Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation difficulties, and childcare issues. AutoTutor for Adult Reading Comprehension (AT-ARC), as an online conversation-based intelligent tutoring system that incorporated a theoretical model of reading comprehension, was developed with great efforts to meet adult learners' needs and be adaptive to their knowledge, skills, self-efficacy, and motivation. In this paper, we introduced the adaptive features of AT-ARC from four aspects: learning material selection, adaptive branching, trialogues, and interface, as well as the rationale behind these designs. In the end, we suggested further research on improving the adaptivity of AT-ARC. [This paper was published in: "HCII 2021," Lecture Notes in Computer Science (LNCS) Vol. 12792, edited by R. A. Sottilare and J. Schwarz, Springer Nature Switzerland AG, 2021, pp. 592-603.]