@article{shahriar_matsuda_2024, title={"I Am Confused! How to Differentiate Between.?" Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task}, volume={14830}, ISBN={["978-3-031-64298-2"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-64299-9_2}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II, AIED 2024}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2024}, pages={17–30} }
 @article{shahriar_matsuda_2023, title={What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning}, volume={13916}, ISBN={["978-3-031-36271-2"]}, ISSN={["1611-3349"]}, url={https://doi.org/10.1007/978-3-031-36272-9_11}, DOI={10.1007/978-3-031-36272-9_11}, abstractNote={Students learn by teaching a teachable agent, a phenomenon called tutor learning. Literature suggests that tutor learning happens when students (who tutor the teachable agent) actively reflect on their knowledge when responding to the teachable agent’s inquiries (aka knowledge-building). However, most students often lean towards delivering what they already know instead of reflecting on their knowledge (aka knowledge-telling). The knowledge-telling behavior weakens the effect of tutor learning. We hypothesize that the teachable agent can help students commit to knowledge-building by being inquisitive and asking follow-up inquiries when students engage in knowledge-telling. Despite the known benefits of knowledge-building, no prior work has operationalized the identification of knowledge-building and knowledge-telling features from students’ responses to teachable agent’s inquiries and governed them toward knowledge-building. We propose a Constructive Tutee Inquiry that aims to provide follow-up inquiries to guide students toward knowledge-building when they commit to a knowledge-telling response. Results from an evaluation study show that students who were treated by Constructive Tutee Inquiry not only outperformed those who were not treated but also learned to engage in knowledge-building without the aid of follow-up inquiries over time.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2023}, pages={126–138} }
 @article{shahriar_matsuda_2021, title={"Can You Clarify What You Said?": Studying the Impact of Tutee Agents' Follow-Up Questions on Tutors' Learning}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, url={https://doi.org/10.1007/978-3-030-78292-4_32}, DOI={10.1007/978-3-030-78292-4_32}, abstractNote={Students learn by teaching others as tutors. Advancement in the theory of learning by teaching has given rise to many pedagogical agents. In this paper, we exploit a known cognitive theory that states if a tutee asks deep questions in a peer tutoring environment, a tutor benefits from it. Little is known about a computational model of such deep questions. This paper aims to formalize the deep tutee questions and proposes a generalized model of inquiry-based dialogue, called the constructive tutee inquiry, to ask follow-up questions to have tutors reflect their current knowledge (aka knowledge-building activity). We conducted a Wizard of Oz study to evaluate the proposed constructive tutee inquiry. The results showed that the constructive tutee inquiry was particularly effective for the low prior knowledge students to learn conceptual knowledge.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Shahriar, Tasmia and Matsuda, Noboru}, year={2021}, pages={395–407} }