Artificial Intelligence in Higher Education: A Decade of Research

Insights Through Bibliometric Analysis

F. Sehkar Fayda-Kinik

School of Foreign Languages, Istanbul Technical University, Turkey

Keywords: Artificial Intelligence (AI), Smart Technologies, AI Research, Higher Education, Bibliometrics.

Abstract: This study aims to examine the key research characteristics of artificial intelligence (AI) in higher education

(HE), identify major collaborative networks, and highlight the main AI research trends and topics over the

past decade. Adopted as a bibliometric analysis on AI research in HE between 2014 and 2024, the relevant

literature was retrieved from the Web of Science (WOS) in November 2024. After the publications obtained

from initial screening (n=37,545) on the WOS were eliminated based on the application of the predetermined

inclusion/exclusion criteria, 2,195 eligible documents were analyzed for their main characteristics,

collaborative networks, research trends and topics on VOSviewer. According to the results, a significant

upward trend has been found with a particular acceleration in both publications and citations since 2014.

Collaboration trends clarified distinct clusters of research activity, with Europe, English-speaking countries,

and Latin America forming strong intra- and inter-regional networks. Keyword clustering analysis further

demonstrated research priorities, with core areas such as AI concepts, data analytics, and educational

strategies whereas topics like academic ethics, security, and robotics are still emerging in the field.

1 INTRODUCTION

Artificial intelligence (AI) can be defined as

computing systems capable of engaging in human-

like processes including cognitive functions such as

learning, adapting, synthesizing, problem-solving,

and using data for complex processing tasks (Baker

& Smith, 2019; Popenici et al., 2017). In higher

education (HE), AI applications cover a range of

areas in which academic and administrative functions

can be enhanced. For instance, AI technologies

support personalized learning through adaptive

systems that adjust educational content to student

needs and offer customized feedback via intelligent

tutoring systems (Mehrfar et al., 2024; Zhang, 2023).

Moreover, predictive analytics is used to forecast

student outcomes, which helps institutions make

data-driven decisions to improve student success

(Chu et al., 2022; Murdan & Halkhoree, 2024).

Additionally, AI facilitates administrative efficiency

by automating routine tasks like admissions and

resource management and allows staff to focus on

more complex responsibilities (Rahardjo et al., 2024;

Shimpi, 2024). Furthermore, virtual assistants and

https://orcid.org/0000-0001-6563-4504

chatbots provide instant support and improve

accessibility and engagement for students (Wenge,

2021; Shimpi, 2024). Because of all these

enhancements in the areas affecting organizational

practices, AI technologies have become prominent to

be seriously evaluated in favor of students, faculty,

and staff in HE.

The integration of AI technologies into HE

settings has substantially escalated in recent years. AI

has started to transform multidimensional aspects of

academia including teaching, learning, and

administrative processes (Crompton & Burke, 2023)

along with research activities (Al-Zahrani, 2023). In

other words, AI technologies facilitate institutional

and academic processes including decision-making,

research, and learning experiences for students,

faculty, and staff because of their capabilities to

analyze information and large datasets for overall

academic operations in HE (Téllez et al., 2024; Zahid

et al., 2024). However, AI use brings about a

complicated web of potential opportunities as well as

challenges that necessitate careful consideration and

academic research (Leoste et al., 2021). For example,

ethical concerns are frequently raised about data

Fayda-Kinik, F. S.

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis.

DOI: 10.5220/0013271500003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 2, pages 63-74

ISBN: 978-989-758-746-7; ISSN: 2184-5026

privacy, algorithmic bias, and academic integrity

within the practices in HE (Al Daraai et al., 2024;

Cotton et al., 2024; Ghandour, 2024).

Despite the wide range of inclusive and capacity-

enhancing functions that AI technologies offer for

practices in universities, there are still ongoing risks

and ethical issues with undefined borders related to

the use of AI in HE. A critical evaluation of AI

research in HE is necessary to enhance its

contribution to HE activities, follow the impact of the

latest AI-focused developments in educational

settings as well as determine the boundaries of

problematic areas to provide the necessary guidance

for all the stakeholders. Therefore, this study aims to

examine the key characteristics of AI research in HE,

identify major collaborative networks, and highlight

the main research trends and topics over the past

decade.

2 METHODS

2.1 Research Design

This study was designed as a bibliometric analysis of

AI research in HE between 2014 and 2024.

Bibliometric analysis is a quantitative method used to

evaluate and analyze the literature in a specific field,

and it involves statistical and mathematical

techniques to bibliographic data obtained from

certain databases to uncover patterns and trends in

scientific research (Carlos et al., 2024; Marvi &

Foroudi, 2023). Accordingly, the following research

questions (RQs) were investigated:

RQ1. What are the descriptive characteristics of

AI research in HE over the past decade?

RQ2. What are the major collaborative networks

in AI research in HE over the past decade?

RQ3. What are the main AI research trends and

topics in HE over the past decade?

2.2 Data Collection

To address the RQs with a bibliometric analysis, a

number of steps were carried out to determine the

eligible publications. First, the keywords were

specified for AI (e.g., smart technologies, machine

learning, deep learning, adaptive systems, neural

network, cognitive computing, robotics, intelligent

systems, chatbots, automated systems, algorithmic

systems, intelligent systems, data mining, learning

analytics, predictive analytics, language processing)

and HE (e.g., university, tertiary education, college,

undergraduate, post-graduate). Subsequently, the

search string was formulated using the specified

keywords with their equivalences. The Boolean

operators (e.g., AND, OR) were integrated properly

into the search string.

To identify the AI studies in HE, the database was

selected as the Web of Science (WOS) because it is a

widely recognized and credible source for academic

research involving comprehensive and high-quality

publications with advanced filtering options.

Thereafter, the search string was applied to retrieve

the relevant literature on the WOS in November 2024.

Figure 1: Flowchart of source eligibility.

CSEDU 2025 - 17th International Conference on Computer Supported Education

After initial screening, the inclusion/exclusion (I-

E) criteria were implemented to obtain eligible

studies. In this respect, I-E/1, I-E/2, I-E/3, and I-E/4

were automatically applied on the WOS platform, but

I-E/5 and I-E/6 were manually carried out by the

researcher, as demonstrated in Figure 1.

A systematic process for the identification of

eligible publications was performed to refine the

initial dataset from the WOS (n=37,545).

Accordingly, the first criterion (I-E/1) limited

documents published between 2014 and November

2024 (n=31,995). The second criterion (I-E/2)

included only articles, excluding other document

types (n=21,192). Next, the studies were restricted to

those within specific WOS categories (I-E/3);

namely, education, educational research, and

education scientific disciplines (n=2,524). The

language criterion (I-E/4) further limited the dataset

to the articles written in English (n=2,335).

Subsequently, based on the accessibility criterion (I-

E/5), the documents lacking the necessary

components for bibliometric analysis (e.g., missing

keywords or abstracts) were excluded (n=2,240).

Finally, the content of the publications was checked

(I-E/6) depending on the scope of the research, and

2,195 eligible publications were obtained for further

analysis.

2.3 Data Analysis

To investigate the eligible sources based on the RQs,

VOSviewer was used to perform the analyses

compatible with bibliometrics. Firstly, citation and

impact analyses were carried out to identify the

distribution of the eligible publications (n=2,195)

among countries and institutions along with annual

trends to reveal the descriptive characteristics of AI

research in HE (RQ1). Additionally, frequency

analyses were performed to detect the top 10

countries with the most influential studies and the

highest number of publications. Then, to determine

how researchers and institutions in different regions

cooperate and which regions have stronger

cooperation (RQ2), co-authorship analysis was

conducted, and cooperative networks were created

between authors and institutions, which depicts the

nature of international cooperation in AI research in

HE. Moreover, density analysis was performed to

detect the publication activities of institutions.

Finally, keyword clustering analysis was conducted

along with frequency and proximity analyses of

keywords in the eligible documents to identify the

main AI research trends and topics in HE and how

they have evolved over time (RQ3). Based on the

results, a network map was created for the thematic

distribution of keywords with a density and centrality

map of keyword categories. The visualizations were

generated as an output of VOSviewer, and Phyton

was used in the production of the density and

centrality map.

3 RESULTS

3.1 Descriptive Characteristics of AI

Research in HE

To address RQ1, the descriptive features of 2,195

publications were investigated in terms of the annual

distribution of the documents with their citations, the

top 10 countries contributing to AI research in HE,

the most influential institutions, and the top 10

impactful AI studies. First, the annual distribution of

the eligible articles was analyzed by number as

demonstrated in Figure 2. The distribution of 2,195

AI publications in HE indicated an upward trend in

the last decade from 2014 to 2024. In 2014, 42

documents were published and received 1,920

citations. The number of documents and citations

gradually increased until 2018, when 99 documents

were published and these documents received a total

of 4,040 citations. In 2019, this trend escalated to 152

documents with 6,101 citations. In 2020, a significant

increase was observed with 182 documents receiving

8,095 citations. In the following years, this upward

trend continued in terms of both publications and

citations and reached its peak in 2023 with 423

publications and 20,965 citations. In 2024, even

though the data was limited to the ones published

until November, it was observed that the most studies

and dramatically high impacts were identified in this

year with 563 publications and 30,215 citations.

Subsequently, the top 10 countries having

contributed to AI research in HE in the last decade

were analyzed based on the number of documents and

citation rates. The results are displayed in Figure 3.

Accordingly, the USA contributed to AI research

in HE at the highest rate with 369 studies and 4,498

citations. China ranked second with 274 articles and

2,772 citations. Australia ranked third with 205

studies and 3,291 citations, followed by England with

157 documents and 2,301 citations, and Spain with

123 articles and 1,191 citations. The remaining

countries are listed in the 70-76 publication range:

Canada, Taiwan, South Africa, Turkey, and

Germany. Among these countries, the highest citation

rates were identified in Taiwan and Turkey with

1,368 and 1,256 citations respectively.

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis

Figure 2: Annual distribution of AI research in HE by number and citations over the past decade

Figure 3: Top 10 countries contributing to AI research in HE over the past decade.

Next, the most influential institutions in the field

were investigated in the last decade. Accordingly,

these institutions are presented with their country,

document and citation numbers, and total link

strength in Table 1. The metric of total link strength

was selected to analyze the extent and intensity of

relationships in more depth because it takes into

account not only the number of links an organization

has with other organizations but also the strength of

these links. Alternatively, links could only be used to

show how many different organizations are

connected to one organization. However, this metric

CSEDU 2025 - 17th International Conference on Computer Supported Education

Table 1: Most influential institutions in AI research in HE over the past decade.

Organization Country Documents Citations

Total Link

Strength*

Universit

of Edinbur

h Scotlan

24 1,380 357

Monash Universit

Australia 40 925 321

University of South Australia Australia 18 682 168

Open Universit

Unite

Kingdo

34 786 145

Universit

of S

dne

Australia 19 588 135

Universit

of Bel

rade Serbia 8 515 124

Curtin Universit

Australia 16 423 113

Universida

Carlos III de Madri

Spain 7 177 99

Murdoch University Australia 7 176 83

Pontificia Universida

Católica de Chile Chile 11 161 81

Universit

of Queenslan

Australia 26 275 81

Tallinn Universit

Estonia 5 105 77

Kin

Abdulaziz Universit

Saudi Arabia 10 84 74

Deakin University Australia 13 285 73

University of Mannhei

German

10 285 72

*The total link strength attributes indicate, respectively, the number of links of an item with other items and the total strength

of the links of an item with other items (Van Eck & Waltman, 2022).

does not measure the intensity of links or the strength

of inter-institutional cooperation, which may lead to

overlooking the nature of this cooperation. Therefore,

total link strength was applied to measure the total

strength of the links between organizations. In this

respect, the dimensions in both quantity and quality

for collaborations were assessed.

In the last decade, the leading institution with the

highest citation number in AI studies was found as the

University of Edinburgh in Scotland, with 24

documents, 1,380 citations, and a total link strength

of 357, which indicates its central role in AI research

in HE. Monash University in Australia followed with

40 studies, 925 citations, and a link strength of 321.

The other prominent Australian institutions were

revealed as the University of South Australia,

University of Sydney, Curtin University, Murdoch

University, University of Queensland, and Deakin

University, which emphasize Australia’s significant

contribution to this research area. Open University in

the United Kingdom also played a crucial role with

34 articles and 786 citations. The University of

Belgrade in Serbia, Universidad Carlos III de Madrid

in Spain, Pontificia Universidad Católica de Chile in

Chile, Tallinn University in Estonia, King Abdulaziz

University in Saudi Arabia, and University of

Mannheim in Germany also ranked among the most

influential institutions, each contributing notably

through document output, citation impact, and

collaborative link strength within the academic

community.

Finally, the top 10 influential studies were

detected according to the impact they have created

with their citations. These studies broadly focused on

the impact of AI, specifically ChatGPT, and learning

analytics in HE. Cotton et al. (2024) explored

academic integrity issues related to ChatGPT and led

with 429 citations. Gasevic et al. (2016), with 364

citations, emphasized the need for personalized

learning analytics rather than a one-size-fits-all

approach. Farrokhnia et al. (2024), cited 244 times,

offered a SWOT analysis on ChatGPT’s role in HE.

You (2016) analyzed predictive indicators for online

course success and contributed to the field with 233

citations. Chatterjee and Bhattacharjee (2020)

reviewed AI adoption in HE and received 154

citations. Similar to Cotton et al. (2024), Perkins

(2023), with 152 citations, examined academic

integrity concerns around AI language models in the

post-pandemic period. Pursel et al. (2016), Chan

(2023), Crompton and Burke (2023), and Chan and

Hu (2023) each investigated diverse aspects of AI in

HE, such as student motivation, policy frameworks,

and student perspectives on generative AI. Overall,

all these works highlighted increasing attention to

challenges and opportunities resulting from AI use in

HE.

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis

Figure 4: Country-based collaborative trends in AI research in HE over the past decade.

3.2 Collaborative Networks in AI

Research in HE

The collaborative trends in the last decade were

explored among 2,195 AI studies in HE in terms of

the interactions of countries and institutions on

VOSviewer. Therefore, co-authorship analysis was

conducted to address RQ2. In Figure 4, international

collaborations through AI research in HE are

illustrated with the countries grouped into color-

coded clusters indicating regional partnerships.

Accordingly, it was revealed that the red cluster

consisted mostly of European countries including

Germany, Austria, and Belgium, which reflects

strong intra-European collaborations. The blue

cluster centered on English-speaking countries such

as the USA, England, and Australia, and the USA

served as a major global hub strongly linking with

China. The green cluster featured Latin American and

European nations like Spain, Mexico, and Brazil,

which highlights the transatlantic link in the field.

Finally, the yellow cluster represented regional

collaborations within Asia, where countries are

working closely on AI research in HE.

Subsequently, the networks among the

institutions contributing to AI research in HE were

revealed through a density analysis on VOSviewer.

Figure 5 demonstrates the findings obtained from the

density analysis of institutions having published at

least five papers on AI research in HE in the last

decade. In the map created as a VOSviewer output,

color intensity was used to indicate areas of high

research activity, and brighter areas were represented

by clusters of institutions having high volumes of

publications. The key institutions were identified as

Monash University, the University of Edinburgh,

Tecnológico de Monterrey, and the University of

Queensland for their strong contributions to the field.

Other universities in Australia, the UK, China, and

the USA also formed notable clusters, which

highlights the intensive AI research efforts in HE in

these regions.

3.3 AI Research Trends and Topics in

As a final step, RQ3 was investigated to reveal the AI

research trends and topics in HE. Accordingly,

keyword clustering analysis along with frequency and

proximity analyses of keywords were carried out on

VOSviewer. In this respect, keyword co-occurrence

analysis was conducted on author keywords used at

CSEDU 2025 - 17th International Conference on Computer Supported Education

Figure 5: Institution density map with high-frequency AI studies in HE over the past decade.

least 10 times in AI research in HE. The results are

displayed as a networking map of the keywords in

Figure 6. The map visualizes keywords as nodes, with

connections between them indicating co-occurrence

relationships. Each color represents a distinct

thematic cluster that groups the keywords based on

their usage patterns and associations.

The red cluster focused on foundational AI terms

like “artificial intelligence” and “machine learning”

with the concepts tied to technical applications. The

green cluster emphasized educational methodologies

and specific learning strategies, such as “active

learning” and “collaborative learning”. The purple

cluster included learning analytics, which indicates a

focus on data-driven approaches to understanding

educational outcomes. The yellow cluster represented

the topics in educational technology and online

learning, with terms like “e-learning” and “distance

education”. The blue cluster featured the keywords

linked to student engagement and instructional

practices including “assessment” and “feedback” as

the key concepts.

Next, author keywords in AI research for HE were

categorized into distinct themes with their occurrence

counts representing the frequency (f) of each theme

as listed in Table 2. The most prominent category was

detected as “artificial intelligence and related

concepts” (AlaRC) which appeared 914 times,

including terms like AI, machine learning, deep

learning, and natural language processing. “Data

analytics in education” (DAiE) followed with 630

occurrences, featuring keywords such as data mining

and learning analytics. “Higher education” (HE) was

found another significant category and appeared 447

times. The categories of “types of education” (ToE)

(f=288), which included online learning and e-

learning, and “learning approaches and strategies”

(LAaS) (f=190), with terms like active learning and

collaborative learning, further diversified the research

focus on AI in the last decade.

Finally, keyword clustering analysis was

performed on VOSviewer to reveal the density and

centrality of the distribution of keywords according to

their categories in AI research in HE, as illustrated in

Figure 7.

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis

Figure 6: Keyword co-occurrence map of high-frequency keywords in AI studies in HE over the last decade.

Table 2: Author keywords in thematic groups.

Merged Category f Keywords in the Merged Category

Artificial Intelligence and

Related Concepts

914

Artificial intelligence (AI), AI literacy, artificial neural network, deep learning,

machine learning, Generative AI, large language model/s, natural language

processing, big data, data science, computational thinking, chatbots, decision tree,

programming, random forest, augmented reality

Data Analytics in Education 630

Data analytics, data mining, educational data mining, predictive analytics,

sentimen

analysis, social networ

analysis, tex

mining, learning analytics

Higher Education 447

Higher education

Types of Education 288

Online education, online learning, distance education, distance learning, e-

learning, blended learning, flipped classroom, flipped learning, massive open

online courses (MOOC/s)

Learning Approaches and

Strategies

190

Learning, learning approach/es, learning strategies, active learning, adaptive

learning, collaborative learning, experiential learning, project-based learning,

personalized learning, surface learning

ChatGPT 158

ChatGPT, chatbot

Evaluation in Education 102

Assessment, evaluation, formative assessment, feedback

Learning and Teaching Design 101

Curriculum, curriculum design, learning design, course design, pedagogy, learning

outcomes

Academic Ethics and Security 94

Academic integrity, ethics, privacy, plagiarism, equity

Student Types 88

College students, university students, undergraduate students, medical students,

students

Academic Success and

Performance

Academic achievement, academic performance, student performance, student

success

Social Media and Interaction 78

Social media, student engagement, engagement

Technology and Educational

Technolo

Technology, educational technology, technology acceptance model

Research and Methodology 64

Prediction, classification, research, case study, qualitative research

Learning Management Systems 36

Learning management system/s, Moodle

Robotics and Innovation 36

Educational robotics, robotics

Others 604

Keywords that cannot be assigned to any category

CSEDU 2025 - 17th International Conference on Computer Supported Education

Figure 7: Thematic map of keyword categories by density and centrality*.

AlaRC: Artificial Intelligence and Related Concepts, DAiE: Data

Analytics in Education, HE: Higher Education, ToE: Types of

Education, LAaS: Learning Approaches and Strategies, C:

ChatGPT, EiE: Evaluation in Education, LaTD: Learning and

Teaching Design, AEaS: Academic Ethics and Security, ST:

Student Types, ASaP: Academic Success and Performance, SMaI:

Social Media and Interaction, TaET: Technology and Educational

Technology, RaM: Research and Methodology, LMS: Learning

Management Systems, RaI: Robotics and Innovation, O: Other

Accordingly, the keywords with high centrality

and high density are presented in the upper right

corner of the map as motor themes, and these are the

categories of “artificial intelligence and related

concepts” (AlaRC), “ChatGPT” (C), and “data

analytics in education” (DAiE). These categories

indicated that studies on AI use in education have

been concentrated and widely investigated in the field

over the last decade. “Robotics and innovation” (RaI)

and “academic ethics and security” (AEaS) are

located in the lower left corner of the map with low

centrality and low intensity as emerging themes,

which suggests that AI studies in this field have been

limited or new over the last decade. Particularly, the

concept of “robotics and innovation” (RaI) was

analyzed with its context as depicted in Figure 8.

While “robotics” represents a field associated

with robotic technologies and engineering processes

in general, “educational robotics” emphasizes the

applicability of these technologies in the educational

context and their integration into pedagogical

practices. Both concepts have a strong connection

with technical skills such as STEM, “computational

thinking” and “programming”, which implies that

robotics applications are a critical tool for the

development of digital skills in HE. In addition, the

theme of “educational robotics” is directly associated

with pedagogical concepts such as “pedagogy”,

“curriculum”, and “project-based learning”, which

shows that these technologies are not only used for

knowledge transfer but also support student-centered,

collaborative, and experiential learning processes.

4 DISCUSSION

In this bibliometric review, the key characteristics of

AI research in HE and major collaborative networks

were investigated along with the main research trends

and topics in AI within HE settings. The findings of

this study revealed a significant upward trend in AI

research over the past decade, with a particular

acceleration in both publications and citations since

2014. The growing interest in AI has been proven as

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis

Figure 8: Keyword co-occurrence map of robotics and educational robotics.

a transformative tool in HE, especially after 2018.

Consistently, Al-Zahrani (2023) confirmed its

revolutionary impact on academic research. By 2023,

AI-related publications and citations peaked because

of the rapid advancements in AI technologies like

ChatGPT and learning analytics, which have drawn

substantial academic attention (Farrokhnia et al.,

2024; Gasevic et al., 2016; Perkins, 2023). The yearly

growth has indicated the broadening acceptance and

application of AI in HE (Chan & Hu, 2023); notably,

its potential to reshape educational practices and

outcomes is reflected in academic research in the

field.

The analysis of country-based contributions to AI

research showed that the USA has become the leader

in AI research in HE in the last decade, followed by

China, Australia, England, and Spain. Moreover, high

citation rates were reported in Taiwan and Turkey,

which indicates impactful research despite their fewer

publications compared to the top contributors. This

geographic distribution in AI research revealed the

expertise in specific regions with strong contributions

from both English-speaking and non-English-

speaking countries. Regarding AI integration into HE

systems, each country’s unique educational needs and

policy environments contributed to the depth of AI

research conducted in different contexts (Chan,

2023). Distinct clusters of AI research activity proved

a collaborative global effort including strong intra-

and inter-regional networks in HE. Similarly, Hu et

al. (2020), in their network analysis concerning AI

between 1985 and 2019, confirmed the significant

level of international collaboration in AI research.

AI research priorities were identified as AI

concepts, data analytics, and educational strategies by

the keyword clustering analysis. AI in education is

still predominantly focused on foundational concepts

and data-driven educational improvements

(Crompton & Burke, 2023; Guo et al., 2024; Paek &

Kim, 2021). However, topics like academic ethics,

security, and robotics were detected as emerging

themes in AI research. Consistently, Guo et al.

(2024), in their bibliometric review on AI in

education between 2013 and 2023, identified

educational robots and large data mining as emerging

topics.

5 CONCLUSION

AI technologies have been transforming HE systems

in a groundbreaking and unprecedented way. This

transformation can be realized substantially with AI

research. Emerging AI applications should be

evaluated in a way that will increase the scope and

quality of the functions of universities, and possible

risks resulting from AI tools should be analyzed, and

necessary guidance should be provided beforehand.

In this respect, policymakers and educational

administrators are recommended to reconsider that AI

research should be supported by the development of

global collaborations and the allocation of resources

to these studies.

This study is limited to the research indexed in the

WOS database between 2014 and 2024 even though

this approach ensures a high level of academic rigor

and relevance. The potential publications in the other

platforms can be included in further research to

expand the scope of findings in AI research. Besides,

the selected keywords can be diversified to obtain

more interpretively extensive results; thus, a wider

range of perspectives and emerging trends in AI

research can be captured within HE.

CSEDU 2025 - 17th International Conference on Computer Supported Education

REFERENCES

Al Daraai, S. B., Al Maqrashi, M., Al Zakwani, M., & Al

Shaikh, Z. (2024). Integrating AI in higher education:

Applications, strategies, ethical considerations. In N. Al

Harrasi & M. Salah El Din (Eds.), Utilizing AI for

assessment, grading, and feedback in higher education

(pp. 189-211). IGI Global. https://doi.org/10.4018/979-

8-3693-2145-4.ch008

Al-Zahrani, A. M. (2023). The impact of generative AI

tools on researchers and research: Implications for

academia in higher education. Innovations in Education

and Teaching International, 61(5), 1029-1043.

https://doi.org/10.1080/14703297.2023.2271445

Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted?

Exploring the future of artificial intelligence in schools

and colleges. Retrieved from https://media.nesta.org.

uk/documents/Future_of_AI_and_education_v5_WEB

.pdf

Carlos, M. D., Josue, R. D., & Carla, S. C. (2024).

Bibliometric studies: An option to develop research in

surgery and related disciplines. Revista de Cirugia,

76(2), 147-156. https://doi.org/10.35687/s2452-

454920240021890

Chan, C. K. Y. (2023). A comprehensive AI policy

education framework for university teaching and

learning. International Journal of Educational

Technology in Higher Education, 20(1), 1-25.

https://doi.org/10.1186/s41239-023-00408-3

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on

generative AI: Perceptions, benefits, and challenges in

higher education. International Journal of Educational

Technology in Higher Education, 20(1), 1-18.

https://doi.org/10.1186/s41239-023-00411-8

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of

artificial intelligence in higher education: A

quantitative analysis using structural equation

modelling. Education and Information Technologies,

25(5), 3443-3463. https://doi.org/10.1007/s10639-020-

10159-7

Chu, H.-C., Hwang, G.-H., Tu, Y.-F., & Yang, K.-H.

(2022). 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(3), 22-42.

https://doi.org/10.14742/ajet.7526

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024).

Chatting and cheating: Ensuring academic integrity in

the era of ChatGPT. Innovations in Education and

Teaching International, 61(2), 228–239.

https://doi.org/10.1080/14703297.2023.2190148

Crompton, H., & Burke, D. (2023). Artificial intelligence in

higher education: The state of the field. International

Journal of Educational Technology in Higher

Education, 20(1), 1-22. https://doi.org/10.1186/s4123

9-023-00392-8

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals,

A. (2023). A SWOT analysis of ChatGPT: Implications

for educational practice and research. Innovations in

Education and Teaching International, 61(3), 460-474.

https://doi.org/10.1080/14703297.2023.2195846

Gasevic, D., Dawson, S., Rogers, T., & Gasevic, D. (2016).

Learning analytics should not promote one size fits all:

The effects of instructional conditions in predicting

academic success. Internet and Higher Education, 28,

68-84. https://doi.org/10.1016/j.iheduc.2015.10.002

Ghandour, D. A. (2024). Navigating the Impact of AI

integration in higher education: Ethical frontiers. In N.

Al Harrasi & M. Salah El Din (Eds.), Utilizing AI for

assessment, grading, and feedback in higher education

(pp. 212-233). IGI Global. https://doi.org/

10.4018/979-8-3693-2145-4.ch009

Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial

intelligence in education research during 2013-2023: A

review based on bibliometric analysis. Education and

Information Technologies, 29(13), 16387-16409.

https://doi.org/10.1007/s10639-024-12491-8

Hu, H., Wang, D., & Deng, S. (2020). Global collaboration

in artificial intelligence: Bibliometrics and network

analysis from 1985 to 2019. Journal of Data and

Information Science, 5(4), 86-115. https://doi.org/10.24

78/jdis-2020-0027

Leoste, J., Jõgi, L., Õun, T., Pastor, L., San Martín López,

J., & Grauberg, I. (2021). Perceptions about the future

of integrating emerging technologies into higher

education: The case of robotics with artificial

intelligence. Computers, 10(9). https://doi.org/10.3390/

computers10090110

Marvi, R., & Foroudi, M. M. (2023). Bibliometric analysis:

Main procedure and guidelines. In P. Foroudi & C.

Dennis (Eds.), Researching and analysing business:

Research methods in practice (pp. 1-12).

https://doi.org/10.4324/9781003107774-4

Mehrfar, A., Zolfaghari, Z., Bordbar, A., &

Karimimoghadam, Z. (2024). The evolution of e-

learning towards the emergence of artificial intelligence

(A narrative review). 11th International and the 17th

National Conference on E-Learning and E-Teaching,

ICeLeT 2024 (pp. 1-5). https://doi.org/10.1109/

ICeLeT62507.2024.10493104

Murdan, A. P., & Halkhoree, R. (2024). Integration of

artificial intelligence for educational excellence and

innovation in higher education institutions. 1st

International Conference on Smart Energy Systems and

Artificial Intelligence, SESAI 2024 (pp. 1-6).

https://doi.org/10.1109/SESAI61023.2024.10599402

Paek, S., & Kim, N. (2021). Analysis of worldwide research

trends on the impact of artificial intelligence in

education. Sustainability (Switzerland), 13(14), 1-20.

https://doi.org/10.3390/su13147941

Perkins, M. (2023). Academic integrity considerations of

AI large language models in the post-pandemic era:

ChatGPT and beyond. Journal of University Teaching

& Learning Practice, 20(2), 7-24. https://doi.org/

10.53761/1.20.02.07

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact

of artificial intelligence on teaching and learning

in higher education. Research and Practice in

Artiﬁcial Intelligence in Higher Education: A Decade of Research Insights Through Bibliometric Analysis

Technology Enhanced Learning, 12(22), 1-13.

https://doi.org/10.1186/s41039-017-0062-8

Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., &

Velegol, D. (2016). Understanding MOOC students:

Motivations and behaviours indicative of MOOC

completion. Journal of Computer Assisted Learning,

32(3, SI), 202–217. https://doi.org/10.1111/jcal.12131

Rahardjo, S., Marmoah, S., Saddhono, K., Sarwanto,

Nugraheni, A. S. C., & Nurhasanah, F. (2024). Smart

system with leveraging AI integrating system for well

designed learning system. 2024 4th International

Conference on Advance Computing and Innovative

Technologies in Engineering, ICACITE 2024 (pp. 901-

906).https://doi.org/10.1109/ICACITE60783.2024.106

17214

Shimpi, S. (2024). Exploring possibilities and

apprehensions about application of artificial

intelligence in higher education. In R. Malik, A.

Sharma, & P. Chaudhary (Eds.), Transforming

education with virtual reality (pp. 87-100).

https://doi.org/10.1002/9781394200498.ch6

Téllez, A. R., Ortiz, L. M. F., & Domínguez, F. C. T.

(2024). Artificial intelligence in university administra-

tion: An overview of its uses and applications. Revista

Interamericana de Bibliotecologia, 47(2), 1-12.

https://doi.org/10.17533/udea.rib.v47n2e353620

Wenge, M. (2021). Artificial intelligence-based real-time

communication and AI-multimedia services in higher

education. Journal of Multiple-Valued Logic Soft

Computing, 36(1-3), 231-248. https://www.oldcity pub

lishing.com/journals/mvlsc-home/mvlsc-issue-content

s/mvlsc-volume-36-number-1-3-2021/mvlsc-36-1-3-p-

231-248/

Van Eck, N. J., & Waltman, L. (2022). VOSviewer manual

(Version 1.6.18). Centre for Science and Technology

Studies (CWTS), Leiden University. Retrieved from

https://www.vosviewer.com/documentation/Manual_

VOSviewer_1.6.18.pdf

You, J. W. (2016). Identifying significant indicators using

LMS data to predict course achievement in online

learning. Internet and Higher Education, 29, 23-30.

https://doi.org/10.1016/j.iheduc.2015.11.003

Zahid, R. A., Jauhari, M. I., Surur, M. A. M., Riswandi, F.

N., Ubaidila, S., & Rahman, M. L. (2024). AI-

Integrated CMS for HE system implementation in

structured way. 2024 4th International Conference on

Advance Computing and Innovative Technologies in

Engineering, ICACITE 2024 (pp. 1608-1613).

https://doi.org/10.1109/ICACITE60783.2024.1061670

Zhang, Y. (2023). Construction of a smart classroom for

image processing courses in colleges and universities

based on artificial intelligence: Taking fundamentals of

photoshop as an example. Proceedings of the 3rd IEEE

International Conference on Social Sciences and

Intelligence Management, SSIM 2023, (pp. 84-88).

https://doi.org/10.1109/SSIM59263.2023.10469561

CSEDU 2025 - 17th International Conference on Computer Supported Education