AutoTutor for Adult Reading Comprehension is participating in the ED Games Expo of 2021.
AutoTutor was initially developed by the Institute of Intelligent Services at the University of Memphis (IIS/UM) via grant support from CSAL at Georgia State University (GSU) (csal.gsu.edu) and is now supported by a 2020 IES grant award, led by Drs. John Sabatini, Arthur Graesser, (IIS/UM) Daphne Greenberg (GSU), and Tenaha O’Reilly (ETS). This project is undergoing active research.
This is a free, web-based tool that tutors adults to learn reading comprehension strategies. It uses two intelligent avatars to guide the learner through questions and to simulate learning through conversation. AutoTutor-ARC is compatible with most PC and laptop browsers with a stable internet connection. It includes thirty lessons covering (1) words and sentences, (2) computers and the internet, and (3) stories and texts. In a previous research phase, AutoTutor-ARC was piloted in tandem with classroom instruction; this intervention was successful in improving literacy skills in adults between 3rd and 8th grade reading level equivalency (Fang et al, 2018).
Especially given the constraints of COVID-19, AT-ARC’s adaptability as a tool for both in-person and remote learning is invaluable. In this phase of the research project, AutoTutor-ARC will be evaluated as a stand-alone instructional program (in or outside the classroom). Additionally, the research team has developed modules to help adults acquire independent digital skills, in order to support their use of AT-ARC outside the classroom. With this in mind, AT-ARC’s design aims for simplicity for both teachers and learners.
Please feel free to email email@example.com if you have any questions!
Fang, Y., Shubeck, K.T., Lippert, A., Cheng,Q., Shi, G., Feng, S., Gatewood, J., Chen, S., Cai, Z., Pavlik, P. I., Frijters, J.C., Greenberg, D., Graesser, A. C. (2018). Clustering the Learning Patterns of Adults with Low Literacy Interacting with an Intelligent Tutoring System. In K.E. Boyer & M. Yudelson ( Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp.348-354). Buffalo, NY: Educational Data Mining Society.