The present research incorporated the content of basic statistics into the Artificial Intelligence Physics Tutor (ARPHY), which was used as the expert system shell, and investigated the effects of the Artificial Intelligent Statistics Tutor (ARSTAT) as a supplement to learning statistics at the college level. Two classes of an introductory educational statistics course in the Department of Educational Foundations, University of North Texas, were used in the study. The daytime class was used as the experimental group and the evening class was used as the control group. The experimental group's lecture/discussion was supplemented with ARSTAT, and the control group received only lecture/discussion. A one-way analysis of covariance was used to compare students' test scores. No significant difference was found; however, the adjusted mean score of the experimental group was slightly higher than that of the control group. A two-way analysis of covariance showed no significant main effect or interaction between gender and study technique. A second two-way analysis of covariance showed no significant interaction between the students' attitude toward statistics and the study technique used. However, the students with a statistics-positive attitude scored significantly higher on the test than students who had a negative attitude toward statistics. This study concluded that the ARSTAT can be used effectively as a tutor for students taking an introductory course in educational statistics. The following recommendations for further study were made: incorporate more advanced topics of statistics into the ARPHY teaching model; incorporate the ARPHY learning theory and statistical content using another version of LISP language or another programming language such as PROLOG; and compare the ARSTAT tutor to some other kind of supplement to lecture/discussion.