Journal Of Artificial Intelligence Research Articles

Detailing the Stakeholder Theory of Management in the AI World: A Position Paper on Ethical Decision-Making

Ademola, O. E. Professor, American International University West Africa College of Management and Information Technology Banjul, The Gambia E-mail: ademolaeo@p-acc.co.uk ABSTRACT As artificial intelligence (AI) technology continues to proliferate across various industries, the ethical implications of its development and deployment have come under scrutiny. In this position paper, we delve into the application of stakeholder theory in the AI world, with a focus on ethical decision-making processes. Drawing on academic literature and real-world examples, we examine how stakeholder theory can guide organizations and policymakers in navigating the complex ethical landscape of AI technology. By considering the perspectives and interests of various stakeholders, we argue that a stakeholder-centred approach is crucial for ensuring that AI is developed and utilized in a responsible and ethical manner. Keywords: Artificial Intelligence, Stakeholder Theory, Ethics, Decision-Making, Management Keywords: Theoretical Evaluation, Social Media, Entrepreneurial Opportunity, Evaluation, Business, Artificial Intelligence Journal Reference Format: Ademola, O.E. (2024): Detailing the Stakeholder Theory of Management in the AI World: A Position Paper on Ethical Decision-Making. Social Informatics, Business, Politics, L:aw, Environmental Sciences & Technology Journal. Vol. 10, No. 1. Pp 1-8 www.isteams/socialinformaticsjournal. dx.doi.org/10.22624/AIMS/SIJ/V10N1P1

Inaugural Issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR): The Emergence of an Interdisciplinary Nexus

For the inaugural issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR), I am honored to present an editorial that encapsulates the essence and ambition of this cutting-edge publication. IJAIRR emerges at a time when Artificial Intelligence and Robotics (AIR) are not merely technological novelties but fundamental drivers of progress across various scientific and practical domains. This journal aims to be at the forefront of documenting, analyzing, and guiding the interdisciplinary integration of AI, robotics, and fundamental sciences.

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals.

The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community. To examine the gender and country distribution among academic editors in leading computer science and AI journals. This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability. Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329; p = .004). The U.S., the U.K., and China comprised 50% of editors, while Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia had the highest women editor representation per million women population. Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.

Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges

Journal of Engineering EducationEarly View GUEST EDITORIAL Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges Muhsin Menekse, Corresponding Author Muhsin Menekse [email protected] orcid.org/0000-0002-5547-5455 School of Engineering Education, Purdue University, West Lafayette, Indiana, USA Department of Curriculum and Instruction, Purdue University, West Lafayette, Indiana, USA Correspondence Muhsin Menekse, School of Engineering Education, Purdue University, West Lafayette, IN, USA. Email: [email protected]Search for more papers by this author Muhsin Menekse, Corresponding Author Muhsin Menekse [email protected] orcid.org/0000-0002-5547-5455 School of Engineering Education, Purdue University, West Lafayette, Indiana, USA Department of Curriculum and Instruction, Purdue University, West Lafayette, Indiana, USA Correspondence Muhsin Menekse, School of Engineering Education, Purdue University, West Lafayette, IN, USA. Email: [email protected]Search for more papers by this author First published: 20 June 2023 https://doi.org/10.1002/jee.20539Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL REFERENCES Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K–12 settings. AI Ethics, 2, 431– 440. https://doi.org/10.1007/s43681-021-00096-7 Alevin, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning technologies. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 522– 560). Routledge. ISBN: 113883176X. Balakrishnan, B., & Long, C. Y. (2020). An effective self-directed personalized learning environment for engineering students during the COVID-19 pandemic. Advances in Engineering Education, 8(4), 1– 8. Bearman, M., Boud, D., & Ajjawi, R. (2020). New directions for assessment in a digital world. In M. Bearman, P. Dawson, R. Ajjawi, J. Tai, & D. Boud (Eds.), Re-imagining university assessment in a digital world (pp. 7–18). The enabling power of assessment (Vol. 7). Springer. https://doi.org/10.1007/978-3-030-41956-1_2 Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the Protégé effect: Increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334– 352. https://doi.org/10.1007/s10956-009-9180-4 Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, T. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471– 533. https://doi.org/10.1016/S0364-0213(01)00044-1 Chou, C. Y., & Chan, T. W. (2016). Reciprocal tutoring: Design with cognitive load sharing. International Journal of Artificial Intelligence in Education, 26, 512– 535. Dawani, S. (2023). Integrating artificial intelligence into creativity education: Developing a creative problem-solving course for higher education. Creative Studies Graduate Student Master's Projects. https://digitalcommons.buffalostate.edu/creativeprojects/363 Duran, D., & Topping, K. J. (2017). Learning by teaching: Evidence-based strategies to enhance learning in the classroom. Routledge. Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M., Robinson, T. J., Summers, M. F., Werner–Washburn, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(3), es5. https://doi.org/10.1187/cbe.16-01-0038 Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2015). Course MIRROR: enhancing large classroom instructor-student interactions via mobile interfaces and natural language processing. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '15) (pp. 1473– 1478). Association for Computing Machinery. https://doi.org/10.1145/2702613.2732853 Huang, K. (2023). Alarmed by AI chatbots, universities start revamping how they teach. New York Times. https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, E., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., … Kasneci, G. (2023). Chat GPT for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 Koretsky, M. D., Amatore, D., Barnes, C., & Kimura, S. (2008). Enhancement of student learning in experimental design using a virtual laboratory. IEEE Transactions on Education, 51(1), 76– 85. Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty's brain system. International Journal of Artificial Intelligence in Education, 18(3), 181– 208. Luo, W., Fan, X., Menekse, M., Wang, J., & Litman, D. (2015). Enhancing instructor-student and student-student interactions with mobile interfaces and summarization. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (pp. 16– 20, May 31 – June 5, 2015). https://doi.org/10.3115/v1/N15-3004 Menekse, M. (2020). The reflection-informed learning and instruction to improve students' academic success in undergraduate classrooms. The Journal of Experimental Education, 88(2), 183– 199. https://doi.org/10.1080/00220973.2019.1620159 Menekse, M., Anwar, S., & Akdemir, Z. G. (2022). How do different reflection prompts affect engineering students' academic performance and engagement? The Journal of Experimental Education, 90(2), 261– 279. Menekse, M., Anwar, S., & Purzer, S. (2018). Self-Efficacy and mobile learning technologies: A case study of course MIRROR. In C. Hodges (Ed.), Self-efficacy in instructional technology contexts. Springer. https://doi.org/10.1007/978-3-319-99858-9_4 Menekse, M., Stump, G., Krause, S., & Chi, M. T. H. (2013). Differentiated overt learning activities for effective instruction in engineering classrooms. Journal of Engineering Education, 102(3), 346– 374. https://doi.org/10.1002/jee.20021 Miller, R. L., Streveler, R. A., Yang, D., & Santiago Román, A. I. (2011). Identifying and repairing student misconceptions in thermal and transport science: Concept inventories and schema training studies. Chemical Engineering Education, 45(3), 203– 210. Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: five strategies, including prompts (March 17, 2023). Available at SSRN: https://ssrn.com/abstract=4391243 or https://doi.org/10.2139/ssrn.4391243 Nie, J., Yuan, Y., Chao, X., Li, Y., & Lv, L. (2023). In smart classroom: Investigating the relationship between human–computer interaction, cognitive load and academic emotion. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2023.2190257 Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education. https://doi.org/10.1080/03043797.2023.2213169 OpenAI. (2023). GPT-4 technical report. https://arxiv.org/pdf/2303.08774.pdf Reeves, S. M., & Crippen, K. J. (2021). Virtual laboratories in undergraduate science and engineering courses: A systematic review, 2009–2019. Journal of Science Education and Technology, 30, 16– 30. Roscoe, R. D., & Chi, M. T. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions. Review of Educational Research, 77(4), 534– 574. Streveler, R., & Menekse, M. (2017). Taking a closer look at active learning. Journal of Engineering Education, 106(2), 186– 190. https://doi.org/10.1002/jee.20160 Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2023). Smiling women pitching down: Auditing representational and presentational gender biases in image generative AI. arXiv preprint arXiv:2305.10566. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257– 285. U.S. Department of Education. (2023). Artificial intelligence and future of teaching and learning: Insights and recommendations. Office of Educational Technology. https://tech.ed.gov/ai-future-of-teaching-and-learning/ Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147– 177. Walker, E., Rummel, N., & Koedinger, K. R. (2015). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24, 33– 61. https://doi.org/10.1007/s40593-013-0001-9 Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Commentary for the International Journal of Artificial Intelligence in Education Special Issue on K-12 AI Education

In this commentary, I review the articles in the IJAIED Special Issue on K-12 AI Education. The articles offer compelling motivation for early AI education and cover an impressive range of approaches, grade levels, and perspectives. Despite the differences, there is coherence across the articles in terms of the goals to address AI awareness, knowledge, skills, and ethics. Deep consideration has gone into creating developmentally appropriate AI content, which is arguably the greatest challenge for a complex topic like AI. However, as we find in many emerging topics in education, the demand for curricula and lessons has outpaced the capacity of the field to do sufficient empirical research on how kids learn about AI. Evidence for many of the design choices reflected in the proposals put forth in this special issue is still emerging. The authors have done an admirable job of organizing their ideas around principles from the learning sciences and connecting their efforts to more general curriculum design efforts, such as the K12 CS Framework (2016). The next step, which is promoted by all of the authors in the special issue, is to define a research agenda to provide an empirical basis for the design of early AI learning experiences and inform future iterations of the curricula and frameworks proposed.

Inaugural Editorial

It gives me great pleasure to write an editorial note for the first edition of the new journal of Artificial Intelligence and Applications (AIA). Before I begin, I would like to thank our Managing Editor, Mrs. Yu Zhang of Bon View Publishing, Singapore for approaching me with a great idea of launching a new journal under my leadership in the field of computer science. Therefore, I am honored and fortunate to be part of AIA and take responsibility as Editor-in-Chief. Since it is the first joint venture for us, initially, we struggled and worked hard to find innovative ways to reach potential authors such that AIA can receive quality papers across the globe. As a result, in a short period, AIA has attracted many quality submissions from different countries including developing and developed countries. Since the journal scope is AI related research, we encouraged the authors to emphasize emerging areas of artificial intelligence, such as image processing, security, augmented reality, data mining, machine learning, distributed computing and big data analysis, etc. The first issue includes high quality papers submitted from different countries in the areas mentioned above. However, in AIA, the acceptance rate is only 24%. We are determined to bring you a highly respected journal by setting our standard from the very first issue. We are happy to release the first issue in January 2023. For this achievement, we should thank our team led by Mrs. Yu Zhang, Ms. Phoebe, Ms. Lantian, Ms. Ruifang Zhang for their efforts, time, hard work and significant contributions. Today, we see the success because of their support and effort. To take any journal to a high level/standard in terms of quality papers according to the rules stipulated by the web of science, Scopus, the role of the International Advisors, Associate Editors, Editorial board members, Reviewers and Editorial staff of the journal is vital. In order to maintain the standards of AIA, we introduced desk reject by providing sufficient technical comments, which helped authors to improve the quality of their work. There were a number of challenges, including issues pertaining to reviews, such as delays in receiving review reports, poor reviews, finding relevant experts for reviewing and sufficient number of reviews. However, with the help of the tips provided by the members of the advisory board, our editorial office managed effectively in communicating with the authors, reviewers and Editor-in-Chief without any problems. What is very important for the journal is popularity, so it should reach as many potential readers as possible to achieve a global scientific exposure and impact. To achieve such goals, we need to explore new ways of dissemination and reaching authors, as well as readers. At the same time, our journal should find new ways to receive more quality papers on intra-and-inter-disciplinary areas. We believe if the topic is interesting and has scope for the future, it will show great motivation on submission and a loyal readership. I am fortunate to be part of a highly effective team led by Yu Zhang in the editorial office who is instrumental to achieving our targets. The current group of Associate Editors work incredibly well to assess paper quality and to maintain standards. The papers published by Bon View Publishing focus on special domains and publish our papers in time. In addition, our journal has good support from members of the international advisory board, which comprises pioneers in different areas of artificial intelligence. They serve like ambassadors for our journal. Last but not the least, I should thank all our contributors who chose our journal to publish their work. Furthermore, there were many authors that were disappointed due to rejection, but they should be happy with their achievements and the editorial board comments to improve their work.

Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence

Firms with the assets complementary to Artificial Intelligence (AI) have actively conducted AI research and selectively published their results since AI has resurged around 2006. Focusing on the recent AI development, we investigate how and to what extent firms’ deep engagement in the publication of emerging science-related technology can influence the evolution of published knowledge. Using bibliometric analyses applied to the papers in major AI conferences and journals, we find that papers with at least one author affiliated to a firm, and particularly papers with only firm-affiliated author(s), have had higher influence on the formation of published knowledge trajectory than other papers. In addition, papers from firm and non-firm (university and public research institution) collaborations show higher novelty and conventionality than other papers. These findings deepen our understanding of the role of firms in the evolution of emerging science-related technology.

Latest Advances on AI and Robotics Applications

The applications of artificial intelligence and robotics, their associated technologies, and their integration continue to grow and are seen in more and more diverse fields. With these considerations in mind, this issue of the Journal of Artificial Intelligence and Technology presents five state-of-the-art peer-reviewed contributions, ranging from theory to prototyping, and covering several distinct application topics.

Scientific Traveling through IJAIT’s 30 Years

IJAIT was established to cover the area between “Theory” and “Applications” in the general field of Artificial Intelligence (AI). That area was called “Tools” by IJAIT Founder, Prof. Nikolaos Bourbakis. “Tools” refers to algorithms, methods, techniques, languages, methodologies, and architectures. Thus, IJAIT has evolved to a general AI journal, covering almost all AI sub-fields in the above scope. Also, the “Tools” restriction was not a barrier for accepting good application-oriented papers.

Withdrawal Notice: Aiand Robotics: A Changing and Challenging Future of Pharmacy

The article has been withdrawn from the journal The Chinese Journal of Artificial Intelligence due to language inconsistencies and incoherent text. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php Bentham Science Disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

The Case Against Registered Reports

Registered reports have been proposed as a way to move from eye‐catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top‐twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence?

Technologies Supporting Artificial Intelligence and Robotics Application Development

Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car. In the third-generation today, AI and robotics have collaboratively been used in many areas in our society, including industry, business, manufacture, research, and education. There are many challenging problems in developing AI and robotics applications. We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies. In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms. We present examples of such development environments created by both industry and academia. We also selected eight papers in the related areas to celebrate the foundation of this journal.

Jim Greer’s 25-Year Influence on a Research Programme on Open Learner Models

For the special issue of the International Journal of Artificial Intelligence in Education dedicated to the memory of Jim Greer, this paper highlights some of Jim’s extensive and always-timely contributions to the field: from his early AI-focussed research on intelligent tutoring systems, through a variety of applications deployed to support students in university courses, to learning analytics tools for instructional experts and university administrators. A substantial quantity of his work included some aspect of open learner modelling, and/or involved core issues that are also central to open learner modelling. Accordingly, this paper identifies Jim’s profound influence throughout an open learner model research programme.

Domain-General Tutor Authoring with Apprentice Learner Models

Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.

Jim Greer\u2019s and Mary Mark\u2019s Reviews of Evaluation Methods for Adaptive Systems: a Brief Comment about New Goals

Mark and Greer’s (International Journal of Artificial Intelligence in Education, 4(2/3), 129–153, 1993) review was very influential in setting out effective goals and methods for evaluating adaptive educational systems of all kinds. A later review brought the story up to date (Greer, International Journal of Artificial Intelligence in Education, 26(1), 387–392, 2016). The current paper explores a new range of evaluative goals which go beyond the quality of learning outcomes, learning efficiency, transfer, retention, and short-term motivation. While learner satisfaction has been downgraded over the years as a reliable indicator of learning quality, it cannot be wholly ignored in terms of wider issues such as the learner’s developing metacognitive and meta-affective insight, regulatory competence and longer-term motivation. These factors lead on to such evaluable issues as the learner’s appetite for further learning of the kind just experienced as well as for learning in general. The rise in the use of data analytics and the increasing use of AIED and computer-based learning systems in schools and universities has led to the development of orchestration systems to assist the teacher to manage their students using such systems. Orchestration systems raise new kinds of evaluation goal, such as the balance of activity, cooperation and agency between the human teacher and the adaptive systems, as well as between the learner, the systems, the teacher and, indeed, other learners. Further evaluable goals include the degree to which the teacher is alerted to the learning difficulties of the learners, the degree to which the teacher’s scarce and valuable time is being used efficiently, and the degree to which the orchestration system can be used as a reflective device for teachers to examine their own practice.

A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019

To analyze the current research status and trends of the artificial intelligence in education field, we applied bibliometric methods to examine the articles published in one of the representative journals of the field, <em>International Journal of Artificial Intelligence in Education, </em>from 2015 to 2019. We analyzed 135 articles retrieved from the Web of Science database and examined prolific countries, collaboration networks, prolific authors, keywords, and the citations the articles received. Through examining keywords, we found that the authors largely focused on students and learning. Through examining prolific authors and countries, we found active publication of corresponding authors from United States, United Kingdom, Canada, and Germany. We found international collaboration among some researchers and institutions, such as strong collaboration network between United States and Canada. We suggest reinforcement in building more widespread international partnership and expanding collaboration network by including diverse institutions. International collaboration and expanded institutional network can improve research by incorporating various perspectives and expertise.

Correction to: International Journal of Artificial Intelligence in Education , Volume 29, Number 4, December 2019

Correction to: International Journal of Artificial Intelligence in Education , Volume 29, Number 4, December 2019

The Artificial Intelligence Journal Club (#RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative

The Artificial Intelligence Journal Club (#RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and society in the future. By means of bibliometric method, the study aims at providing an overview on the research tendency and the most concerned topics of AI during the past decade. The database of Web of Science was chosen and the articles published in AI journals were retrieved. Top 10% of the yearly high-citation articles (12,301 articles) published since the year of 2008 were selected as sampling articles for the analysis. The bibliographic records were used for the overall analysis, and the core keywords were studied and classified into three categories (algorithm, general technology and application technology) for topics analysis. As results, number of articles in AI by year and country, the country collaboration and well-known institutes and researchers in AI were presented. Also we proposed and concluded the five most concerned topics, which are perception intelligence (1st), human mind simulated intelligence (2nd), classical model based machine learning (3rd), bio-inspired intelligence (4th), and big-data based intelligence (5th). It is the authors’ wish that the study were helpful for researchers to have an overall grasp of the recent status of AI development.

Topological Properties of Four-Layered Neural Networks

Abstract A topological property or index of a network is a numeric number which characterises the whole structure of the underlying network. It is used to predict the certain changes in the bio, chemical and physical activities of the networks. The 4-layered probabilistic neural networks are more general than the 3-layered probabilistic neural networks. Javaid and Cao [Neural Comput. and Applic., DOI 10.1007/s00521-017-2972-1] and Liu et al. [Journal of Artificial Intelligence and Soft Computing Research, 8(2018), 225-266] studied the certain degree and distance based topological indices (TI’s) of the 3-layered probabilistic neural networks. In this paper, we extend this study to the 4-layered probabilistic neural networks and compute the certain degree-based TI’s. In the end, a comparison between all the computed indices is included and it is also proved that the TI’s of the 4-layered probabilistic neural networks are better being strictly greater than the 3-layered probabilistic neural networks.