SyllabusQA: A Course Logistics Question Answering Dataset

Moreover, text similarity metrics may not be suitable In educational applications, artificial intelligence for some open-ended natural language generation (AI) approaches have shown significant promise in tasks (Amidei et al., 2018). As an example, the answer improving learning outcomes (Aleven et al., 2016; "The final exam will be on Dec 15", has high VanLehn, 2011), by automatically providing feedback surface-level textual similarity with the reference to students or engaging in tutoring dialogues answer, "The final exam is on Dec 14", but contains with them. The key idea is to use AI to create an ondemand a critical factual error that may lead to significant virtual teaching assistant to interact with negative consequences to students. Meanwhile, many students simultaneously; see, e.g., Khamigo human instructors and teaching assistants often answer from Khan Academy (Academy, 2022). These approaches student questions in a concise way, without can scale up the effort of expert human giving any unnecessary information. Therefore, it teachers and tutors, and relieve them from doing is important for LLM-based approaches to generate repetitive tasks so that they can focus on providing answers that are both concise and precise.