Measuring Artificial Intelligence Integration in Higher Education: A Bibliometric Analysis of Quantitative Studies

Hatice Çifçi; Mehmet Altuğ Şahin; Ibrahim Cifci; Gurel Cetin

Makale detayları

Measuring Artificial Intelligence Integration in Higher Education: A Bibliometric Analysis of Quantitative Studies

Yazarlar:

Hatice Çifçi | Mehmet Altuğ Şahin | Ibrahim Cifci | Gurel Cetin

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

Journal of Data Applications Issue 3, pp.33-62

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001340842

pISSN:

2980-3357

Anahtar kelimeler:

Özet:

 This study explores the current state of Artificial Intelligence AI adoption in higher education, evaluating its scope via bibliometric methods. The research builds upon the knowledge acquired from quantitative studies and establishes guidance for future studies. A total of 24 publications from the combined database of Scupos and Web of Science WOS were collected and used as the resource for the bibliometric analysis. The bibliometric analysis using Biblioshiny identified seven indicators, including annual publications, the top 10 contributing countries, the most relevant sources, a thematic map, motor and niche themes, emerging or declining themes, and basic themes. In addition, for the keyword analysis, the authors used the VOSviewer, which identified three clusters: pedagogy, AI tools, and ethics. As a result, the paper provides an improved understanding of AI adoption in education and a framework that includes both students and educators perspectives on the measures and quantitative research in AI utilization in education. Such knowledge not only provides significant information on the current state of literature and trends but also implications for educators, administrators, and educational technology EduTech suppliers.

 This study explores the current state of Artificial Intelligence AI adoption in higher education, evaluating its scope via bibliometric methods. The research builds upon the knowledge acquired from quantitative studies and establishes guidance for future studies. A total of 24 publications from the combined database of Scupos and Web of Science WOS were collected and used as the resource for the bibliometric analysis. The bibliometric analysis using Biblioshiny identified seven indicators, including annual publications, the top 10 contributing countries, the most relevant sources, a thematic map, motor and niche themes, emerging or declining themes, and basic themes. In addition, for the keyword analysis, the authors used the VOSviewer, which identified three clusters: pedagogy, AI tools, and ethics. As a result, the paper provides an improved understanding of AI adoption in education and a framework that includes both students and educators perspectives on the measures and quantitative research in AI utilization in education. Such knowledge not only provides significant information on the current state of literature and trends but also implications for educators, administrators, and educational technology EduTech suppliers.

 This study explores the current state of Artificial Intelligence AI adoption in higher education, evaluating its scope via bibliometric methods. The research builds upon the knowledge acquired from quantitative studies and establishes guidance for future studies. A total of 24 publications from the combined database of Scupos and Web of Science WOS were collected and used as the resource for the bibliometric analysis. The bibliometric analysis using Biblioshiny identified seven indicators, including annual publications, the top 10 contributing countries, the most relevant sources, a thematic map, motor and niche themes, emerging or declining themes, and basic themes. In addition, for the keyword analysis, the authors used the VOSviewer, which identified three clusters: pedagogy, AI tools, and ethics. As a result, the paper provides an improved understanding of AI adoption in education and a framework that includes both students and educators perspectives on the measures and quantitative research in AI utilization in education. Such knowledge not only provides significant information on the current state of literature and trends but also implications for educators, administrators, and educational technology EduTech suppliers.

 This study explores the current state of Artificial Intelligence AI adoption in higher education, evaluating its scope via bibliometric methods. The research builds upon the knowledge acquired from quantitative studies and establishes guidance for future studies. A total of 24 publications from the combined database of Scupos and Web of Science WOS were collected and used as the resource for the bibliometric analysis. The bibliometric analysis using Biblioshiny identified seven indicators, including annual publications, the top 10 contributing countries, the most relevant sources, a thematic map, motor and niche themes, emerging or declining themes, and basic themes. In addition, for the keyword analysis, the authors used the VOSviewer, which identified three clusters: pedagogy, AI tools, and ethics. As a result, the paper provides an improved understanding of AI adoption in education and a framework that includes both students and educators perspectives on the measures and quantitative research in AI utilization in education. Such knowledge not only provides significant information on the current state of literature and trends but also implications for educators, administrators, and educational technology EduTech suppliers.

Makale detayları

Measuring Artificial Intelligence Integration in Higher Education: A Bibliometric Analysis of Quantitative Studies

Yazarlar:

Hatice Çifçi | Mehmet Altuğ Şahin | Ibrahim Cifci | Gurel Cetin

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

Journal of Data Applications Issue 3, pp.33-62

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001340842

pISSN:

2980-3357

AI Adoption

Education

Students

Educators

Bibliometric Analysis

Sayı

Makale detayları

Measuring Artificial Intelligence Integration in Higher Education: A Bibliometric Analysis of Quantitative Studies

Yazarlar: Hatice Çifçi | Mehmet Altuğ Şahin | Ibrahim Cifci | Gurel Cetin

Yayıncı: Istanbul University

Ana Başlıklar: Ekonometri (Diğer)

Dergi: Journal of Data Applications Issue 3, pp.33-62

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001340842

pISSN: 2980-3357

AI Adoption

Education

Students

Educators

Bibliometric Analysis

Sayı

Yazarlar:

Hatice Çifçi | Mehmet Altuğ Şahin | Ibrahim Cifci | Gurel Cetin

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

Journal of Data Applications Issue 3, pp.33-62

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001340842

pISSN:

2980-3357