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Maryam Sarafzadeh, Soheila Alavi,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The present paper discusses results of a study which aimed to explore the knowledge and use of Online social networking by MLIS students in Iran and to explore their perceptions of using that technology for academic and professional purposes, and challenges they face for using them.
Method: The research method was explorative and empirical. Data was collected through a web-based survey questionnaire containing both open and close ended question. The link of questionnaire was emailed to MLIS students in Tehran. 113 students completed the questionnaire.
Results: The results show that 35 percent of respondents were not users of SNS. Internet filtering in Iran was identified as the major barrier on using SNS by MLIS students. Negative perceptions toward SNSs were identified as another big barrier for using SNSs by MLIS students. 22% of respondents felt that social networking would waste their time and some 14% saw on benefit on online social networking. 36% of respondents trusted very little to information released in SNSs. MLIS students were not sophisticated users of social networking sites. They have good level of skills on basic features of social networking sites. However, in more advanced features of SNSs such as privacy settings their skills seemed limited. Respondents to the questionnaire showed positive feedback on using SNS for academic and professional purposes.
Conclusion: There are several advantages for integrating SNS in LIS education. Preparing students for employing those technologies on their work places upon their graduation; increasing the employability of LIS students; reaping the educational benefits that SNSs offer and preparing students to teach social networking literacy competencies to others are some of the advantages of incorporating SNSs into LIS education. Authors suggest that skills to master online social networking must be taught in LIS education
Vahide Zeinali, Nosrat Riahinia, Vadood Javadi Parvaneh, Saeid Asadi,
Volume 4, Issue 1 (6-2017)
Volume 4, Issue 1 (6-2017)
Abstract
Background and Aim: Health Information Prescription (HIP) means delivering right information to the right person at the right time. The present study was performed to determine the effect of HIP on caregivers' self-care ability.
Materials and methods: In order to evaluate the effect of HIP on caregivers' self-care ability the quasi-experimental study was carried out on 37 caregivers. Data collection was conducted using a checklist for evaluation of self-care ability. According to the checklist, the caregivers' information including four main domains (total knowledge about JHMS, attention to the effects of JHMS, the effective treatment activities and the quest of medical services) was scored based on a 5-point Likert scale. HIP services were then delivered. The caregivers' information was evaluated once again through the checklist and the pre and post intervention results were compared.
Finding: This study showed that the mean score of self-care ability in each of four domains including total knowledge about JHMS (p=0.001), attention to the effects of JHMS (p=0.001), the effective treatment activities (p=0.001) and the quest of medical services (p=0.001) increased significantly after HIPS.
Conclusion: Results of this study indicated that HIP can increasingly improve the caregivers' self-care ability.
Mostafa Baghmirani, Mohammad Reza Esmaili Givi, Mohammad Hassanzadeh, Ali Reza Noruzi,
Volume 4, Issue 3 (12-2017)
Volume 4, Issue 3 (12-2017)
Abstract
Background and Aim: This study aims to introduce a developed conceptual model of problem finding.
Methods: This article is carried out by utilizing qualitative directed content analysis. The researcher used this method to gather new knowledge on the four features of problem finding. In total, 17 scientific sources were analyzed.
Results: This research revealed 182 codes, 22 themes that outline issues related to the research problem finding. Personality feature extended in five dimensions; psychological feature extended in four dimensions; thinking feature (creative thinking) extended in six dimensions and (critical thinking) extended in two dimensions; and educational feature extended in five dimensions.
Conclusion: Based on available data, the number of factors identified in this study is greater and more comprehensive than that of any previous research. It could provide an added value to the current researches about problem finding. This study has also developed the previous conceptual model and utilized a directed content analysis method that has never been conducted to date in the field of problem finding.
Saeed Malekmohammadi, Mohsen Zainolabedini,
Volume 5, Issue 3 (12-2018)
Volume 5, Issue 3 (12-2018)
Abstract
Background and Aim: This study attempted to investigate Khouzestan Institute for Higher Education students' viewpoints on librarians' nonverbal communication skills and their roles to attract the audience.
Methods: This is an analytical survey which used a researcher-made questionnaire to measure librarians’ communication skills in five dimensions by 39 statements. It also measured students’ satisfaction of librarians’ performance through one question. The population consisted of 3500 bachelor students enrolled in second semester of 2016-2017 academic year. According to Krejcie and Morgan Table, 346 students were determined as the sample. Using SPSS v.20 software, descriptive and inferential statistics were applied.
Results: From the students point of view the total score of nonverbal communication skills of librarians was 4.58±0.71. The highest means belonged to “facial expression” (4.70±.30) and “their situations and hints (4.64±.468). The score of students’ satisfaction with librarians was 4.68±0.47, accordingly.
Conclusion: There are a lot of research on non-verbal communications skills in general; however, there was no sufficient research on librarian's non-verbal communication skills. Library in academic institutions, where librarians interact with the youth, non-verbal communication skills may prevent many unnecessary encounters.
Ms Maryam Babaee, Dr Hassan Rastegarpour,
Volume 6, Issue 4 (3-2020)
Volume 6, Issue 4 (3-2020)
Abstract
Background and Aim: With the advent of technology and the use of social networks such as Instagram, Facebook, blogs, forums, and many other platforms, interactions of learners with one another and their lecturers have become progressively relaxed. This has led to the accumulation of large quantities of data and information about students' attitudes, learning experiences, opinions, and feelings about the teaching-learning process. Opinion mining is one of the growing applications of data mining knowledge which by discovering patterns and models in users' opinions could help higher education to well plan, make well-versed policies, and to have fruitful management. Therefore, the purpose is to describe the applications of opinion mining to advance the excellence of higher education in Iran.
Methodology: Research method is an applied qualitative one. Population comprises of all the research and books associated with opinion mining that were available in reputable databases of IEEE, SSCI, Elsevier, CIVILICA, and Science Direct during the research data collection period in the spring of 2019. Using the convenience sampling method, 35 articles were selected with the aim of reviewing and describing educational opinion mining and analyzing its application in higher education.
Results: Based on the studies, it was found that opinion mining can be used as an effective tool in three parts: 1. Improving student performance; 2. Designing better online courses; and 3. Evaluating the efficiency of the educational activities of universities, professors, and various programs. Therefore it can also help to recognize the existing shortcomings, strengths, and weaknesses.
Conclusion: Higher education can scrutinize the sentiments, opinions, and ideas generated by students through opinion mining. Exploring this valuable information enables educational institutions, principals, and educators to make more appropriate decisions in education and improve the quality of educational services which leads to the improvement of academic performance and better career choices for individuals.
Mrs Maryam Tavosi, Mr Nader Naghshineh,
Volume 7, Issue 3 (12-2020)
Volume 7, Issue 3 (12-2020)
Abstract
Purpose: This is an applied research, with the aim of a comparative study of the presence and participation of Iranian and international researchers affiliated with the top scientific centers (Times Ranking 2020), in the Research Gate research network. Altimetric indicators, such as "RG score", "Reads", "number of registrations" and "number of research items" were considered.
Methodology: Survey performed with altimetric approach and analytical method. Sample of top 10 universities in Iran and 10 scientific centers around the world by Times Ranking performance index of education, research, knowledge transfer, and international perspective done. First, a comparative study of the activities of Iranian researchers with one another, then of international researchers completed separately. Lastly, an analysis of the differences in performance amongst these two groups was performed by "Libre Office Calc" software.
Findings: Among Times top 10 international scientific institutes, the indicators of "number of registrations," "RG-score per member," "number of publications per member" and "reads" the highest rates were observed in researchers at Cambridge University in London, the California research center, and the California research center at Oxford university, respectively. Among the top 10 Universities in Iran, the indicators of "number of registrations," "RPG score per media member," "number of research copies per member" and "reading rate of research copies, "among the researchers with organizational affiliation to the Amir Kabir University of technology, Tehran University of medical sciences, Iran university of medical sciences, Tehran university of medical sciences, the highest amount was observed. The total "average score per member" at the international level was 8.4 and at the Iranian level was 5.1. The "average reads" index for the top 10 universities or research institutes at the international level was 154990.2. The figure was obtained for the top 10 universities in Iran, 22736.1.
Conclusion: Researchers affiliated with top universities in Iran, compared to their international counterparts, have a stronger social interaction in terms of indicators of ResearchGate in activities such as "enquiring," "answering questions" and "suggestion." Although the difference between the number of research items shared internationally is more than 3 times that of Iran, but the average RG score is not seen 3 times that of Iran globally. So, the high RG score is not related to the number of research items on the ResearchGate. Also, comparative study on the presence and activities of researchers affiliated with Times top Universities in Iran and internationally could lead to better future.
Mrs Fariba Mirzaei, Dr Maryam Sarrafzadeh, Dr Mohammad Khandan,
Volume 7, Issue 3 (12-2020)
Volume 7, Issue 3 (12-2020)
Abstract
Aim: today, digital literacy, as a set of skills that enable people to use digital space effectively for success in personal, educational and professional life, has become a necessity in all societies and public libraries are one of the most important providers of digital literacy education in the world. Digital literacy education has not been considered in public libraries in Iran. The first step in promoting digital literacy education in Iran`s public libraries is to design courses and topics that can cover a variety of this aspects of the phenomenon. Now, the boundaries of this content art not clear, and this research seeks to design a comprehensive framework to meet this need. The purpose of this study is to present a content framework for digital literacy education to Iranian public libraries users.
Methodology: this research is a qualitative research and it`s method is qualitative content analysis. at first, the courses provided by the public libraries of Iran Were reviewed and then content analysis of digital literacy courses was conducted in public libraries of developed countries. 16 libraries were selected from English speaking countries that hold digital literacy courses and share information on their websites by cluster sampling with probability proportional to size. To perform Delphi, Experts from the fields of library and information science and information technology, and public libraries professionals were selected.
Findings: 9 courses were identified and after compiling a questio0nnaire and preforming two Delphi rounds, these courses were reduced to 8 main courses and 37 sub-courses. These courses were included computer, internet, job management, information literacy, financial literacy, learning skills, effective communication skills and how to work with smartphones. Software courses were not considered suitable by specialists. This seems to be due to the different groups of society and the heterogeneous community of public libraries users and the experts do not see this as a readiness in the society to receive software related courses.
Conclusion: digital literacy education in Iran has generally neglected. Generally, digital literacy education in universities and educational institutions is provided in formal computer training courses. Libraries in general have paid very little attention to the issue of education. the first step in advancing digital literacy education in public libraries is designing courses that can covers the various aspects of this phenomenon. Educational content is also one of the sides of the education triangle (learner, educator and educational content). However today the boundaries of this content are not clear and this study try to design a comprehensive framework to meet this need.
Professor Saleh Rahimi, Ms Fatemeh Rahimi, Samira Daniali,
Volume 9, Issue 3 (10-2022)
Volume 9, Issue 3 (10-2022)
Abstract
Purpose: Due to increasing the amount of information and the importance of applying images in various fields, the significance of visual literacy is obvious and research showed that visual literacy has a positive effect on learning. So, the purpose of this study was to determine the importance of visual literacy and to identify the necessity of its use in education and learning, as well as to dictate the components of visual literacy to improve learning.
Methodology: Using the library method and utilizing descriptive research method, the literature in the field of visual literacy has been reviewed
Findings: Visual literacy is typically considered as an ability to evaluate, analyze and interpret the meanings of images and their use, also as a main concept is contemplated.
Conclusion: Visual literacy standards help us study, analyze, understand, and recognize visual elements based on a reasonable and pre-defined framework. It makes people benefit more appropriately from information resources in the form of symbols and images. The concept of visual literacy is evolving and requires more advanced tools. Therefore, students must be acquainted with the tools of this technique.
Methodology: Using the library method and utilizing descriptive research method, the literature in the field of visual literacy has been reviewed
Findings: Visual literacy is typically considered as an ability to evaluate, analyze and interpret the meanings of images and their use, also as a main concept is contemplated.
Conclusion: Visual literacy standards help us study, analyze, understand, and recognize visual elements based on a reasonable and pre-defined framework. It makes people benefit more appropriately from information resources in the form of symbols and images. The concept of visual literacy is evolving and requires more advanced tools. Therefore, students must be acquainted with the tools of this technique.
Yaghoub Norouzi, Nayereh Jafarifar, Zahra Bighlari,
Volume 10, Issue 1 (6-2023)
Volume 10, Issue 1 (6-2023)
Abstract
purpose: The article aims to identify and prioritize indicators for evaluating the accessibility capabilities of the user interface in virtual education systems.
Methodology: In step 1, to identify the evaluation indicators from the indicators listed in ISO 9241, a localized checklist was prepared by the virtual education systems inside the country. Then, using the Fuzzy Delphi method and the opinions of experts from the higher education system of Iran in the field of accessibility of education and e-learning systems, the identified indicators were modified and finalized. In the next step, these indicators were prioritized using the BWM method from the point of view of experts, and their importance was determined.
Findings: The final framework of indicators for evaluating the accessibility capabilities of the interaction environment (user interface) of virtual education systems was compiled and prioritized in four general indicators (general guidelines and requirements, inputs, outputs, support services, help, and online documentation) and 24 sub-indices. For this purpose, LINGO software was used. Based on the findings of the research, the component "Compatibility with accessible technologies" won the first rank among all sub-indices. The component "online documentation" was ranked second and "setting accessibility levels" was ranked third. The last rank (rank 24) was awarded to "Camera".
Conclusion: There is no consensus on the standard framework for evaluating the accessibility of virtual education systems. The review of the conducted research showed that there is a research gap in the field of not comprehensively identifying and presenting a comprehensive and coherent picture to evaluate the accessibility of the interaction environment in virtual education systems and it was concluded that to improve the use of virtual education systems, identifying and prioritizing the factors It is necessary to evaluate the accessibility of virtual education systems. The innovation of this article is to provide a comprehensive framework for identifying and prioritizing the accessibility evaluation indicators of the interactive environment in virtual education systems localized for the country. The internet speed in Iran is not high and turning on the camera during virtual classes due to the high volume of the internet it consumes causes disconnection and communication between them. The statistical community of the research was aware of this fact, therefore, according to the existing conditions, they assigned the least weight to this index.
Methodology: In step 1, to identify the evaluation indicators from the indicators listed in ISO 9241, a localized checklist was prepared by the virtual education systems inside the country. Then, using the Fuzzy Delphi method and the opinions of experts from the higher education system of Iran in the field of accessibility of education and e-learning systems, the identified indicators were modified and finalized. In the next step, these indicators were prioritized using the BWM method from the point of view of experts, and their importance was determined.
Findings: The final framework of indicators for evaluating the accessibility capabilities of the interaction environment (user interface) of virtual education systems was compiled and prioritized in four general indicators (general guidelines and requirements, inputs, outputs, support services, help, and online documentation) and 24 sub-indices. For this purpose, LINGO software was used. Based on the findings of the research, the component "Compatibility with accessible technologies" won the first rank among all sub-indices. The component "online documentation" was ranked second and "setting accessibility levels" was ranked third. The last rank (rank 24) was awarded to "Camera".
Conclusion: There is no consensus on the standard framework for evaluating the accessibility of virtual education systems. The review of the conducted research showed that there is a research gap in the field of not comprehensively identifying and presenting a comprehensive and coherent picture to evaluate the accessibility of the interaction environment in virtual education systems and it was concluded that to improve the use of virtual education systems, identifying and prioritizing the factors It is necessary to evaluate the accessibility of virtual education systems. The innovation of this article is to provide a comprehensive framework for identifying and prioritizing the accessibility evaluation indicators of the interactive environment in virtual education systems localized for the country. The internet speed in Iran is not high and turning on the camera during virtual classes due to the high volume of the internet it consumes causes disconnection and communication between them. The statistical community of the research was aware of this fact, therefore, according to the existing conditions, they assigned the least weight to this index.
Model of the antecedents and consequences of students self-disclosure in the educational environment
Mrs Shamsi Sokout, Mrs Maryam Slampanah, Mr Mohamad Javad Karam Afroz, Mrs Faranak Mousavi,
Volume 10, Issue 3 (11-2023)
Volume 10, Issue 3 (11-2023)
Abstract
purpose: The purpose of this research was to present a model of the antecedents and consequences of self-disclosure in the country's educational environment.
Although self-disclosure, its effective factors and consequences have special value from the aspect of many sciences such as counseling, educational management, educational sciences, psychology, etc., and numerous studies have been conducted on it, but a careful review of the research literature proved that so far no comprehensive and written study with a mixed approach and in the form of providing an integrated model has been done that has investigated the dimensions of this concept, its effective factors and consequences. Each of the existing studies have investigated one or more variables affecting self-disclosure separately. Therefore, according to the existing study gap, according to the history of the researcher's educational activity as a teacher and understanding the importance of self-disclosure in order to maintain the mental health of students of this country, the concern arises that in addition to a comprehensive look at the conducted researches, with an in-depth perspective, analyze this structure, its components, factors affecting it and its consequences in the educational environment of the country. Therefore, the main object of this research is "providing a model of the antecedents and consequences of self-disclosure in the educational environment of the country". In this research, the researcher intends to present effective factors and possible consequences in the form of a conceptual model.
Methodology: The methodology of this research is quantitative-descriptive. The sample of the current research is the first high school students of selected Tehran areas. These areas were selected by multi-stage cluster sampling method. According to subject of this research and the studied population (target), the sample size is selected according to the application of structural equations software, in the number of 600 students of the first secondary school in Tehran. For this purpose, the city of Tehran was first divided into five regions of North, South, East, West and Center by cluster sampling method. Then, from these five regions (regions 1, 5, 6, 8, 16), schools were selected as a cluster and then random samples were selected from each school. In this research, the researcher intends to use the questionnaire extracted from the qualitative part (made by the researcher). This questionnaire consists of two parts. The first part consists of demographic characteristics including gender, age, education level, and region, etc and the second part will contain specialized questions with a response package prepared on a five-point Likert scale.
A: Reflective measurement model test (cv com test)
B: Structural model tests include:
• Hypothesis significance test (direct path)
• R-squares or R2 test (determination coefficient)
• Structural model quality test (Cv Red)
C: the general fit test of the model at the end (SRMR)
Findings: According to the data obtained, there are 172 students in the seventh grade and 31.44%, 180 students in the eighth grade and 32.91%, and 195 students in the ninth grade and 35.65% and a total of 547 people answered the questions of the questionnaire in the educational levels of the first secondary school. According to the obtained data, all values of CV com are higher than 0.15. As a result, the quality of the measurement model is medium to high.
The results show that after removing the counseling expertise variable, the paths of the model from antecedents to self-disclosure and from self-disclosure to the consequences of self-disclosure are significant. It can be concluded that the components of trust, internal motivation, experience, being supported, context of acceptance and environmental conditions are the antecedents of students' self-disclosure. And the components of privacy awareness, mutual communication, creative thinking, problem solving, privacy risk, self-belief, optimism, monitorability, empathy, effective listening and learning from According to the obtained data, the effect of internal motivation on self-disclosure is T-Value = 7.509, the effect of trust on self-disclosure is T-Value = 4.632, the effect of having experience on self-disclosure is T-Value = 2.017, the effect of acceptance context on self-disclosure T-Value = 3.461, the effect of environmental conditions on self-disclosure T-Value = 4.343 and the effect of being supported on self-disclosure with T-Value = 4.392 which It is in the range [-1/96, 1/96]. P-Value=0, which is less than 0.05, rejects the H0 hypothesis and accepts the H1 hypothesis at the 95% confidence level. But the effect of consultant's expertise on self-disclosure T-Value = 0.5, which is not outside the range [-1.96, 1.96] and P-Value = 0.617, which is not less than 0.05, at the confidence level of 95 % HO hypothesis is confirmed and H1 hypothesis is rejected. And on the other hand, the value of β, which shows the intensity and direction of the effect, is positive in internal motivation 0.259, trust 0.168, experience 0.065, environment acceptance 0.12, environmental conditions 0.145, and being supported 0.2; therefore, it is predicted that if the research is repeated in a larger sample of the same community, this hypothesis will be confirmed, and on the other hand, the β value that shows the intensity and direction of the effect, the consultant's expertise is 0.015 and its direction is negative; Therefore, this hypothesis is not confirmed.
According to the obtained data, the effect of self-disclosure on mutual relationship T-value= 799.56, the effect of self-disclosure on creative thinking T-value = 252.22, the effect of self-disclosure on problem solving skill T-value = 684.41 Value, the effect of self-disclosure on self-belief, T-Value = 862/55, the effect of self-disclosure on monitorability, T-Value = 228/56, the effect of self-disclosure on empathy, T-Value = 10/911, the effect of self-disclosure on learning from mistakes T-Value = 64.12, the effect of self-disclosure on privacy awareness T-Value = 118.20, the effect of self-disclosure on privacy risk T-Value = 23.5, the effect of self-disclosure on optimism T-Value = 113.19 and the effect of self-disclosure on effective listening T-Value = 15.407, which is outside the range [-1.96, 1.96]. And P-Value = 0, which is less than 0.05, rejects the H0 hypothesis and confirms the H1 hypothesis at the 95% confidence level. And on the other hand, the value of β, which shows the intensity and direction of the effect, is 0.818 in mutual communication, 0.626 in creative thinking, 0.771 in problem solving skill, 0.829 in self-belief, 0.82 in monitorability, in empathy is 0.419, learning from mistakes is 0.468, privacy awareness is 0.591, privacy risk is 0.214, optimism is 0.551, and effective listening is 0.54. Therefore, it is expected that if the research is repeated in a larger sample of the same community, this hypothesis will be confirmed.
According to the findings, SRMR=0.062 is less than 0.08, so it can be concluded that the overall model has a good fit. The quality and fit status of the structural model resulting from the qualitative part of the research, which was one of the objectives of the quantitative part, was also examined.
In the present study, to calculate the goodness of fit (GOF), the average (R2) and (AVE) of the research components were extracted from smartpls software and included in the relevant formula.
GOF = √ 0.628192308× 0.685423077 = 0.656
According to the obtained data and the calculation result of GOF = 0.656, it can be said: the research model has a very good fit.
Conclusion: School is the foundation to all existing structures in society. A teacher is one of the most important role of a society and the first adult person after parents that the way of communicating with them is very important and has a serious impact on the emotional and social performance of students in school. The effective communication between students and the teacher not only gives the teacher a lot of motivation, but also can lead to creativity, satisfaction, academic progress, and better behavior of students in school and society. And finally, it should lead to students and teacher's mental health and create a foundation for establishing beneficial social relationships of students throughout their lives.
Self-disclosure is a process in which people verbally express their personal information or experiences to others. The school is one of the most important organized social institutions that by providing a healthy environment can cause the growth and prosperity of people's body and mind; This is shaped by students' relationships with teachers, counselors, and school administrators, as well as students' emotional growth, and self-disclosure develops this relationship.
Although self-disclosure, its effective factors and consequences have special value from the aspect of many sciences such as counseling, educational management, educational sciences, psychology, etc., and numerous studies have been conducted on it, but a careful review of the research literature proved that so far no comprehensive and written study with a mixed approach and in the form of providing an integrated model has been done that has investigated the dimensions of this concept, its effective factors and consequences. Each of the existing studies have investigated one or more variables affecting self-disclosure separately. Therefore, according to the existing study gap, according to the history of the researcher's educational activity as a teacher and understanding the importance of self-disclosure in order to maintain the mental health of students of this country, the concern arises that in addition to a comprehensive look at the conducted researches, with an in-depth perspective, analyze this structure, its components, factors affecting it and its consequences in the educational environment of the country. Therefore, the main object of this research is "providing a model of the antecedents and consequences of self-disclosure in the educational environment of the country". In this research, the researcher intends to present effective factors and possible consequences in the form of a conceptual model.
Methodology: The methodology of this research is quantitative-descriptive. The sample of the current research is the first high school students of selected Tehran areas. These areas were selected by multi-stage cluster sampling method. According to subject of this research and the studied population (target), the sample size is selected according to the application of structural equations software, in the number of 600 students of the first secondary school in Tehran. For this purpose, the city of Tehran was first divided into five regions of North, South, East, West and Center by cluster sampling method. Then, from these five regions (regions 1, 5, 6, 8, 16), schools were selected as a cluster and then random samples were selected from each school. In this research, the researcher intends to use the questionnaire extracted from the qualitative part (made by the researcher). This questionnaire consists of two parts. The first part consists of demographic characteristics including gender, age, education level, and region, etc and the second part will contain specialized questions with a response package prepared on a five-point Likert scale.
A: Reflective measurement model test (cv com test)
B: Structural model tests include:
• Hypothesis significance test (direct path)
• R-squares or R2 test (determination coefficient)
• Structural model quality test (Cv Red)
C: the general fit test of the model at the end (SRMR)
Findings: According to the data obtained, there are 172 students in the seventh grade and 31.44%, 180 students in the eighth grade and 32.91%, and 195 students in the ninth grade and 35.65% and a total of 547 people answered the questions of the questionnaire in the educational levels of the first secondary school. According to the obtained data, all values of CV com are higher than 0.15. As a result, the quality of the measurement model is medium to high.
The results show that after removing the counseling expertise variable, the paths of the model from antecedents to self-disclosure and from self-disclosure to the consequences of self-disclosure are significant. It can be concluded that the components of trust, internal motivation, experience, being supported, context of acceptance and environmental conditions are the antecedents of students' self-disclosure. And the components of privacy awareness, mutual communication, creative thinking, problem solving, privacy risk, self-belief, optimism, monitorability, empathy, effective listening and learning from According to the obtained data, the effect of internal motivation on self-disclosure is T-Value = 7.509, the effect of trust on self-disclosure is T-Value = 4.632, the effect of having experience on self-disclosure is T-Value = 2.017, the effect of acceptance context on self-disclosure T-Value = 3.461, the effect of environmental conditions on self-disclosure T-Value = 4.343 and the effect of being supported on self-disclosure with T-Value = 4.392 which It is in the range [-1/96, 1/96]. P-Value=0, which is less than 0.05, rejects the H0 hypothesis and accepts the H1 hypothesis at the 95% confidence level. But the effect of consultant's expertise on self-disclosure T-Value = 0.5, which is not outside the range [-1.96, 1.96] and P-Value = 0.617, which is not less than 0.05, at the confidence level of 95 % HO hypothesis is confirmed and H1 hypothesis is rejected. And on the other hand, the value of β, which shows the intensity and direction of the effect, is positive in internal motivation 0.259, trust 0.168, experience 0.065, environment acceptance 0.12, environmental conditions 0.145, and being supported 0.2; therefore, it is predicted that if the research is repeated in a larger sample of the same community, this hypothesis will be confirmed, and on the other hand, the β value that shows the intensity and direction of the effect, the consultant's expertise is 0.015 and its direction is negative; Therefore, this hypothesis is not confirmed.
According to the obtained data, the effect of self-disclosure on mutual relationship T-value= 799.56, the effect of self-disclosure on creative thinking T-value = 252.22, the effect of self-disclosure on problem solving skill T-value = 684.41 Value, the effect of self-disclosure on self-belief, T-Value = 862/55, the effect of self-disclosure on monitorability, T-Value = 228/56, the effect of self-disclosure on empathy, T-Value = 10/911, the effect of self-disclosure on learning from mistakes T-Value = 64.12, the effect of self-disclosure on privacy awareness T-Value = 118.20, the effect of self-disclosure on privacy risk T-Value = 23.5, the effect of self-disclosure on optimism T-Value = 113.19 and the effect of self-disclosure on effective listening T-Value = 15.407, which is outside the range [-1.96, 1.96]. And P-Value = 0, which is less than 0.05, rejects the H0 hypothesis and confirms the H1 hypothesis at the 95% confidence level. And on the other hand, the value of β, which shows the intensity and direction of the effect, is 0.818 in mutual communication, 0.626 in creative thinking, 0.771 in problem solving skill, 0.829 in self-belief, 0.82 in monitorability, in empathy is 0.419, learning from mistakes is 0.468, privacy awareness is 0.591, privacy risk is 0.214, optimism is 0.551, and effective listening is 0.54. Therefore, it is expected that if the research is repeated in a larger sample of the same community, this hypothesis will be confirmed.
According to the findings, SRMR=0.062 is less than 0.08, so it can be concluded that the overall model has a good fit. The quality and fit status of the structural model resulting from the qualitative part of the research, which was one of the objectives of the quantitative part, was also examined.
In the present study, to calculate the goodness of fit (GOF), the average (R2) and (AVE) of the research components were extracted from smartpls software and included in the relevant formula.
GOF = √ 0.628192308× 0.685423077 = 0.656
According to the obtained data and the calculation result of GOF = 0.656, it can be said: the research model has a very good fit.
Conclusion: School is the foundation to all existing structures in society. A teacher is one of the most important role of a society and the first adult person after parents that the way of communicating with them is very important and has a serious impact on the emotional and social performance of students in school. The effective communication between students and the teacher not only gives the teacher a lot of motivation, but also can lead to creativity, satisfaction, academic progress, and better behavior of students in school and society. And finally, it should lead to students and teacher's mental health and create a foundation for establishing beneficial social relationships of students throughout their lives.
Self-disclosure is a process in which people verbally express their personal information or experiences to others. The school is one of the most important organized social institutions that by providing a healthy environment can cause the growth and prosperity of people's body and mind; This is shaped by students' relationships with teachers, counselors, and school administrators, as well as students' emotional growth, and self-disclosure develops this relationship.
ِdr Shahnaz Khademizadeh, Mrs Fatemeh Rafieinasab, Dr Natarajan Radhakrishnan,
Volume 11, Issue 1 (6-2024)
Volume 11, Issue 1 (6-2024)
Abstract
Introduction
With the emergence of the Internet and the expansion of information technologies in the current era, along with the increase in awareness and literacy levels in human societies, there has been a growing interest in accessing information in various fields. One type of information that has always captivated the general public is health-related information, as it directly impacts their quality of life. Health information encompasses a wide range of data that can influence decisions related to individual and social well-being. Electronic health resources play a crucial role in helping individuals manage important health issues, make informed decisions about their health, and communicate with healthcare providers. Studies have shown that internet users not only exhibit better adherence to treatment, reduced anxiety, and a greater sense of security, but also demonstrate improved self-care behaviors compared to those who do not utilize online resources (Riahi, 2017). By seeking health information online, individuals can gain a better understanding of their health status, which in turn can lead to the formation of informed opinions, beliefs, and attitudes towards healthy behaviors, ultimately aiding in making informed decisions regarding healthcare (Ahedzadeh and Sharif, 2017; Brown, Skelly, Chew-Graham, 2020). Therefore, the objective of this study is to explore the health information-seeking behavior of clients within health and treatment networks in Ahvaz city.
Methods and Materoal
The current research is an experimental study conducted using the pre-test and post-test intervention method. The research population consisted of 40 clients from Ahvaz Medical Center, whose average score of health information-seeking behavior was one point below the average. Of these, 20 individuals were randomly assigned to the control group. Additionally, two groups of 20 people each were formed using block randomization: one as the control group and the other as the test group for the intervention. The educational intervention took the form of a 4-hour online training workshop. To assess changes in online health information search behaviors, uncertainty, and cyberchondria in the study group, the Generalized Estimating Equations (GEE) method was utilized. The protocol used for online education on social networks was the SDI protocol (Bhushan 2006, Juyani et al. 2022). A questionnaire served as the data collection tool, completed by the test groups within one month and returned to the researchers. Data collection occurred at baseline (pre-intervention) and at one, two, and three months post-intervention. Baseline scores were used as covariates in the model to adjust for differences between the control and intervention groups. The results of the GEE model reflected changes in the desired factors at the three measurement points post-intervention. Furthermore, Bonferroni's post hoc test was employed to compare the distribution of online health information search scores, uncertainty, and cyberchondria between the two groups at each time point. All analyses were conducted using SPSS software version 26.
Resultss and Discussion
Findings: There was no significant difference in the effect of the educational intervention on changes in the online health information search score (P>0.05). For the control group, the average uncertainty score increased over time. However, there was a significant difference in the effect of the educational intervention on changes in the uncertainty score (P<0.001). Results of the external post-hoc test comparing changes in the uncertainty score between the two intervention groups and the control group showed a significant difference in the distribution of uncertainty scores before the intervention (P>0.046), as well as two and three months after the intervention (P>0.001). Additionally, there was a significant difference in the cyberchondria score before the intervention (P>0.076) and three months after the intervention (P>0.025) in each case.
Conclusion
Health information behavior training is a positive step towards increasing the decision-making self-efficacy of community members and patients. When combined with other efforts to promote health and care, it can lead to improved health outcomes. The effectiveness of educational interventions in different groups depends on the methods, materials, and content used. This study found that a patient educational intervention aimed at teaching information-seeking behavior skills to health center visitors had a positive impact on reducing cyberchondria and increasing certainty. The availability of information search platforms, the type of sources used, and the ability to utilize them based on age requirements are important factors in information seeking (Bahadir and Dundar, 2022). Uncertainty and doubt can drive individuals to seek health information online (Khademizadeh, Rafieinasab and Radhakrishnan, 2024), making educational interventions focused on empowerment dimensions beneficial in improving clinical and psychological outcomes. These interventions can enhance quality of life for both society and patients, taking into account factors such as age, education, occupation, and income status. This research presents a new approach to educational interventions that are cost-effective, accessible, and complication-free for health management and promotion in society.
With the emergence of the Internet and the expansion of information technologies in the current era, along with the increase in awareness and literacy levels in human societies, there has been a growing interest in accessing information in various fields. One type of information that has always captivated the general public is health-related information, as it directly impacts their quality of life. Health information encompasses a wide range of data that can influence decisions related to individual and social well-being. Electronic health resources play a crucial role in helping individuals manage important health issues, make informed decisions about their health, and communicate with healthcare providers. Studies have shown that internet users not only exhibit better adherence to treatment, reduced anxiety, and a greater sense of security, but also demonstrate improved self-care behaviors compared to those who do not utilize online resources (Riahi, 2017). By seeking health information online, individuals can gain a better understanding of their health status, which in turn can lead to the formation of informed opinions, beliefs, and attitudes towards healthy behaviors, ultimately aiding in making informed decisions regarding healthcare (Ahedzadeh and Sharif, 2017; Brown, Skelly, Chew-Graham, 2020). Therefore, the objective of this study is to explore the health information-seeking behavior of clients within health and treatment networks in Ahvaz city.
Methods and Materoal
The current research is an experimental study conducted using the pre-test and post-test intervention method. The research population consisted of 40 clients from Ahvaz Medical Center, whose average score of health information-seeking behavior was one point below the average. Of these, 20 individuals were randomly assigned to the control group. Additionally, two groups of 20 people each were formed using block randomization: one as the control group and the other as the test group for the intervention. The educational intervention took the form of a 4-hour online training workshop. To assess changes in online health information search behaviors, uncertainty, and cyberchondria in the study group, the Generalized Estimating Equations (GEE) method was utilized. The protocol used for online education on social networks was the SDI protocol (Bhushan 2006, Juyani et al. 2022). A questionnaire served as the data collection tool, completed by the test groups within one month and returned to the researchers. Data collection occurred at baseline (pre-intervention) and at one, two, and three months post-intervention. Baseline scores were used as covariates in the model to adjust for differences between the control and intervention groups. The results of the GEE model reflected changes in the desired factors at the three measurement points post-intervention. Furthermore, Bonferroni's post hoc test was employed to compare the distribution of online health information search scores, uncertainty, and cyberchondria between the two groups at each time point. All analyses were conducted using SPSS software version 26.
Resultss and Discussion
Findings: There was no significant difference in the effect of the educational intervention on changes in the online health information search score (P>0.05). For the control group, the average uncertainty score increased over time. However, there was a significant difference in the effect of the educational intervention on changes in the uncertainty score (P<0.001). Results of the external post-hoc test comparing changes in the uncertainty score between the two intervention groups and the control group showed a significant difference in the distribution of uncertainty scores before the intervention (P>0.046), as well as two and three months after the intervention (P>0.001). Additionally, there was a significant difference in the cyberchondria score before the intervention (P>0.076) and three months after the intervention (P>0.025) in each case.
Conclusion
Health information behavior training is a positive step towards increasing the decision-making self-efficacy of community members and patients. When combined with other efforts to promote health and care, it can lead to improved health outcomes. The effectiveness of educational interventions in different groups depends on the methods, materials, and content used. This study found that a patient educational intervention aimed at teaching information-seeking behavior skills to health center visitors had a positive impact on reducing cyberchondria and increasing certainty. The availability of information search platforms, the type of sources used, and the ability to utilize them based on age requirements are important factors in information seeking (Bahadir and Dundar, 2022). Uncertainty and doubt can drive individuals to seek health information online (Khademizadeh, Rafieinasab and Radhakrishnan, 2024), making educational interventions focused on empowerment dimensions beneficial in improving clinical and psychological outcomes. These interventions can enhance quality of life for both society and patients, taking into account factors such as age, education, occupation, and income status. This research presents a new approach to educational interventions that are cost-effective, accessible, and complication-free for health management and promotion in society.
Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
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