Environmental monitoring for extraction of learner’s features and accurately learner modeling is a time and cost consuming task. This paper proposes a flexible framework using agent architecture capabilities in learners’ monitoring and also in optimization of INTELLIGENT TUTORING subsystems interactions. In this research learner’s learning approaches have been recognized by learning style theory then their uncertainty has been reduced by Bayesian Network. This framework prepares recommendations by combination of learners’ learning style and learner’s abilities (that are computed by Item Response Theory (IRT)) for three groups: learners, tutors and system designer. The architecture of proposed TUTORING system has been presented in three layers. In middle layer there are four agents that monitor learners, create learner’s model, update it and prepare some recommendations based on courseware features, learner’s abilities and his/her learning style. A study was conducted on 30 Computer Engineering students during one semester. The implementation of the proposed system on participants indicates an increase in all evaluation factors, for example it doubles educational success rate and learner’s satisfaction rate.

Background and Purpose: Academic achievement motivation and self-efficacy are two critical factors in students' academic success. Achievement motivation is the force that drives students to learn and achieve success, while self-efficacy strengthens their belief in their abilities. The present study aimed to investigate the effect of the INTELLIGENT Personalized TUTORING System (ITS) on academic achievement motivation and self-efficacy of seventh grade students. Methodology: This study utilized a quasi-experimental method with a pretest-posttest control group design. The statistical population consisted of seventh-grade students from a selected school. Sampling was conducted using a random method, and 60 students were divided into experimental and control groups. Data collection tools included the standardized Academic Motivation questionnaires (1980, 1981), the Academic Self-Efficacy Questionnaire by Jinks and Morgan (1999). Results: Data analysis was conducted using covariance analysis (ANCOVA). The findings revealed that the use of ITS had a significant impact on increasing students' achievement motivation and academic self-efficacy. Students who utilized this system demonstrated a notable improvement in motivational and self-efficacy aspects compared to the control group. These findings align with educational theories, particularly Bandura’s self-efficacy theory, indicating that personalized instruction accompanied by immediate feedback can enhance students’ sense of competence and confidence. Conclusion: According to the results of this study, the use of smart TUTORING system has a significant impact on students' motivation for academic achievement and academic self-efficacy. It is suggested that special attention be paid to providing constructive and personalized feedback, using gamification techniques, and creating interactive learning environments in the design and implementation of smart TUTORING systems.

An import factor in the success of human one-to-one TUTORING is the tutor's ability to identify and respond to affective cues. However, current INTELLIGENT TUTORING Systems model students solely on their possible for computers to identify affective cues by analyzing images of learners' facial expressions. A new generation of INTELLIGENT TUTORING Systems, Affective TUTORING System, is being developed at Massey University in New Zealand. An important component of this system is a facial expression analysis component that was implemented using a fuzzy approach. This fuzzy facial expression analysis approach and future directions are discussed in this paper.

Considering Teaching Persian by Safar Moghaddam (2012), the present research aimed to design an INTELLIGENT TUTORING system based on two natural language processing engines in order to teach Persian to English speakers. The system basically works by comparing logical forms derived from both languages and reports anomalies in the system. To do so, 60 applicants were randomly selected through pedagogical websites and divided into control and experimental groups, each of which included 30 applicants. Learners in control group were exposed to Persian grammar via teachers in an online class while learners in experimental group were exposed to English translations through software and they were asked to translate the sentences as well as verify their accuracy. In addition, learners in control group presented their translations to online tutors and received corrective feedback. Data analyses revealed a significant difference between learners in both groups, i.e., experimental group outperformed control group. In addition, learners‟ attitudes gathered through questionnaire and interview toward the system were reported positive. Finally, learners‟ interaction with the system and their common mistakes were reported.

In this paper, a new personalized system based on learners' motivation with utilization of game elements have been implemented for the e-learning environment. Learners' motivation is measured by AMS questionnaire and then by finding the player's type based on AMS results, appropriate game elements are selected for each learner and added to their webpage. To implement the designed system, the INTELLIGENT TUTORING system has been designed with 117 participants in a math course. To measure the performance of the designed system, academic performance and the time spent on learning management system (LMS) in the control and test groups, before and after personalization have been compared. The results show that personalization based on individual motivation by utilization of gamification leads to a significant improvement in learners’ academic performance compared to pre-personalization and compared to the control group.

An INTELLIGENT TUTORING System (ITS) can be effectively employed for a power plant simulator so that the need for instructor is minimized. In this paper using the above concept as well as object oriented programming and SIMULINK Toolbox of MATLAB, an INTELLIGENT TUTORING power plant simulator is proposed. Its successful application on a typical 11 MW power plant is demonstrated.