E-learning environment as intelligent tutoring system

Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student's learning behavior and presents to the student the learning material (content, questions‐answers, assignments) accordingly. In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents.Student modeling is a key component in any Intelligent Tutoring System (ITS). In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar group. In the present paper we present a methodology where using Case Based Reasoning (CBR), the ITS provides student modeling for online learning in a distributed environment with the help of agents.The Chapter describes the approach, the architecture, and the agent characteristics for the student modeling in the ITS. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students' learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and students' model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.

098 Developing an Intelligent tutoring system for prostate cryosurgery

Due to the rapid growth of the use of computers and increasing use of the Internet in education a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The Web-based intelligent learning has become more effective in the past decade due to increasing use of the Internet in education. A literature search indicates that there is a lack of relevant comprehensive research concerning the efficiency of computer-assisted instructions used in engineering education. The main focus of the research described in this chapter is on the comprehensive review of design and development of the Web-based authoring tool for an Intelligent Tutoring System in engineering education. The chapter outlines and discusses important issues of the development of Intelligent Tutoring System (ITS) in engineering education with an example of the development of a Web-Based Computer-Assisted Tutorials and Laboratory Procedures (WCALP).KeywordsEducational SoftwareExpert ModelInstructional SoftwareEducational Technology PublicationNatural Language DialogueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The rapid advancement of artificial intelligence (AI) technologies has significantly reshaped educational systems and learning environments worldwide. In particular, the integration of AI into language education has created new opportunities for improving learning efficiency, enhancing personalized instruction, and facilitating interactive communication environments. In the context of globalization and international talent development, the ability to communicate effectively in English has become an essential competence for participating in global economic, cultural, and technological exchanges. However, traditional language education models often rely on teacher-centered instruction and standardized assessments, which may not fully address the diverse learning needs of students or provide sufficient opportunities for authentic communication practice. This study explores the role of artificial intelligence in empowering the development of English competence for international talents. Drawing upon theories of technology-enhanced learning, intelligent tutoring systems, and global competence development, the paper proposes a conceptual framework that illustrates how AI technologies can support language learning through multiple application scenarios. These scenarios include intelligent language tutoring systems, AI-mediated communication environments, automated language assessment systems, and data-driven learning analytics. Based on these application scenarios, the study further proposes an AI-empowered educational model that integrates artificial intelligence technologies with innovative pedagogical approaches to support the cultivation of international talents. The model emphasizes personalized learning pathways, adaptive instruction, and global communication platforms supported by AI systems. The study contributes to theoretical discussions on AI-supported language education and provides practical insights for cultivating internationally competitive talents in the era of digital transformation.

Digital revolution has transformed the traditional teaching procedures, students are going online to access study materials. It is realised that analysis of student engagement in an e-learning environment would facilitate effective task accomplishment and learning. Well known social cues of engagement/disengagement can be inferred from facial expressions, body movements and gaze patterns. In this paper, student's response to various stimuli (educational videos) are recorded and cues are extracted to estimate variations in engagement level. We study the association of a subject's behavioral cues with his/her engagement level, as annotated by labelers. We have localized engaging/non-engaging parts in the stimuli videos using a deep multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs) video material. Recognizing the lack of any publicly available dataset in the domain of user engagement, a new ‘in the wild’ dataset is curated. The dataset: Engagement in the Wild contains 264 videos captured from 91 subjects, which is approximately 16.5 hours of recording. Detailed baseline results using different classifiers ranging from traditional machine learning to deep learning based approaches are evaluated on the database. Subject independent analysis is performed and the task of engagement prediction is modeled as a weakly supervised learning problem. The dataset is manually annotated by different labelers and the correlation studies between annotated and predicted labels of videos by different classifiers are reported. This dataset creation is an effort to facilitate research in various e-learning environments such as intelligent tutoring systems, MOOCs, and others.

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration.

This research assessed how the performance and team skills of three-person teams working with an Intelligent Team Tutoring System (ITTS) on a virtual military surveillance task were affected by feedback privacy, participant role, task experience, prior team experience, and teammate familiarity. Previous work in Intelligent Tutoring Systems (ITSs) has focused on outcomes for task skill training for individual learners. As research extends into intelligent tutoring for teams, both task skills and team skills are necessary for good team performance. This work includes a brief review of previous research on ITTSs, feedback, teams, and teamwork, including the recounting of two categories of a framework of teamwork performance, Communication and Cognition, which are relevant to the present study. This research examines the effects of an intelligent agent, as well as features of the team, its members, and the task being undertaken, on team communication (measured by relevant key-presses) and team situation awareness (as measured by scores on a quiz). Thirty-seven teams of three participants, each at their own computer running a multiplayer surveillance simulation, were given just-in-time private (individually delivered) or public (team-delivered) performance feedback during four 5-min trials. In the fourth trial, two of the three participants switched roles. Feedback type, teamwork experience, and teammate familiarity had no statistically significant effect on communication or team situation awareness. However, higher levels of role experience and task experience showed significant and medium-sized effects on communication performance. Results, based on performance data and structured interview responses, also revealed areas of improvement in future feedback design and a potential benchmark for feedback frequency in an action-oriented serious game-based ITTS. Among the conclusions are six design objectives for future ITTSs, establishing a foundation for future research on designing effective ITTSs that train interpersonal skills to nascent teams.

Recent research results indicate that cognitive processes are related to emotions. Since emotions are omnipresent in any kind of interaction it can be advantageous to take them into account, particularly in the context of e-learning. Emotions are now seen as closely related to cognition processes including decision-making, memory, attention, etc. Thus, e-learning environments have begun to take into consideration the emotional state of the learner in order to enhance his performance. This paper presents a tool for man-machine interaction which uses facial input. We believe that facial interaction will have its place among the interaction techniques in the near future. Our work concerns the improvement of computer based learning by mean of a lifelike presence in learning environment. Our approach combines Intelligent Tutoring System with research on human emotion in Cognitive Science, Psychology and Communication. According for relations between emotion, cognition and action in contextual learning, we propose an emotional intelligent tutoring system based on multi-agent architecture to design of adaptive distributed collaborative and peer to peer e-learning environments. An emotionally intelligent tutoring system should be able to provide feedback to students, taking into account relevant aspects of the mental state of the student.

This study examined Chinese undergraduate medical students’ acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e., perceived usefulness (PU), perceived ease of use (PEOU), behavioural intention and actual use of ITSs). The results of PLS-SEM showed that perceived enjoyment was the most influential antecedent as it significantly impacted both PU and PEOU. Both self-efficacy and prior experience only significantly contributed to PEOU but not PU. Both PU and PEOU significantly and positively predicted behavioural intention, which in turn had a significant and positive path to actual use. The results provide some practical implications to teachers as to how to encourage Chinese undergraduate medical students’ adoption of ITSs: by integrating some gamification elements into the learning activities in ITSs to foster students’ enjoyable feelings or familiarising students with using ITSs so that they can quickly adapt to learning through them. This could be achieved by providing guidance in using ITSs via videos, websites or booklets, or at the beginning of the course, inviting senior students to share their perceived advantages and usefulness of using ITSs. Implications for practice or policy: Because of the importance of perceived enjoyment, teachers may integrate some gamification into the learning activities in ITSs to nurture students’ enjoyable feelings. To enable students to quickly accommodate learning via ITSs, institutions may provide students with information on common features of ITSs or how to navigate a specific ITS. Teachers may explicitly explain how learning objectives can be better achieved through using an ITSs so that students will appreciate its usefulness.

Cyberlearning is challenged by the lack of personal and assessment-driven learning, and students are often puzzled by the lack of instructor guidance and feedback, the huge volume and diversity of the learning materials, and thelack of the ability to zoom in from the general concepts to the more specific ones, or the opposite. Intelligent tutoring systems are needed to improve the cyberlearning quality. One of the major difficulties is knowledge representation. The current industry standard is to use Web Ontology Language (OWL) forrepresenting knowledge structure. But OWL only supports one first-class relation, is-a, between the concepts, and different knowledge areas usually need different custom relations to describe the relations among the concepts. For example part-of and time dependency are important relations torepresent most engineering knowledge bodies. OWL has to use object properties to emulate such custom relations, leading to awkward knowledge representation hard for domain experts to code, validate and use such knowledge bases. This research uses Pace University's extension to OWL, named Knowledge Graph (KG), to support knowledge representation with custom relations. The instructors can use Pace University extended Protege IDE to declare and apply custom relations in a single document. The instructor teaching experience is also coded in the KG to better support custom learning order by students with different backgrounds. The prototype of a knowledge-driven tutoring system was designed and implemented to illustrate how the KG supports integrated assessments, using assessment results to custom student learning order or material, and let the students freely navigate in the knowledge space from general to specific or the opposite, and following various custom relations. A web technology tutorial is used to validate the design and effectiveness of this approach.

This article aims at exploring the identified benefits and challenges when integrating Educational Artificial Intelligence (AIEd) into contemporary Constructive-Learning-Based Curriculum design. It is argued that the Intelligent Tutoring System, which lies at the centre of AIEd, could help enhancing learners’ constructive learning experience by providing personalised and engaging learning environment, and it could promoting learners’ twenty-first century skills during the learning process. However, biased data in the Intelligent Tutoring System may also give rise to learning recommendation bias and inequality. Also, the algorithms in Intelligent Tutoring System is uncapable of capturing unquantifiable learning information, and put its emphasize mainly on the quantifiable educational recommendations, that will deteriorate the construction of the Constructivist-Learning-Based Curriculum.KeywordsCurriculumConstructivist LearningIntelligent Tutoring System

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

The concept of Intelligent Tutoring System (ITS) is used to describe any computer-based educational systems that provide immediate and personalized instruction and feedback to learners, usually without the intervention from a human teacher. This study examined the availability, accessibility, and utilization of ITS applications for teaching among selected secondary schools in Ilorin, Kwara state. The research was a descriptive type using the survey method. A random sampling technique was used to select 150 teachers from 5 secondary schools in Ilorin, Kwara state. Intelligent tutoring systems are not available to secondary school teachers in the Ilorin metropolis; the reaction of the respondents towards the use of ITS application software for instruction was Positive. From the findings of this study, it was concluded that Teachers do not have access to ITS application for instruction as a result of the availability of ITS instruction application. The implication is that if teachers have access to the ITS application for Instruction it will further foster education security. The study recommends that Government and School administrators should ensure adequate power supply and Internet facilities for easy access and utilization of ITS applications for Instruction in the classroom, also teachers who are not in ICT compliance should be encouraged to study further to meet up with the current technological trends.