Abstract- With the current speedy development of computer technology, scholars have attempted to accept artificialintelligence on education. Computer networks have been developed by both scientists and educationist using computer- aided instruction systems to develop programs to test and improve the educational performance of students. A long-sought goal in education is being sought by instruction and integrating sound assessment. Current advances in educational measurement and artificialintelligence have positioned this aim within reach of people. Leveraging assessment information could improve intelligent education systems information that is collected from different sources (e.g., formative and summative information), hence, reporting useful approach in accomplishing the expected goals in education. Therefore, this article provided evidence on the role of different artificialintelligence in the educational sector, summarises the uniqueness of successfully, used intelligent educational systems, and provides an evidence-based approach. This paper is divided into four sections. The first section is the introduction of the study. Next is followed by the literature review of AIEDs. Next section three, the contributions of AIED for the development of education and lastly, section four the conclusion of the study.
Dr. Shubhash Clinician is an online virtual patient system that uses artificialintelligence technology specifically for teaching hospitals, medical colleges, and residents. The system is widely used in education and clinical thinking evaluation of medical students. The software collects hundreds of real patient data and is compiled by experts and artificialintelligence as specific cases. These cases cover a wide range of clinical issues. Medical students make diagnoses through inquiry, simulated physical examinations, and supplementary examinations of virtual patients to diagnose and provide treatment plans. For teachers, Dr. Shubhash Clinician can be used as a useful analysis tool to help teachers understand students’ behaviour and adjust courses through appraisal results. For students, they can quickly develop clinical problem solving skills. By interacting with the cases, students can learn a lot about important disease diagnosis. At the same time, the system can identify mistakes that students make in the process of case analysis, conducts deep learning and analysis, and help students solve these problems.
In the past decade, the application of artificialintelligence has solved or partially solved many challenges in the education field, including language processing, reasoning, planning and cognitive modelling. Artificialintelligence provides students with more opportunities to participate in a digital and dynamic way. These opportunities are often not found in out-dated textbooks or the fixed environment of the classroom. Through this Collaborative learning method, each student has the potential to advance others, and can accelerate the exploration of new learning and the creation of innovative technologies. Four applications are provided below to illustrate how artificialintelligence can be applied to medical education.
The field of ArtificialIntelligence in education has undergone significant developments over last years. We analyze papers of journal of AI in education to identify foci and typical scenarios that occupy the field of AI in education. ArtificialIntelligence (AI) already plays a major role in our daily life. Sound knowledge about AI and the principles of computer science will be of vast importance for future careers in science and engineering. Looking towards the near future, jobs will largely be related to AI. In this context literacy in AI and computer science will become as important as classic literacy (reading/writing). By using an analogy with this process we developed a novel AI education concept aiming at fostering AI literacy. The concept comprises modules for different age groups on different educational levels. Fundamental AI/computer science topics addressed in each module are, amongst others, problem solving by search, sorting, graphs and data structures. We developed, conducted and evaluated four proof-of-concepts modules focusing on kindergarten/primary school as well as middle school, high school and university. Preliminary results of the pilot implementations indicate that the proposed AI education concept aiming at fostering AI literacy works.
• Courses often omit important topics and key theoretical ideas that have contributed much historically to computa- tional accounts of intelligence. Problem areas like qual- itative reasoning, analogy, and creativity are ignored in favor of ones that are more easily formalized. Even foun- dational AI concepts, such as list processing, satisficing, and expert systems are in danger of being forgotten. Table 1 summarizes how each course fares along these di- mensions, based on inspection of their on-line schedules and exercises, with ◦, ◦·, and • denoting poor, medium, and good scores, respectively. The situation supports the concerns ex- pressed earlier that introductory AI courses downplay inte- gration, representation, cumulative presentation, program- ming, and breadth. One especially narrow course focused primarily on statistical learning, almost to the exclusion of other topics. Naturally, our analysis is subjective and based on limited information, but we predict others would draw similar conclusions from the content available. At the same time, we expect many AI educators would disagree that low scores on these criteria are undesirable. They are likely to believe that presenting the field as a collection of algorithms, using available software, and ignoring ‘outmoded’ topics are evidence of its maturity, not a cause for dismay.
Abstract: Artificialintelligence is the science of automating intelligent behaviour presently which is achievable by human beings. In future, intelligent machines will replace or enhance the human capabilities in many areas. It is the intelligence exhibited by machines or software. ArtificialIntelligence has been becoming a popular topic in the field of computer science as it has changed/ enhanced the human life in many areas. Artificialintelligence in the last two decades has greatly improved performance of the manufacturing and service sectors. Research in the area of artificialintelligence has given rise to the rapidly growing technology known as expert system. ArtificialIntelligence is having a huge impact on various fields of life as expert system is widely used these days to solve the complex problems in various areas as science, engineering, business, medicine, education, traffic system, weather forecasting, etc. The areas employing the technology of ArtificialIntelligence have seen an increase in the quality and efficiency. ArtificialIntelligence areas are Expert Systems, Speech recognition, Robotics and Sensory Systems, Computer Vision and Scene Recognition, Intelligent Computer Aided Instruction, Neural Computing, Natural Language Processing. This paper gives an overview of the technology and its application areas like: PSS design to damp the power system oscillations caused by interruptions, Network Intrusion for protecting computer and communication networks from intruders, medical area, to improve hospital inpatient care, for medical image classification, accounting databases to mitigate the problems of it and in the computer games.
Neurobiological modeling has the goal to develop models of artificial neuronal network. In this context, the knowledge of the exact cellular properties of the nervous system is essential. The last few decades have produced a vast amount of knowledge about the function of the nervous system, mostly concerning the function of neurons. Much more recently, the possible importance of astroglial cells in the biological neuronal network has emerged. One of the most novel and exciting areas of neuroscience has emerged after the demonstration of the existence of intercellular astrocyte communication, which may represent a novel extraneuronal communication system, possibly with information processing capacity. Furthermore, the existence of reciprocal communication between astrocytes and neurons adds further complexity to the communication pathways in the nervous system. Therefore, future developments concerning artificial neuronal networks might be improved by including the possibility that an artificial glial network would provide a parallel super-regulatory system. Therefore, three different components of the overall nervous system must be considered: the astrocyte network, the network of Figure 5. Schematic drawing illustrating the new concept of the synaptic physiology — the tripartite synapse — where astrocytes play an active role by exchanging information with the synaptic elements. Two pairs of neurons with pre- and post- synaptic contact are shown, as well as an astrocyte in close proximity to the synapse. During synaptic activity, neurotransmitters released form presynaptic terminals elicit postsynaptic potentials. The neurotransmitters eventually reach the astrocytic membrane, activating receptors that increase astrocytic Ca 2+ levels through the
The very question that asks whether computers can be intelligent, or whether machines can think, came to us from the ‘dark ages’ of artificialintelligence (from the late 1940s). The goal of artificialintelligence (AI) as a science is to make machines do things that would require intelligence if done by humans (Boden, 1977). Therefore, the answer to the question ‘Can machines think?’ was vitally important to the discipline. However, the answer is not a simple ‘Yes’ or ‘No’, but rather a vague or fuzzy one. Your everyday experience and common sense would have told you that. Some people are smarter in some ways than others. Sometimes we make very intelligent decisions but sometimes we also make very silly mistakes. Some of us deal with complex mathematical and engineering problems but are moronic in philosophy and history. Some people are good at making money, while others are better at spending it. As humans, we all have the ability to learn and understand, to solve problems and to make decisions; however, our abilities are not equal and lie in different areas. There- fore, we should expect that if machines can think, some of them might be smarter than others in some ways.
(b) The book may benefit readers from different disciplines and can be used at different levels (to be explained in "How to use the book"). Note that this treatment is not intended to blur the boundaries of different computer science disciplines; our purpose is just to encourage an integrated way of thinking which is a critical element of decision making. There are materials taken from existing books because they are nice, but there are more materials not found in any individual book. We cover some selected, matured computational intelligence techniques useful for decision support; we should not only correctly apply these techniques, but also analyze the indications (such as similarities or differences) behind these techniques, and identify invariants shared by various techniques. With emphasis on applications, we have made a compromise between theoretical rigorous and practical concerns. We cover some most recent developments in data mining and data warehousing, yet we still stick with the most important principles of intelligent decision making. In addition, we are interested in using folk psychology to implement systems to assist human intelligence. (that is, we will include materials from "sidetrack AI").
example, no automate can “comprehend” so far that the sentence Niños son niños is not a trivial tautology, but the idea that chil- dren have specific features of their own and thus should be treated properly. To cope with such intricacies, linguists should model the human world with respect to its customs, habits, eti- quette, relations between generations, etc. This is an extralinguis- tic knowledge of encyclopedic type. Until now, computational linguistics and artificialintelligence do not know how to effec- tively distinguish, to assemble apart and then to combine the knowledge of purely linguistic and evidently encyclopedic type. What is more, a “dictionary,” which specifies all encyclopedic information needed for comprehension of texts rather simple for a human to understand, would be so huge that it is unlikely to be compiled with sufficient completeness in the nearest future. • The results of the recent investigations mainly cover separate
This series reflects the latest advances and applications in machine learning and pattern recognition through the publication of a broad range of reference works, textbooks, and handbooks. The inclusion of concrete examples, appli- cations, and methods is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of machine learning, pattern recogni- tion, computational intelligence, robotics, computational/statistical learning theory, natural language processing, computer vision, game AI, game theory, neural networks, computational neuroscience, and other relevant topics, such as machine learning applied to bioinformatics or cognitive science, which might be proposed by potential contributors.