Artificial Intelligence in Higher Education: A Bibliometric Analysis

Abstract

Today, artificial intelligence plays a very important role in every field. It is inevitable to say that it will play a very efective role in higher education. This study is based on bibliometric studies on the publications indexed in Web of Science on Artificial Intelligence in Higher Education. This study was carried out to determine the most cited authors, keywords, in which journals or conferences it was discussed and in which countries it was studied the most, by performing bibliometric analysis on Artificial Intelligence in Higher Education.For the analysis of these data, the analysis of the publications indexed in Web of Science is based on mapping and bibliometric analysis with VosViewer software. The analysis of the simultaneous occurrence of diferent keywords indicates a particular association between the concepts of artificial intelligence, higher education and performance, while reinforcing the inevitable raise of concern around ethical issues of using such technologies in educational institutions.

Keywords

• eol>Artificial Intelligence
• Higher education
• Bibliometric Analysis

1. Introduction

for which we propose to address the following research questions: (RQ1) determine the most cited authors with respect to "Artificial Intelligence in Higher Education"; (RQ2) which are the most cited keywords used in the literature review; (RQ3) analyze which are the journals or conferences in which (RQ1) and (RQ2) appear.

The usage of AI in higher education has been addressed in several research papers by diferent researchers. Recently, a very interesting approach proposed on using AI and Internet-of-Things in order to address the knowledge management (KM) process in higher education institution [ 3 ]. Diferent AI algorithms are used in order to analyze and classify the knowledge received from the educational process, students and staf. However, the application of AI in the processes used in higher education institutions involve ethical concerns that need to be addressed both internally in the institutions, but also by the large governmental agencies like those of European Union or UNESCO or professional organizations like Institute of Electrical and Electronics Engineers or Association for Computing Machinery [ 4 ]. A possible threat to the ethical usage of AI is considered to be the biased algorithms of AI, especially when these algorithms are used in the grading processes of diferent courses or even in the admission steps. It is argued that such algorithms can have “devastating efects” on the students [ 4 ]. Another concern raised in the cited paper is perspective in which the human educators can be replaced by AI programs. The proposed solutions in [ 4 ] are ranging from a close cooperation of stakeholders to a care adoption of AI in order to protect individuals. Overall, as it was presented in [ 5, 6, 7 ], there is an increase interest into how AI can be used into higher education, based on parameters like accoutnability, traceability, transparency and openness, in those ways that maximize the benefit of the students. In this regard, new, improved tools are developed [ 8 ] in order to measure the degree to which AI can enhance diferent skills of the students when faced to AI-based interaction.

A very notable approach is that from [ 9 ] as it comes as a mean to educate diferent stakeholders from higher education on how to ethically use AI in this sector. The authors base their approach on the fact that AI can be used to personalize the teaching and learning experience of tutors and students. They identify several ways in which the assessment methods can be improved, as wel as the education management can be enhanced by using AI tools. The idea of improved teaching quality by means of AI approaches is addressed in [ 10 ]. More than this, the paper is addressing not only the teaching process, but also the human resource management, as the data collected from the university processes can be used for decision-making models [ 11 ]. Such metrics can be used for developing new possiblities for AI to improve the lecturing process in regard to didactic materials, students’ assessment or providing the tutors with helpfull hints on how to improve their lecture based on the feedback the algorithms is receiving from students [ 12 ].

A notable approach is that from [ 13 ] as it proposes a complete system which is collecting several learning and teaching materials and stores them on a Learning Experience Platform. The collevted materials are both video and audio format. The AI algorithms will process the input data and will ofer the possibility for users (tutors and students) to search according to several criteria. The module also has the possiblity to generate assessment exercises. The idea of improving students’ study plans, but also a physical robots to assist the students are presented in [ 14 ]. Using AI-based robots brings another important problem, that of legal community. From this point of view, in [ 15 ] the questions of legality of AI in higher education, the involvement of legal community into the AI debate etc. It has been proved [ 16 ] that these AI-usage challenges, as well as the use-case for AI in higher education are all part of Education 4.0. In this context, AI is ofering multimodal learning analytics, based on the user data, taking into consideration the user’s stress situation and well-being, everything in a legal and ethical environment [ 17 ].

It can be noted that applying AI in higher education has started back at the end of eighties and currently there are diferent approaches into how this can be done [ 18, 19, 20, 21, 22 ]. Still, a lot of work needs to be done in order to use AI in higher education, but also to research the impact that AI-based methods and tools has upon the stakeholders, mainly tutors and students. The research on using AI in higher education is still concerned alot with the ethics and legality of AI. But, as we have previously presented, several innovative studies are presenting their experience on using AI for the benefit of students and tutors.

2. Method

This study was carried out on 12 July 2023 by selecting "all fields" in Web of Science with the keyword "Artificial Intelligence in Higher Education" 22 studies were reached with the related word. The data were analyzed using the VosViewer software.

While analyzing, it was limited to Citation of authors, Co-occurrence-all keywords, Bibliographic coupling of documents, Bibliographic coupling of sources, Bibliographic coupling of countries. The purpose of the selection of these criteria is to determine which keywords the authors working on Artificial Intelligence in Higher Education use, where they publish and in which countries the authors publish.

3. Findings

3.1. Citation of authors

While performing this analysis, type of analysis and counting methods were selected as citation of authors in VosViewer, in order to address (RQ1) from the research questions proposed for this study. While performing the analysis, the minimum number of documents of an author 2 was selected, while the minimum number of citations of an author 1 of the 70 authors, 21 meet the treshold. 3.2. Co-occurrence-all keywords

In this analysis, type of analysis and counting methods were selected as Co-occurrenceall keywords in VosViewer, in order to address (RQ2). Afterwards, the minimum number of occurrences of a keyword 2 of the 106 keywords, 10 meet the threshold. Number of the keywords selected 10.

3.3. Bibliographic coupling of documents

In this analysis, type of analysis and counting methods in VosViewer were chosen as Bibliographic coupling of documents. Afterwards, the minimum number of citations of a document was set to 1 of the 22 documents, 13 meet the threshold.

3.4. Bibliographic coupling of sources

In this analysis, type of analysis and counting methods in VosViewer were chosen as Bibliographic coupling of sources. Afterwards, the minimum number of documents of a source 1 and the minimum number of citations of a source 1of the 21 sources, 13 meet the thresholds.The results for (RQ3) are summarized in Table 4.

3.5. Bibliographic coupling of countries

In this analysis, type of analysis and counting methods in VosViewer were chosen as Bibliographic coupling of countries. Afterwards, the minimum number of documents of a country 2 and the minimum number of citations of a country 0 of the 18 countries, 6 meet the thresholds.

4. Discussion

The foundations of the presented study consist in the specification of 5 diferent bibliometric analysis on the corpus of data provided by Web of Science, performed by the VosViewer software tool, being respectively: Citation of authors, Co-occurrence-all keywords, Bibliographic coupling of documents, Bibliographic coupling of sources and finally, Bibliographic coupling of countries. We also proposed to address 3 research questions, namely (RQ1) determine the most cited authors with respect to "Artificial Intelligence in Higher Education" from Web of Science; (RQ2) understand which the most cited keywords used in the literature review; (RQ3) analyze which are the journals or conferences in which (RQ1) and (RQ2) appear.

The graphically generated results indicate a particular intensity in collaborations for the Executive Director of the Research Institute for Digital Innovation in Learning (RIDIL) and Professor of Instructional Technology at Old Dominion University, Helen Crompton, the total link strength for “crompton, helen” being 5. From a quantitative analysis regarding the most individually cited authors in the literature, the present study highlights the scientific relevance in the “Artificial Intelligence in Higher Education” domain of Professors Inmaculada Aznar Diaz, Francisco Javier Hinojo-Lucena, Maria Pilar Caceres Recheand Jose Maria Romero Rodriguez from the University of Granada, Associate Professor Kalyan Kumar Chattopadhyay and Professor Sheshadri Chatterjee.

The analysis of the simultaneous occurrence of diferent keywords indicates aparticular association between the concepts of artificial intelligence, higher education and performance in the specified literature, revealing the influencial nature of intelligent systems on the performative results of students in higher education [ 11, 12 ]. A relevant result may be observed in the relatively high total link strength for the “education” keyword, suggesting that the impact of artificially intelligent systems extends beyond education of higher degree, while the presence of “validation” keyword reinforces the inevitable raise of concern around ethical issues of using such technologies in educational institutions [ 4 ].

The analysis of the common source of citations for diferent works individuates a higher number of citations for Professor Francisco Javier Hinojo-Lucena’s “Artificial Intelligence in Higher Education: A Bibliometric Study on its Impact in the Scientific Literature ” [ 20 ] and for Dr.Sheshadri Chatterjee’s “Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling”, indicating also a close relationship between the study of the impact of intelligent systems on higher education and the domain of psychology [ 18 ].

The results of our study indicates the importance of the “Education and Information Technologies” journal, cited also in “A help or a threat to contemporary education. Should students be forced to think and do their tasks independently?” and the “Education Sciences” journal, published by MDPI.The 2021 IEEE Global Engineering Education Conference has also been highly influencial, resulting one of the most cited conferences by the studied scientific literature [ 16 ].

Two countries have significally been contributing to the documentation of the studied domain: India, having 65 citations and Spain having 57, due to impactful works of Professors Inmaculada Aznar Diaz, Francisco Javier Hinojo-Lucena, Maria Pilar Caceres Reche and Jose Maria Romero Rodriguez from the University of Granada and Associate Professor Kalyan Kumar Chattopadhyay and Professor SheshadriChatterjee.In Europe, countries such as Romania and Germany have scored high total link strengths, suggesting the importance of their collaborative work, being graphically observable in the VosViewer analysis’ generated graph.

5. Conclusions and Future Work

References

[1] Y. K. Dwivedi , N. Kshetri , L. Hughes , E. L. Slade , A. Jeyaraj , A. K. Kar , A. M. Baabdullah , A. Koohang , V. Raghavan , M. Ahuja , et al., “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy , International Journal of Information Management 71 ( 2023 ) 102642 .

[2] X. Xie , T. Wang , Artificial intelligence: A help or threat to contemporary education. should students be forced to think and do their tasks independently?, Education and Information Technologies ( 2023 ) 1 - 15 .

[3] D. Galgotia , N. Lakshmi , Development of iot-based methodology for the execution of knowledge management using artificial intelligence in higher education system , Soft Computing ( 2023 ) 1 - 10 .

[4] Z. Slimi , B. V. Carballido , Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global ai ethics policies ., TEM Journal 12 ( 2023 ).

[5] H. Crompton , D. Burke , Artificial intelligence in higher education: the state of the field , International Journal of Educational Technology in Higher Education 20 ( 2023 ) 1 - 22 .

[6] M. Bearman , J. Ryan , R. Ajjawi , Discourses of artificial intelligence in higher education: A critical literature review , Higher Education 86 ( 2023 ) 369 - 385 .

[7] H.-C. Chu , G.-H. Hwang , Y.-F. Tu , K.-H. Yang , Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles , Australasian Journal of Educational Technology 38 ( 2022 ) 22 - 42 .

[8] T. Stamer , G. Essers, J. Steinhäuser , K. Flägel , From summative maas global to formative maas 2.0-a workshop report ., GMS Journal for Medical Education 40 ( 2023 ).

[9] A. Aswin , C. Ariati , S. Kurniawan , Artificial intelligence in higher education: a practical approach , 2022 .

[10] X. Xin , Y. Shu-Jiang , P. Nan , D. ChenXu , L. Dan, Review on a big data-based innovative knowledge teaching evaluation system in universities , Journal of Innovation & Knowledge 7 ( 2022 ) 100197 .

[11] Y. Teng , J. Zhang , T. Sun, Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities , Expert Systems 40 ( 2023 ) e12820 .

[12] A. Wróblewska , J. Jasek , B. Jastrzebski , S. Pawlak , A. Grzywacz , S. A. Cheong , S. C. Tan , T. Trzciński , J. Hołyst , Deep learning for automatic detection of qualitative features of lecturing , in: International Conference on Artificial Intelligence in Education , Springer, 2022 , pp. 698 - 703 .

[13] T. Schmohl , K. Schelling , S. Go , K. J. Thaler , A. Watanabe , Development, implementation and acceptance of an ai-based tutoring system: A research-led methodology ., in: CSEDU (2) , 2022 , pp. 179 - 186 .

[14] N. R. Mosteanu , Machine learning and robotic process automation take higher education one step further , SCIENCE AND TECHNOLOGY 25 ( 2022 ) 92 - 99 .

[15] T. G. Makarov , K. M. Arslanov , E. V. Kobchikova , E. G. Opyhtina , S. V. Barabanova , Legal aspects of using artificial intelligence in higher education , in: International Conference on Interactive Collaborative Learning , Springer, 2021 , pp. 286 - 295 .

[16] M. I. Ciolacu , P. Svasta , Education 4.0: Ai empowers smart blended learning process with biofeedback , in: 2021 IEEE Global Engineering Education Conference (EDUCON) , IEEE, 2021 , pp. 1443 - 1448 .

[17] H. Crompton , D. Song , The potential of artificial intelligence in higher education , Revista virtual Universidad catolica del Norte 62 ( 2021 ).

[18] S. Chatterjee , K. K. Bhattacharjee , Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling , Education and Information Technologies 25 ( 2020 ) 3443 - 3463 .

[19] E. Tundrea , Artificial intelligence in higher education: Challenges and opportunities , INTED2020 Proceedings ( 2020 ) 2041 - 2049 .

[20] F.-J. Hinojo-Lucena , I. Aznar-Díaz , M.-P. Cáceres-Reche , J.-M. Romero-Rodríguez , Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature , Education Sciences 9 ( 2019 ) 51 .

[21] F. Altinay , Developing at a great pace: Studies on artificial intelligence in higher education , 2019 .

[22] N. Ozbey , M. Karakose , A. Ucar , The determination and analysis of factors afecting to student learning by artificial intelligence in higher education , in: 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET) , IEEE, 2016 , pp. 1 - 6 .

[23] M. Stratil , C. Hayball , P. Jarratt , Artificial intelligence in higher education and cbt technology , Educational & Training Technology International 26 ( 1989 ) 215 - 225 .