8,309
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach

ORCID Icon &
Article: 2261730 | Received 07 Feb 2023, Accepted 12 Sep 2023, Published online: 18 Oct 2023

References

  • Abbas, J., J. Aman, M. Nurunnabi, and S. Bano. 2019. The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability 11 (6):1683–2358. doi:10.3390/su11061683.
  • Akgun, S., and C. Greenhow. 2022. Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics 2 (3):431–40. doi:10.1007/s43681-021-00096-7.
  • Aldowah, H., H. Al-Samarraie, and W. Fauzy. 2019. Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics 37:13–49. doi:10.1016/j.tele.2019.01.007.
  • Alexander, B., K. Ashford-Rowe, N. Barajas-Murph, G. Dobbin, J. Knott, M. McCormack, J. Pomerantz, R. Seilhamer, and N. Weber. (2019, 4). EDUCAUSE horizon report: 2019 higher education edition. Louisville: EDUCAUSE.
  • Azer, S. A., A. P. Guerrero, and A. Walsh. 2013. Enhancing learning approaches: Practical tips for students and teachers. Medical Teacher 35 (6):433–43. doi:10.3109/0142159X.2013.775413.
  • Bahadır, E. 2016. Using neural network and logistic regression analysis to predict prospective mathematics teachers’ academic success upon entering graduate education. Educational Sciences Theory & Practice 16 (3):943–64.
  • Bertoli-Barsotti, L., and T. Lando. 2017. A theoretical model of the relationship between the h-index and other simple citation indicators. Scientometrics 111 (3):1415–48. doi:10.1007/s11192-017-2351-9.
  • Bhardwaj, D. 2019. Artificial intelligence: Patient care and health professional’s education. Journal of Clinical and Diagnostic Research 13 (1):1–2. doi:10.7860/JCDR/2019/38035.12453.
  • Blei, D. M. 2012. Probabilistic topic models. Communications of the ACM 55 (4):77–84. doi:10.1145/2133806.2133826.
  • Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993–1022.
  • Bornmann, L., and H. D. Daniel. 2007. What do we know about the h index? Journal of the American Society for Information Science and Technology 58 (9):1381–85. doi:10.1002/asi.20609.
  • Bowdre, P. 2020. The use of predictive analytics to shift the culture of academic advising toward a focus on student success. Journal of Education & Social Policy 7 (3):22–28. doi:10.30845/jesp.v7n3p3.
  • Chatterjee, S., and K. K. Bhattacharjee. 2020. Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies 25 (5):3443–63. doi:10.1007/s10639-020-10159-7.
  • Chaudhry, M. A., and E. Kazim. 2021. Artificial intelligence in education (AIEd): A high-level academic and industry note. AI and Ethics 2 (1):1–9. doi:10.1007/s43681-021-00074-z.
  • Christie, M., and E. de Graaff. 2017. The philosophical and pedagogical underpinnings of active learning in engineering education. European Journal of Engineering Education 42 (1):5–16. doi:10.1080/03043797.2016.1254160.
  • Colchester, K., H. Hagras, D. Alghazzawi, and G. Aldabbagh. 2017. A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research 7 (1):47–64. doi:10.1515/jaiscr-2017-0004.
  • Daniel, B. 2019. Big data and data science: A critical review of issues for educational research. British Journal of Educational Technology 50 (1):101–13. doi:10.1111/bjet.12595.
  • Daniel, B. K. 2015. Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology 46 (5):904–20. doi:10.1111/bjet.12230.
  • Daws, R. (2019, November 1). ABI research: USA reclaims the top spot from China for AI investments. (ABI research). Retrieved April 1, 2021, https://artificialintelligence-news.com/2019/11/01/abi-research-usa-reclaims-top-china-ai-investments/
  • De Lange, C. 2015. Welcome to the bionic dawn. New Scientist 227 (3032):24–25. doi:10.1016/S0262-4079(15)30881-2.
  • Durieux, V., and P. A. Gevenois. 2010. Bibliometric indicators: Quality measurements of scientific publications. Radiology 255 (2):342–51. doi:10.1148/radiol.09090626.
  • Elbadrawy, A., and G. Karypis (2016). Domain-aware grade prediction and top-n course recommendation. Proceedings of the 10th ACM Conference on Recommender Systems (pp. 183–90). Coimbra: ACM.
  • Ellis, L. 2019. Artificial intelligence for precision education in radiology – experiences in radiology teaching from a UK foundation doctor. The British Journal of Radiology 92 (1104):1–2. doi:10.1259/bjr.20190779.
  • Gobert, J., P. Sao, R. Baker, E. Toto, and O. Montalvo. 2012. Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within microworlds. The Journal of Educational Data Mining 4 (1):104–43.
  • Greenhow, C., S. Galvin, D. Brandon, and E. Askari. 2020. A decade of research on K–12 teaching and teacher learning with social media: Insights on the state of the field. Teachers College Record: The Voice of Scholarship in Education 122 (6):1–7. doi:10.1177/016146812012200602.
  • Hinojo-Lucena, F.-J., I. Aznar-Díaz, M.-P. Cáceres-Reche, and J.-M. Romero-Rodríguez . 2019. Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences 9 (1):51–59. doi:10.3390/educsci9010051.
  • Hirsch, J. E. 2005. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences 102 (46):16569–72. doi:10.1073/pnas.0507655102.
  • Holmes, W., D. Bektik, D. Whitelock, B. Woolf, C. Rosé, R. Martínez-Maldonado, H. Hoppe, R. Luckin, M. Mavrikis, and K. Porayska-Pomsta. 2018. Ethics in AIED: Who cares? In Artificial intelligence in education, ed. Juan Manuel Trujillo Torres, 551–53. Cham: Springer International Publishing.
  • Huang, A., O. H. Lu, C. Yin, S. Yang, and S. J. H. Yang. 2020. Predicting students’ academic performance by using educational big data and learning analytics: Evaluation of classification methods and learning logs. Interactive Learning Environments 28 (2):206–30. doi:10.1080/10494820.2019.1636086.
  • Jordan, M., and T. Mitchell. 2015. Machine learning: Trends, perspectives, and prospects. Science 349 (6245):255–60. doi:10.1126/science.aaa8415.
  • Khosravi, H., S. B. Shum, G. Chen, C. Conati, Y. S. Tsai, J. Kay, S. Knight, R. Martinez-Maldonado, S. Sadiq, and D. Gašević. 2022. Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence 3:1–22. doi:10.1016/j.caeai.2022.100074.
  • Klašnja-Milicevic, A., M. Ivanovic, and Z. Budimac. 2017. Data science in education: Big data and learning analytics. Computer Applications in Engineering Education 25 (6):1066–78. doi:10.1002/cae.21844.
  • Lang, C., G. Siemens, A. Wise, D. Gasevic, C. Lang, G. Siemens, A. Wise, and D. Gasevic. 2017. Handbook of learning analytics. New York: Society for Learning Analytics Research (SoLAR). doi:10.18608/hla17.
  • Laurillard, D. 2013. Rethinking university teaching: A conversational framework for the effective use of learning technologies. London: Routledge.
  • Lazer, D., R. Kennedy, G. King, and A. Vespignani. 2014. The parable of Google flu: Traps in big data analysis. Science 243 (6176):1203–05. doi:10.1126/science.1248506.
  • Lu, Y. 2019. Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics 6 (1):1–29. doi:10.1080/23270012.2019.1570365.
  • Luan, H., P. Geczy, H. Lai, J. Gobert, S. Yang, H. Ogata, J. Baltes, R. Guerra, P. Li, and C.-C. Tsai. 2020. Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology 11:1–11. doi:10.3389/fpsyg.2020.580820.
  • Lu, H., Y. Li, M. Chen, H. Kim, and S. Serikawa. 2018. Brain intelligence: Go beyond artificial intelligence. Mobile Networks & Applications 3 (2):368–75. doi:10.1007/s11036-017-0932-8.
  • Maphosa, V. 2023. Artificial intelligence and state power. In 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp.1–5), IEEE. Durban, South Africa. https://ieeexplore.ieee.org/abstract/document/10220459
  • Maphosa, M., W. Doorsamy, and B. S. Paul. 2020. A review of recommender systems for choosing elective courses. International Journal of Advanced Computer Science & Applications 11 (9):287–95. doi:10.14569/IJACSA.2020.0110933.
  • Maphosa, M., and V. Maphosa. 2020. Educational data mining in higher education in sub-saharan Africa: A systematic literature review and research agenda. In Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications (pp.1–7), ACM, Mauritius. doi:10.1145/3415088.3415096.
  • Maphosa, V., and M. Maphosa (2021). The trajectory of artificial intelligence research in higher education: A bibliometric analysis and visualisation. International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1–7). Durban: IEEE.
  • Mislevy, R., D. Yan, J. Gobert, and P. Sao. 2020. Automated scoring in intelligent tutoring systems. In Handbook of automated scoring, ed. R. A. Yan D, 403–22. London: Chapman and Hall/CRC. doi:10.1201/9781351264808-22.
  • Moro, S., P. Cortez, and P. Rita. 2015. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications 42 (3):1314–24.
  • Mou, X. (2019, September). Artificial intelligence: Investment trends and selected industry uses. Retrieved 2 11, 2021, from EMCompass-Note-71-AI-Investment-Trends: https://www.ifc.org/wps/wcm/connect/7898d957-69b5-4727-9226-277e8ae28711/EMCompass-Note-71-AI-Investment-Trends.pdf?MOD=AJPERES&CVID=mR5Jvd6
  • Naqvi, A. 2020. Artificial intelligence for audit, forensic accounting, and valuation: A strategic perspective. Toronto: Wiley. doi:10.1002/9781119601906.
  • Page, M. J., J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, and D. Moher. 2021. Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement. Journal of Clinical Epidemiology 134 (1):103–12. doi:10.1016/j.jclinepi.2021.02.003.
  • Pardo, A., and G. Siemens. 2014. Ethical and privacy principles for learning analytics. British Journal of Educational Technology 45 (3):438–50. doi:10.1111/bjet.12152.
  • Popenici, S., and S. Kerr. 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12 (1):1–13. doi:10.1186/s41039-017-0062-8.
  • Regan, P., and J. Jesse. 2019. Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology 21 (3):167–79. doi:10.1007/s10676-018-9492-2.
  • Riahi, Y., and S. Riahi. 2018. Big data and big data analytics: Concepts, types and technologies. International Journal of Research and Engineering 5 (9):524–28. doi:10.21276/ijre.2018.5.9.5.
  • Roll, I., and R. Wylie. 2016. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education 26 (2):582–99. doi:10.1007/s40593-016-0110-3.
  • Saravanakumar, N. 2019. Implementation of artificial intelligence in imparting education and evaluating student performance. Journal of Artificial Intelligence and Capsule Networks 1 (1):1–9. doi:10.36548/jaicn.2019.1.001.
  • Seo, K., J. Tang, I. Roll, S. Fels, and D. Yoon. 2021. The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education 18 (1):54–63. doi:10.1186/s41239-021-00292-9.
  • Sievert, C., and K. Shirley (2014). Ldavis: A method for visualizing and interpreting topics. Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63–70). Maryland: Association for Computational Linguistics.
  • Smutny, P., and P. Schreiberova. 2020. Chatbots for learning: A review of educational chatbots for the Facebook messenger. Computers & Education 151:1–11. doi:10.1016/j.compedu.2020.103862.
  • Steele, G. 2018. Student success: Academic advising, student learning data, and technology. New Directions for Higher Education 2018 (184):59–68. doi:10.1002/he.20303.
  • Thai-Nghe, N., L. Drumond, T. Horváth, A. Nanopoulos, and L. Schmidt-Thieme (2011). Matrix and tensor factorization for predicting student performance. Proceedings of the 3rd International Conference on Computer Supported Education. 1, pp. 69–78. Noordwijkerhout: CSEDU.
  • Tsai, S., C. Chen, Y. Shiao, J. Ciou, and T. Wu. 2020. Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education 17 (1):1–13. doi:10.1186/s41239-020-00186-2.
  • van Eck, N., and L. Waltman. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 (2):523–38. doi:10.1007/s11192-009-0146-3.
  • Williamson, B. 2019. Brain data: Scanning, scraping and sculpting the plastic learning brain through neurotechnology. Postdigital Science & Education 1 (1):65–86. doi:10.1007/s42438-018-0008-5.
  • Xu, B. 2021. Artificial intelligence teaching system and data processing method based on big data. Complexity 2021:1–11. doi:10.1155/2021/1961061.
  • Yu, Z. 2020. Visualizing artificial intelligence used in education over two decades. Journal of Information Technology Research 13 (4):32–46. doi:10.4018/JITR.2020100103.
  • Zawacki-Richter, O., M. Marín, V. Bond, and F. Gouverneur. 2019. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education 16 (1):1–27. doi:10.1186/s41239-019-0171-0.
  • Zemel, R., Y. Wu, K. Swersky, T. Pitassi, and C. Dwork (2013). Learning fair representations. International Conference on Machine Learning. 28, pp. 325–33. Atlanta: PMLR.
  • Zhang, K., and A. B. Aslan. 2021. AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence 2:1–11. doi:10.1016/j.caeai.2021.100025.
  • Zhang, Y., Y. Yun, R. An, J. Cui, H. Dai, and X. Shang. 2021. Educational data mining techniques for student performance prediction: Method review and comparison analysis. Frontiers in Psychology 12:1–19. doi:10.3389/fpsyg.2021.698490.
  • Zupic, I., and T. Čater. 2015. Bibliometric methods in management and organization. Organizational Research Methods 18 (3):429–72. doi:10.1177/1094428114562629.