scholarly journals The Construction of Intelligent English Teaching Model Based on Artificial Intelligence

2017 ◽  
Vol 12 (12) ◽  
pp. 35 ◽  
Author(s):  
Xiaoguang Li
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Learning Strategies ◽  
Learning Effect ◽  
Learning System ◽  
Lower Degree ◽  
Teaching Model ◽  
Software System ◽  
English Learning ◽  
Function Extension

In order to build a modernized tool platform that can help students improve their English learning efficiency according to their mastery of knowledge and personality, this paper develops an online intelligent English learning system that uses Java and artificial intelligence language Prolog as the software system. This system is a creative reflection of the thoughts of expert system in artificial intelligence. Established on the Struts Spring Hibernate lightweight JavaEE framework, the system modules are coupled with each other in a much lower degree, which is convenient to future function extension. Combined with the idea of expert system in artificial intelligence, the system developed appropriate learning strategies to help students double the learning effect with half the effort; Finally, the system takes into account the forgetting curve of memory, on which basis the knowledge that has been learned will be tested periodically, intending to spare students’ efforts to do a sea of exercises and obtain better learning results.

On the Analysis of Web-Based Intelligent English Self-Learning System

2014 ◽  
Vol 644-650 ◽  
pp. 6229-6232
Author(s):  
Ming Wang ◽  
Xiao Man Zheng ◽  
Yu Qian Xie ◽  
Shi Ping Yu
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Information Society ◽  
Learning System ◽  
Basic Knowledge ◽  
English Learning ◽  
Web Based ◽  
Self Learning ◽  
New System

With the development of Web-based Intelligent Learning System in the information society, we should pay attention to its usage in autonomous English learning. At first, this thesis shows the basic knowledge of Artificial Intelligence and discusses the feasibility of combining Artificial Intelligence with Web-based Intelligent English self-learning system. Second, it analyzes the combination of Expert system with Web-based Intelligent English self-learning system. Finally, it probes into that this new system will promote the revolutionary of the English self-learning mode.

scholarly journals Guide Teaching System Based on Artificial Intelligence

2018 ◽  
Vol 13 (08) ◽  
pp. 90 ◽  
Author(s):  
Dali Luo
Keyword(s):  
Artificial Intelligence ◽  
System Theory ◽  
Expert System ◽  
Learning Effect ◽  
Learning Strategy ◽  
Test Results ◽  
Artificial Intelligence System ◽  
Teaching System ◽  
Intelligence System ◽  
Classified Students

To improve the development and deployment efficiency of the system, this paper combined the software system with Java and AI language Prolog to achieve the guide teaching system based on artificial intel-ligence (AI). The system creatively adopted the theory of artificial intelligence expert system, at the same time, built a Struts+Spring+Hibernate lightweight JavaEE framework. The coupling degree of each module in the system was greatly reduced to facilitate the expansion of future functions. Based on the development principle of the artificial intelligence expert system, the system diagnosed the learner's mastery of each point of knowledge. It classified students' learning effect and evaluated the knowledge points. Making full use of the learning state of students and combining it with artificial intelligence expert system theory, the system developed a suitable learning strategy to help students improve their learning with less efforts. In addition, the system took the forgetting rule of human brain into account, which periodically presented trainees’ knowledge points assessment and avoided students wasting time. The purpose was to help students improve their learning effect. Finally, the system was tested. The test results showed that the system is applicable and useful. It is concluded that the artificial intelligence system provides an example for the same method and has certain reference significance.

scholarly journals An English Learning Method Based on Computer-assisted System

2017 ◽  
Vol 12 (02) ◽  
pp. 66 ◽  
Author(s):  
Jingyi Bo ◽  
Yubin Wang ◽  
Kun Han
Keyword(s):  
Learning Effect ◽  
Learning System ◽  
Computer Assisted ◽  
English Learning ◽  
Learning Method ◽  
Game Based Learning ◽  
Learning Pattern ◽  
Relevant Question ◽  
Examination Result ◽  
Computer Knowledge

Computer terminology is studied by unit, professional computer knowledge and lexical features, etc. in this paper. An effective game-based learning pattern is generated combined with the characteristics of computer English terms. The computer-assisted English learning system is studied to help students memorize terminology. This learning system could also examine the learning effect after study. The relevant question bank is available to users for game-based examination. The examination result and the statistic score could be kept in the question bank for analysis of the learning effect.

scholarly journals Application of Artificial Intelligence in Autonomous English Learning among College Students

2019 ◽  
Vol 14 (06) ◽  
pp. 63
Author(s):  
Bing Han
Keyword(s):  
Artificial Intelligence ◽  
College Students ◽  
Learning System ◽  
English Learning ◽  
College English ◽  
Effective Learning ◽  
Self Evaluation ◽  
Sql Server Database ◽  
Research Findings ◽  
Application Fields

To improve the English proficiency of college students, this paper attempts to promote autonomous English learning among these students using artificial intel-ligence technology. Specifically, the principles, features and application fields of artificial intelligence were introduced in details, and the requirements, structure, database and processes of autonomous English learning systems were discussed at length. On this basis, the backpropagation (BP) neural network, a typical artifi-cial intelligence technique, was selected to design the modules of the autonomous English learning system for college students. The proposed system was then im-plemented in integrated open environment of Visual Studio, the architecture of the browser/server system and the SQL Server database on the Windows platform. The results show that the proposed system provides college students with intelli-gent and open self-evaluation tests and effective learning feedbacks. The research findings shed new light on improving the English learning of college students and reliving the pressure on college English teachers.

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

scholarly journals Artificial intelligence-based education assists medical students’ interpretation of hip fracture

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Chih-Yuan Fu ◽  
Chung-Hsien Chaou ◽  
Yu-Tung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Hip Fracture ◽  
Medical Students ◽  
Medical Image ◽  
Learning System ◽  
Significant Difference ◽  
Dynamic Collaboration ◽  
Increase In Accuracy ◽  
Image Learning

Abstract Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

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