  The purpose of this study is to introduce software technologies and models
and artificial intelligence algorithms to improve the weaknesses of CBT
(Cognitive Behavior Therapy) method in psychotherapy. The presentation method
for this purpose is the implementation of psychometric experiments in which the
hidden human variables are inferred from the answers of tests. In this report,
we describe the various models of Item Response Theory and measure the hidden
components of ability and complementary parameters of the reality of the
individual's situation. Psychometrics, selecting the appropriate model and
estimating its parameters have been introduced and implemented using R language
developed libraries. Due to the high flexibility of the Multi variant Rasch
mixture Model, machine learning has been applied to this method of data
modeling. BIC and CML were used to determine the number of hidden classes of
the model and its parameters respectively, to obtain Measurement Invariance.
The sensitivity of items to hidden attributes varies between groups (DIF), so
methods for detecting it are introduced. This simulation is done based on the
Verbal Aggression Dataset. We also analyze and compile a reference model based
on this certificate based on the discovered patterns of software engineering.
Other achievements of this study are related to providing a solution to explain
the reengineering problems of the mind, by preparing an identity card for the
clients by an ontology. Finally, applying the developed knowledge in the form
of system thinking and recommended patterns in software engineering during the
treatment process is pointed out.
