In this paper two favorite artificial intelligence methods: ANN and SVM are proposed as a means to achieve accurate question level diagnosis, intelligent question classification and updates of the question model in intelligent learning environments such as E-Learning or distance education platforms. This paper reports the investigation of the effectiveness and performances of two favorite artificial intelligence methods: ANN and SVM within a web-based environment (E-Learning) in the testing part of an undergraduate course that is "History of Human Civilizations" to observe their question classification abilities depending on the item responses of students, item difficulties of questions and question levels that are determined by putting the item difficulties to Gaussian Normal Curve. 
The effective nesses of ANN and SVM methods were evaluated by comparing the performances and class correct nesses of the sample questions using the same 3 inputs as: item responses, item difficulties, question levels to 5018 rows of data that are the item responses of students in a test composed of 13 questions. The comparative test performance analysis conducted using the classification correctness revealed yielded better performances than the Artificial Neural Network (ANN) and Support Vector Machine (SVM).