms.