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Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis

Year 2023, Volume: 10 Issue: Special Issue, 208 - 223, 27.12.2023

Nazlı Ruya Taşkın Bedizel

https://doi.org/10.21449/ijate.1369290

Abstract

The rapid evolution of digital technologies and computer sciences is ushering society into a technologically driven future where machines continually advance to meet human needs and enhance their own intelligence. Among these groundbreaking innovations, Artificial Intelligence (AI) is a cornerstone technology with far-reaching implications. This study undertakes a bibliometric review to investigate contemporary AI and assessment topics in education, aiming to delineate its evolving scope. The Web of Science Databases provided the articles for analysis, spanning from 1994 to September 2023. The study seeks to address research questions about prominent publication years, authors, countries, universities, journals, citation topics, and highly cited articles. The study’s findings illuminate the dynamic nature of AI in educational assessment research, with AI firmly establishing itself as a vital component of education. The study underscores global collaboration, anticipates emerging technologies, and highlights pedagogical implications. Prominent trends emphasize machine learning, Chat GPT, and their application in higher education and medical education, affirming AI's transformative potential. Nevertheless, it is essential to acknowledge the limitations of this study, including data currency and the evolving nature of AI in education. Nonetheless, AI applications are poised to remain a prominent concern in educational technology for the foreseeable future, promising innovative solutions and insights.

Keywords

Artificial intelligence (ai) Assessment Bibliometric analysis Education

References

  • Agarwal, A., Durairajanayagam, D., Tatagari, S., Esteves, S., Harlev, A., Henkel, R., Roychoudhury, S., Homa, S., Puchalt, N., Ramasamy, R., Majzoub, A., Ly, K., Tvrda, E., Assidi, M., Kesari, K., Sharma, R., Banihani, S., Ko, E., Abu-Elmagd, M., … Bashiri, A. (2016). Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian Journal of Andrology, 18(2), 296. https://doi.org/10.4103/1008-682x.171582
  • Alam, A., Hasan & Raza, M. (2022). Impact of artificial intelligence (AI) on education: changing paradigms and approaches. Towards Excellence, 281 289. https://doi.org/10.37867/te140127
  • Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020). How to achieve trustworthy artificial intelligence for health. Bulletin of the World Health Organization, 98(4), 257-262. https://doi.org/10.2471/blt.19.237289
  • Baker, R.S., & Inventado, P.S. (2014). Educational data mining and learning analytics. Learning Analytics, 61-75. https://doi.org/10.1007/978-1-4614-3305-7_4
  • Baker, R.S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
  • Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1). Retrieved from https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/718
  • Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A.E., & Rodríguez, M.E. (2021). Artificial intelligence and reflections from educational landscape: a review of AI studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800
  • Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C.M., Romero-Hall, E., Koutropoulos, A., … Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130. https://doi.org/10.5281/zenodo.7636568
  • Braiki, B.A., Harous, S., Zaki, N., & Alnajjar, F. (2020). Artificial intelligence in education and assessment methods. Bulletin of Electrical Engineering and Informatics, 9(5), 1998-2007. https://doi.org/10.11591/eei.v9i5.1984
  • Chai, C.S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
  • Civaner, M.M., Uncu, Y., Bulut, F., Chalil, E.G., & Tatli, A. (2022). Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education, 22(1), 772. https://doi.org/10.1186/s12909-022-03852-3
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135, 758 773. https://doi.org/10.1016/j.jbusres.2021.07.015
  • Durán-Sánchez, A., Del Río-Rama, M. de la C., Álvarez-García, J., & García-Vélez, D.F. (2019). Mapping of scientific coverage on education for entrepreneurship in higher education. Journal of Enterprising Communities: People and Places in the Global Economy, 13(1/2), 84-104. https://doi.org/10.1108/jec-10-2018-0072
  • Erickson, J.A., Botelho, A.F., McAteer, S., Varatharaj, A., & Heffernan, N.T. (2020). The automated grading of student open responses in mathematics. In C. Rensing, & H. Drachsler (Eds.), Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 615–624). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375523
  • Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114. https://doi.org/10.1016/j.ijpe.2015.01.003
  • Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?'. Journal of Computer Assisted Learning, 37(5), 1207-1216. https://doi.org/10.1111/jcal.12577
  • González-Calatayud, M.L., Fernández, C., & Meneses, J. (2019). Learning styles and educational assessment: A systematic review. Frontiers in Psychology, 10, 2381. https://doi.org/10.3389/fpsyg.2019.02381
  • González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: a systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
  • Grüning, D.J. (2022). Synthesis of human and artificial intelligence: review of “how to stay smart in a smart world: why human intelligence still beats algorithms” by Gerd Gigerenzer. Futures & Foresight Science, 4(3-4). https://doi.org/10.1002/ffo2.137
  • Gülmez, D., Özteke, İ., & Gümüş, S. (2021). Overview of Educational Research from Turkey Published in International Journals: A Bibliometric Analysis. Education & Science/Eğitim ve Bilim, 46(206), 1-27. https://doi.org/10.15390/EB.2020.9317
  • Hassanien, A., Darwish, A., & El-Aska, H. (2020). Machine Learning and Data Mining in Aerospace Technology. Springer Nature Switzerland AG: Cham, Switzerland.
  • Janpla, S., & Piriyasurawong, P. (2018). The development of problem-based learning and concept mapping using a block-based programming model to enhance the programming competency of undergraduate students in computer science. TEM Journal, 7(4), 708.
  • Kaya, S. (2023). A bibliometric journey into research trends in curriculum field: Analysis of two journals. International Journal of Assessment Tools in Education, 10(3), 496-506. https://doi.org/10.21449/ijate.1278728
  • Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., Fiedler, D., Strauß, S., Cress, U., Drachsler, H., Neumann, K., & Rummel, N. (2022). Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.981910
  • Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O.M.D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
  • Lancaster, T. (2023). Artificial intelligence, text generation tools and ChatGPT–does digital watermarking offer a solution?. International Journal for Educational Integrity, 19(1), 10. https://doi.org/10.1007/s40979-023-00131-6
  • Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., Lu, G.,Li, S., Liu, T., & Zhai, X. (2023). Artificial general intelligence (AGI) for education. arXiv preprint arXiv:2304.12479. https://doi.org/10.48550/arXiv.2304.12479
  • Li, T., Reigh, E., He, P., & Adah Miller, E. (2023). Can we and should we use artificial intelligence for formative assessment in science? Journal of Research in Science Teaching, 60(6), 1385-1389. https://doi.org/10.1002/tea.21867
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L.B. (2016). Intelligence unleashed: An argument for AI in education. Journal of Computer Assisted Learning, 32(3), 201-210. https://doi.org/10.1111/jcal.12140
  • Naismith, B., Mulcaire, P., & Burstein, J. (2023, July). Automated evaluation of written discourse coherence using GPT-4. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) (pp. 394-403).
  • National Research Council. (2012). A Framework for K-12 Science Education. https://doi.org/10.17226/13165
  • National Research Council. (2013). Next Generation Science Standards: For states, by states. https://doi.org/10.17226/18290
  • Okagbue, E.F., Ezeachikulo, U.P., Nwigwe, E.O., & Juma, A.A. (n.d.). Machine learning and artificial intelligence in education research: a comprehensive overview of 22 years of research indexed in the scopus database. https://doi.org/10.21203/rs.3.rs-1845778/v1
  • Ouyang, F., Dinh, T.A., & Xu, W. (2023). A systematic review of AI-driven educational assessment in stem education. Journal for STEM Education Research, 6(3), 408-426. https://doi.org/10.1007/s41979-023-00112-x
  • Owan, V.J., Abang, K.B., Idika, D.O., Etta, E.O., & Bassey, B.A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428
  • Qu, J., Zhao, Y., & Xie, Y. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), 581 588. https://doi.org/10.1002/sres.2864
  • Saito, T., & Watanobe, Y. (2020). Learning path recommendation system for programming education based on neural networks. International Journal of Distance Education Technologies (IJDET), 18(1), 36-64. https://doi.org/10.4018/IJDET.2020010103
  • Sapci, A.H., & Sapci, H.A. (2020). Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Medical Education, 6(1), e19285. https://doi.org/10.2196/19285
  • Sharma, K., Papamitsiou, Z., & Giannakos, M. (2019). Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach. British Journal of Educational Technology, 50(6), 3004-3031. https://doi.org/10.1111/bjet.12854
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Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis

Year 2023, Volume: 10 Issue: Special Issue, 208 - 223, 27.12.2023

Nazlı Ruya Taşkın Bedizel

https://doi.org/10.21449/ijate.1369290

Abstract

The rapid evolution of digital technologies and computer sciences is ushering society into a technologically driven future where machines continually advance to meet human needs and enhance their own intelligence. Among these groundbreaking innovations, Artificial Intelligence (AI) is a cornerstone technology with far-reaching implications. This study undertakes a bibliometric review to investigate contemporary AI and assessment topics in education, aiming to delineate its evolving scope. The Web of Science Databases provided the articles for analysis, spanning from 1994 to September 2023. The study seeks to address research questions about prominent publication years, authors, countries, universities, journals, citation topics, and highly cited articles. The study’s findings illuminate the dynamic nature of AI in educational assessment research, with AI firmly establishing itself as a vital component of education. The study underscores global collaboration, anticipates emerging technologies, and highlights pedagogical implications. Prominent trends emphasize machine learning, Chat GPT, and their application in higher education and medical education, affirming AI's transformative potential. Nevertheless, it is essential to acknowledge the limitations of this study, including data currency and the evolving nature of AI in education. Nonetheless, AI applications are poised to remain a prominent concern in educational technology for the foreseeable future, promising innovative solutions and insights.

Keywords

Artificial intelligence (ai) Assessment Bibliometric analysis Education

Ethical Statement

The author declares no conflict of interest. This research study complies with research publishing ethics.

References

  • Agarwal, A., Durairajanayagam, D., Tatagari, S., Esteves, S., Harlev, A., Henkel, R., Roychoudhury, S., Homa, S., Puchalt, N., Ramasamy, R., Majzoub, A., Ly, K., Tvrda, E., Assidi, M., Kesari, K., Sharma, R., Banihani, S., Ko, E., Abu-Elmagd, M., … Bashiri, A. (2016). Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian Journal of Andrology, 18(2), 296. https://doi.org/10.4103/1008-682x.171582
  • Alam, A., Hasan & Raza, M. (2022). Impact of artificial intelligence (AI) on education: changing paradigms and approaches. Towards Excellence, 281 289. https://doi.org/10.37867/te140127
  • Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020). How to achieve trustworthy artificial intelligence for health. Bulletin of the World Health Organization, 98(4), 257-262. https://doi.org/10.2471/blt.19.237289
  • Baker, R.S., & Inventado, P.S. (2014). Educational data mining and learning analytics. Learning Analytics, 61-75. https://doi.org/10.1007/978-1-4614-3305-7_4
  • Baker, R.S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
  • Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1). Retrieved from https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/718
  • Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A.E., & Rodríguez, M.E. (2021). Artificial intelligence and reflections from educational landscape: a review of AI studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800
  • Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C.M., Romero-Hall, E., Koutropoulos, A., … Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130. https://doi.org/10.5281/zenodo.7636568
  • Braiki, B.A., Harous, S., Zaki, N., & Alnajjar, F. (2020). Artificial intelligence in education and assessment methods. Bulletin of Electrical Engineering and Informatics, 9(5), 1998-2007. https://doi.org/10.11591/eei.v9i5.1984
  • Chai, C.S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
  • Civaner, M.M., Uncu, Y., Bulut, F., Chalil, E.G., & Tatli, A. (2022). Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education, 22(1), 772. https://doi.org/10.1186/s12909-022-03852-3
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135, 758 773. https://doi.org/10.1016/j.jbusres.2021.07.015
  • Durán-Sánchez, A., Del Río-Rama, M. de la C., Álvarez-García, J., & García-Vélez, D.F. (2019). Mapping of scientific coverage on education for entrepreneurship in higher education. Journal of Enterprising Communities: People and Places in the Global Economy, 13(1/2), 84-104. https://doi.org/10.1108/jec-10-2018-0072
  • Erickson, J.A., Botelho, A.F., McAteer, S., Varatharaj, A., & Heffernan, N.T. (2020). The automated grading of student open responses in mathematics. In C. Rensing, & H. Drachsler (Eds.), Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 615–624). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375523
  • Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114. https://doi.org/10.1016/j.ijpe.2015.01.003
  • Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?'. Journal of Computer Assisted Learning, 37(5), 1207-1216. https://doi.org/10.1111/jcal.12577
  • González-Calatayud, M.L., Fernández, C., & Meneses, J. (2019). Learning styles and educational assessment: A systematic review. Frontiers in Psychology, 10, 2381. https://doi.org/10.3389/fpsyg.2019.02381
  • González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: a systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
  • Grüning, D.J. (2022). Synthesis of human and artificial intelligence: review of “how to stay smart in a smart world: why human intelligence still beats algorithms” by Gerd Gigerenzer. Futures & Foresight Science, 4(3-4). https://doi.org/10.1002/ffo2.137
  • Gülmez, D., Özteke, İ., & Gümüş, S. (2021). Overview of Educational Research from Turkey Published in International Journals: A Bibliometric Analysis. Education & Science/Eğitim ve Bilim, 46(206), 1-27. https://doi.org/10.15390/EB.2020.9317
  • Hassanien, A., Darwish, A., & El-Aska, H. (2020). Machine Learning and Data Mining in Aerospace Technology. Springer Nature Switzerland AG: Cham, Switzerland.
  • Janpla, S., & Piriyasurawong, P. (2018). The development of problem-based learning and concept mapping using a block-based programming model to enhance the programming competency of undergraduate students in computer science. TEM Journal, 7(4), 708.
  • Kaya, S. (2023). A bibliometric journey into research trends in curriculum field: Analysis of two journals. International Journal of Assessment Tools in Education, 10(3), 496-506. https://doi.org/10.21449/ijate.1278728
  • Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., Fiedler, D., Strauß, S., Cress, U., Drachsler, H., Neumann, K., & Rummel, N. (2022). Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.981910
  • Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O.M.D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
  • Lancaster, T. (2023). Artificial intelligence, text generation tools and ChatGPT–does digital watermarking offer a solution?. International Journal for Educational Integrity, 19(1), 10. https://doi.org/10.1007/s40979-023-00131-6
  • Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., Lu, G.,Li, S., Liu, T., & Zhai, X. (2023). Artificial general intelligence (AGI) for education. arXiv preprint arXiv:2304.12479. https://doi.org/10.48550/arXiv.2304.12479
  • Li, T., Reigh, E., He, P., & Adah Miller, E. (2023). Can we and should we use artificial intelligence for formative assessment in science? Journal of Research in Science Teaching, 60(6), 1385-1389. https://doi.org/10.1002/tea.21867
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L.B. (2016). Intelligence unleashed: An argument for AI in education. Journal of Computer Assisted Learning, 32(3), 201-210. https://doi.org/10.1111/jcal.12140
  • Naismith, B., Mulcaire, P., & Burstein, J. (2023, July). Automated evaluation of written discourse coherence using GPT-4. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) (pp. 394-403).
  • National Research Council. (2012). A Framework for K-12 Science Education. https://doi.org/10.17226/13165
  • National Research Council. (2013). Next Generation Science Standards: For states, by states. https://doi.org/10.17226/18290
  • Okagbue, E.F., Ezeachikulo, U.P., Nwigwe, E.O., & Juma, A.A. (n.d.). Machine learning and artificial intelligence in education research: a comprehensive overview of 22 years of research indexed in the scopus database. https://doi.org/10.21203/rs.3.rs-1845778/v1
  • Ouyang, F., Dinh, T.A., & Xu, W. (2023). A systematic review of AI-driven educational assessment in stem education. Journal for STEM Education Research, 6(3), 408-426. https://doi.org/10.1007/s41979-023-00112-x
  • Owan, V.J., Abang, K.B., Idika, D.O., Etta, E.O., & Bassey, B.A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428
  • Qu, J., Zhao, Y., & Xie, Y. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), 581 588. https://doi.org/10.1002/sres.2864
  • Saito, T., & Watanobe, Y. (2020). Learning path recommendation system for programming education based on neural networks. International Journal of Distance Education Technologies (IJDET), 18(1), 36-64. https://doi.org/10.4018/IJDET.2020010103
  • Sapci, A.H., & Sapci, H.A. (2020). Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Medical Education, 6(1), e19285. https://doi.org/10.2196/19285
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There are 53 citations in total.

Details

Primary Language English
Subjects Measurement and Evaluation in Education (Other)
Journal Section Special Issue 2023
Authors

Nazlı Ruya Taşkın Bedizel 0000-0001-6027-719X

Publication Date December 27, 2023
Submission Date September 30, 2023
Published in Issue Year 2023 Volume: 10 Issue: Special Issue

Cite

APA Taşkın Bedizel, N. R. (2023). Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis. International Journal of Assessment Tools in Education, 10(Special Issue), 208-223. https://doi.org/10.21449/ijate.1369290

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