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Enrollments in distance-learning scenarios have been tremendously rising. Here, the ability of students to receive answers to questions is hindered due to an uneven educator-student ratio. Students often do not receive quick answers to simple questions, and educators feel stressed by answering the same questions repeatedly. However, advances in Natural-Language-Processing and Machine Learning bear the opportunity to design new forms of human-computer interaction by embedding question-answering (Q&A) models in conversational agents. Such a system enables students to receive personalized answers independent of an instructor, time, and location. This paper presents the first steps of our design science research project on designing a student-centered Q&A system that helps learners receive personalized answers in large-scale settings. Based on social response theory and user interviews, we propose five design principles for the design of a conversational Q&A system. Furthermore, we instantiate those principles as design features in a natively built prototype.



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