Development of an intelligent tutoring system for design education: personalised learning support in engineering

Dutch research on knowledge-based instructional systems: Introduction to the special issue

To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control (CNC) programming. The knowledge acquisition, knowledge representation and reasoning used in CNC programming were researched. The CNC programming system functional architecture of impeller parts based on knowledge based engineering (KBE) was constructed. The structural model of the general knowledge-based system (KBS) was also constructed. The KBS of CNC programming system was established through synthesizing database technology and knowledge base theory. And in the context of corporate needs, based on the knowledge-driven manufacturing platform (i.e. UG CAD/CAM), VC++6.0 and UG/Open, the KBS and UG CAD/CAM were integrated seamlessly and the intelligent CNC programming KBE system for the impeller parts was developed by integrating KBE and UG CAD/CAM system. A method to establish standard process templates was proposed, so as to develop the intelligent CNC programming system in which CNC machining process and process parameters were standardized by using this KBE system. For the impeller parts processing, the method applied in the development of the prototype system is proven to be viable, feasible and practical.

Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems

This chapter aims to indicate some of the potential benefits and dangers inherent in the technology of Knowledge-Based Systems (KBSs) and to suggest courses of action that would both minimize risk and promote social benefit in future developments. KBSs offer cheap and ready access to knowledge, efficient management and organization of information, conversion of information into usable knowledge to guide human action, knowledge and advice relevant to the context in which it is needed. There are, however, potential dangers if a user is unfamiliar with the assumptions underlying KBS structures of reasoning and representing knowledge: the danger of a program becoming incomprehensible or unreliable in an emergency: the possibility of placing too much faith in the abilities of KBSs and thereby losing the capacity for critical evaluation of its judgments: the inevitable uncertainty or inexactitude programmed into KBSs: the economic implications at an uneven distribution of new technology: and the likelihood of misinterpretation or technical failure when autonomous. systems are automatically activating machinery (e. g. military/defence systems). The chapter argues for the regulation of KBS construction, particularly for those systems designed for public or “non-expert” use. Suggestions include programming into each system a clear qualification of answers and requests for data, setting up a workable Code of Conduct for AI practitioners, and constant supervision of the assumptions and activities of each computerized system. Since KBSs work by consulting an internal model of the world (supplied to them by a human programmer), their decisions must be challenged or ignored when necessary and always treated with an appropriate degree of scepticism

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.

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.

Multi-Agent Based Intelligent Tutoring System Source Code Generation Using MASITS ToolSeveral agent development tools have been proposed. At the same time, specific tools for agent based Intelligent Tutoring System (ITS) development do not exist. However, ITSs have some specific characteristics that must be taken into consideration during the development. General purpose Agent Oriented Software Engineering (AOSE) methodologies and therefore development tools do not sufficiently correspond to the characteristics of ITSs. Additionally, the general purpose AOSE methodologies and tools do not allow plugging in domain specific rules and diagrams. Thus, usage of knowledge gained in ITS research during the development process is limited. In the absence of general tools that allow plugging in knowledge from ITS research, a specific tool named MASITS has been developed. The tool supports a specific agent based ITS development methodology named MASITS which takes into consideration specific characteristics of ITSs and integrates knowledge from ITS research. The tool supports all phases of ITS development, starting from requirements analysis and ending with deployment. Requirements analysis and design phases are supported by appropriate diagram creation tools. Implementation is supported by source code generation from diagrams created during the design phase. JADE platform is used for agent implementation. Thus, Java classes for ontology, agents and behaviours are generated from the design diagrams. The paper includes a brief overview of diagrams used in the source code generation and detailed algorithms for source code generation from the diagrams.

Intelligent information systems, such as those built using knowledge-based system (KBS) technology, stress the human-computer interface in new ways. Such systems use knowledge as well as lower-level information. The application of KBS technology depends on two aspects of the communication of knowledge (knowledge transfer). First, it depends on the ability of humans to explicate their knowledge in ways that can be incorporated into the computer system. Second, it depends on the ability of the computer to divulge its knowledge to humans in a palatable and understandable form.

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.

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.

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.

The development of any Intelligent Tutoring System (ITS) requires the acquisition of diverse and extensive design information. This chapter describes what specific design information is needed to build an ITS. It outlines several issues with regard to using empirical studies to gather design information of which the ITS designer should be aware. A traditional ITS is divided into four distinct modules: expert model, student model, instructional model (which incorporates diagnostic and pedagogical processes), and the instructional environment, or user interface. The chapter briefly describes each of these components, including the types of information ITS designers should try to acquire for the specific modules. It presents two case studies of field research conducted for different ITSs, one for teaching spreadsheet programming, the other for teaching object-oriented modeling. Finally, the chapter presents some suggestions for designing and conducting quasi-experimental field research for the purpose of designing an ITS.