Authors: (1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran; (2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran; (3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada (4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Table of Links Abstract and Introduction Materials and Methods Results RQ 1: AI and scientometrics RQ 2: AI and webometrics RQ 3: AI and bibliometrics Discussion RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI Conclusion, Limitations, and References Materials and Methods Our study has involved conducting a thorough review of the existing literature to explore the various aspects and several indicators related to the use of AI-enhanced in Scientometrics, Webometrics, and Bibliometrics. Throughout the preparation of this manuscript, we have adhered to the PRISMA-ScR checklist and followed the recommended reporting guidelines for systematic reviews [19]. It is important to note that this manuscript has not been previously registered in PROSPERO or any similar database. We want to emphasize that while PROSPERO registration is typically associated with systematic reviews, we have made a deliberate decision not to register this specific review. This decision is based on the scope of our review, which does not strictly meet the eligibility criteria of PROSPERO, and the practicality within the limitations of our project. We want to assure readers that our literature search and selection process follow rigorous methodology, and our findings are reported transparently, thus in order to address any concerns regarding credibility. Research Questions "How do the cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research evaluation and impact assessment?" "What advancements have been made in AI-enhanced webometrics and how do they enhance the understanding of web-based information and online user behavior?" "In what ways do the cutting-edge techniques in AI-enhanced bibliometrics revolutionize the analysis and measurement of scholarly publications and their impact?" • Additionally, we are seeking answers to the following inquiries: "What does the future hold for Scientometrics, Webometrics, and Bibliometrics with AI?" "What are the ethical considerations that need to be taken into account when utilizing AI in Scientometrics, Webometrics, and Bibliometrics?" Inclusion and Exclusion Criteria During the study selection process, we implemented specific criteria to identify relevant articles from the database. We considered various types of articles, excluding systematic review articles as our aim is to concentrate on original research studies, and meta-analyses as they often have their own distinct inclusion and exclusion criteria that may differ from ours. The selected articles needed to focus on the use of AI to transform the measurement and analysis of scholarly communication, do identify emerging research trends and, evaluate the impact of scientific publications. Consequently, articles solely addressing the analysis of scholarly communication and the impact of scientific publications without any relevance to AI are excluded from the review. Through the application of these criteria, we ensure that the chosen studies directly address the analysis of AI-enhanced techniques in the field of scientometrics, webometrics, and bibliometrics, enabling us to provide a targeted and focused analysis for our research. Databases and Search Method We have conducted searches in several databases including ProQuest (LISTA & IBSS), EBSCO (LISTA), IEEE Explore, Web of Science, and Scopus to identify relevant studies. The search was limited to articles published between January 1, 2000, and September 2022, in order to encompass the most recent literature related to our research objectives. To ensure a comprehensive search strategy, we utilized a combination of broad search terms and conducted a nested search [20]. The search strategy involved using keywords that were relevant to our research topic, including variations and synonyms to maximize coverage. For instance, in Scopus, our search string included terms such as "AI" OR "Artificial Intelligence" AND "Scientometrics" OR "Webometrics" OR "Bibliometrics" or variations of it. By incorporating these keywords and using Boolean operators to combine them, our aim was to identify articles that focused on the impact, effectiveness, and evaluation of healthcare or smart health technologies. The specific search terms and string may have varied slightly for each database, but they followed a similar structure. Study selection The study selection process consisted of two steps to identify articles that met our inclusion criteria. Initially, two independent reviewers (HR.S. and E.H.) screened the titles and abstracts of the identified articles to determine their relevance to our research question and inclusion criteria. Any disagreements between the reviewers were resolved through discussion and consensus. If disagreements persisted, a third reviewer (M.A.) was consulted as an arbitrator. The third reviewer carefully examined the articles in question and provided input to reach a consensus. This approach ensured that the final selection of articles was (and is) based on collective agreement. Study quality appraisal The quality assessment of the included reviews was conducted by two researchers (HR.S. and E.H.) using the CASP Systematic Review Checklist (Appendix 1). We resolved any disagreements through discussion and reached a consensus on the quality of each study. Coding framework for analyzing the selected articles To ensure a systematic and consistent analysis of the selected articles, a coding framework was developed and applied. The coding framework consisted of several key categories and criteria that guided the analysis process. The following is an overview of the coding framework used: 1. Category 1: Research Methodology - Criteria: Identify the research methodology employed in each article (e.g., experimental, survey, case study, etc.). 2. Category 2: AI Applications - Criteria: Determine the specific applications of artificial intelligence discussed or utilized in each article (e.g., machine learning, natural language processing, data mining, etc.). 3. Category 3: Metrics and Measures - Criteria: Capture the different metrics and measures used or proposed in the articles for evaluating the impact or effectiveness of the AI applications in scientometrics, webometrics, and bibliometrics. 4. Category 4: Ethical Considerations - Criteria: Identify any ethical considerations or implications discussed in relation to the AI applications in the selected articles. 5. Category 5: Future Implications - Criteria: Examine the discussions or predictions regarding the future implications and potential developments related to the use of AI in scientometrics, webometrics, and bibliometrics. During the analysis, two independent researchers coded each article using this framework. Any discrepancies or disagreements in coding were resolved through discussion and consensus. Intercoder reliability was assessed by calculating Cohen's kappa coefficient, which yielded a substantial agreement level of 0.85. By employing this coding framework, we aimed to provide a comprehensive analysis of the selected articles and ensure consistency in the evaluation of relevant aspects. The coding process allowed for a systematic examination of the research methodology, AI applications, metrics, ethical considerations, and future implications discussed in each article. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran; (2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran; (3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada (4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Authors: Authors: (1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran; (2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran; (3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada (4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania. Table of Links Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Results Results RQ 1: AI and scientometrics RQ 1: AI and scientometrics RQ 2: AI and webometrics RQ 2: AI and webometrics RQ 3: AI and bibliometrics RQ 3: AI and bibliometrics Discussion Discussion RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI Conclusion, Limitations, and References Conclusion, Limitations, and References Materials and Methods Our study has involved conducting a thorough review of the existing literature to explore the various aspects and several indicators related to the use of AI-enhanced in Scientometrics, Webometrics, and Bibliometrics. Throughout the preparation of this manuscript, we have adhered to the PRISMA-ScR checklist and followed the recommended reporting guidelines for systematic reviews [19]. It is important to note that this manuscript has not been previously registered in PROSPERO or any similar database. We want to emphasize that while PROSPERO registration is typically associated with systematic reviews, we have made a deliberate decision not to register this specific review. This decision is based on the scope of our review, which does not strictly meet the eligibility criteria of PROSPERO, and the practicality within the limitations of our project. We want to assure readers that our literature search and selection process follow rigorous methodology, and our findings are reported transparently, thus in order to address any concerns regarding credibility. Research Questions Research Questions Research Questions "How do the cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research evaluation and impact assessment?" "What advancements have been made in AI-enhanced webometrics and how do they enhance the understanding of web-based information and online user behavior?" "In what ways do the cutting-edge techniques in AI-enhanced bibliometrics revolutionize the analysis and measurement of scholarly publications and their impact?" "How do the cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research evaluation and impact assessment?" "How do the cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research evaluation and impact assessment?" "What advancements have been made in AI-enhanced webometrics and how do they enhance the understanding of web-based information and online user behavior?" "What advancements have been made in AI-enhanced webometrics and how do they enhance the understanding of web-based information and online user behavior?" "In what ways do the cutting-edge techniques in AI-enhanced bibliometrics revolutionize the analysis and measurement of scholarly publications and their impact?" "In what ways do the cutting-edge techniques in AI-enhanced bibliometrics revolutionize the analysis and measurement of scholarly publications and their impact?" • Additionally, we are seeking answers to the following inquiries: "What does the future hold for Scientometrics, Webometrics, and Bibliometrics with AI?" "What are the ethical considerations that need to be taken into account when utilizing AI in Scientometrics, Webometrics, and Bibliometrics?" "What does the future hold for Scientometrics, Webometrics, and Bibliometrics with AI?" "What does the future hold for Scientometrics, Webometrics, and Bibliometrics with AI?" "What are the ethical considerations that need to be taken into account when utilizing AI in Scientometrics, Webometrics, and Bibliometrics?" "What are the ethical considerations that need to be taken into account when utilizing AI in Scientometrics, Webometrics, and Bibliometrics?" Inclusion and Exclusion Criteria Inclusion and Exclusion Criteria Inclusion and Exclusion Criteria During the study selection process, we implemented specific criteria to identify relevant articles from the database. We considered various types of articles, excluding systematic review articles as our aim is to concentrate on original research studies, and meta-analyses as they often have their own distinct inclusion and exclusion criteria that may differ from ours. The selected articles needed to focus on the use of AI to transform the measurement and analysis of scholarly communication, do identify emerging research trends and, evaluate the impact of scientific publications. Consequently, articles solely addressing the analysis of scholarly communication and the impact of scientific publications without any relevance to AI are excluded from the review. Through the application of these criteria, we ensure that the chosen studies directly address the analysis of AI-enhanced techniques in the field of scientometrics, webometrics, and bibliometrics, enabling us to provide a targeted and focused analysis for our research. Databases and Search Method Databases and Search Method Databases and Search Method We have conducted searches in several databases including ProQuest (LISTA & IBSS), EBSCO (LISTA), IEEE Explore, Web of Science, and Scopus to identify relevant studies. The search was limited to articles published between January 1, 2000, and September 2022, in order to encompass the most recent literature related to our research objectives. To ensure a comprehensive search strategy, we utilized a combination of broad search terms and conducted a nested search [20]. The search strategy involved using keywords that were relevant to our research topic, including variations and synonyms to maximize coverage. For instance, in Scopus, our search string included terms such as "AI" OR "Artificial Intelligence" AND "Scientometrics" OR "Webometrics" OR "Bibliometrics" or variations of it. By incorporating these keywords and using Boolean operators to combine them, our aim was to identify articles that focused on the impact, effectiveness, and evaluation of healthcare or smart health technologies. The specific search terms and string may have varied slightly for each database, but they followed a similar structure. Study selection Study selection Study selection The study selection process consisted of two steps to identify articles that met our inclusion criteria. Initially, two independent reviewers (HR.S. and E.H.) screened the titles and abstracts of the identified articles to determine their relevance to our research question and inclusion criteria. Any disagreements between the reviewers were resolved through discussion and consensus. If disagreements persisted, a third reviewer (M.A.) was consulted as an arbitrator. The third reviewer carefully examined the articles in question and provided input to reach a consensus. This approach ensured that the final selection of articles was (and is) based on collective agreement. Study quality appraisal Study quality appraisal Study quality appraisal The quality assessment of the included reviews was conducted by two researchers (HR.S. and E.H.) using the CASP Systematic Review Checklist (Appendix 1). We resolved any disagreements through discussion and reached a consensus on the quality of each study. Coding framework for analyzing the selected articles Coding framework for analyzing the selected articles Coding framework for analyzing the selected articles To ensure a systematic and consistent analysis of the selected articles, a coding framework was developed and applied. The coding framework consisted of several key categories and criteria that guided the analysis process. The following is an overview of the coding framework used: 1. Category 1: Research Methodology 1. Category 1: Research Methodology - Criteria: Identify the research methodology employed in each article (e.g., experimental, survey, case study, etc.). 2. Category 2: AI Applications 2. Category 2: AI Applications - Criteria: Determine the specific applications of artificial intelligence discussed or utilized in each article (e.g., machine learning, natural language processing, data mining, etc.). 3. Category 3: Metrics and Measures 3. Category 3: Metrics and Measures - Criteria: Capture the different metrics and measures used or proposed in the articles for evaluating the impact or effectiveness of the AI applications in scientometrics, webometrics, and bibliometrics. 4. Category 4: Ethical Considerations 4. Category 4: Ethical Considerations - Criteria: Identify any ethical considerations or implications discussed in relation to the AI applications in the selected articles. 5. Category 5: Future Implications 5. Category 5: Future Implications - Criteria: Examine the discussions or predictions regarding the future implications and potential developments related to the use of AI in scientometrics, webometrics, and bibliometrics. During the analysis, two independent researchers coded each article using this framework. Any discrepancies or disagreements in coding were resolved through discussion and consensus. Intercoder reliability was assessed by calculating Cohen's kappa coefficient, which yielded a substantial agreement level of 0.85. By employing this coding framework, we aimed to provide a comprehensive analysis of the selected articles and ensure consistency in the evaluation of relevant aspects. The coding process allowed for a systematic examination of the research methodology, AI applications, metrics, ethical considerations, and future implications discussed in each article. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv
