DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring

This paper presents DIRECT, a dialogue-based intelligent tutoring system for reading comprehension that asks questions, assesses student answers, provides hints, and engages in encouraging chat. The system is evaluated on a dataset constructed from RACE-M, which contains reading comprehension exercises from English exams for middle school students.

A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and k

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