Academic Quality Management System Audit Using Artificial Intelligence Techniques

  • Rodolfo BojorqueEmail author
  • Fernando Pesántez-Avilés
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)


Quality management systems are a challenge for higher education centers. Nowadays, there are different management systems, for instance: quality, environmental, information security, etc. that can be applied over education centers, but to implement all of them is not a guarantee of education quality because the educational process is very complex. However, a few years ago the Quality Management Systems for higher education centers are taking importance especially in Europe and North America, although in Latin America is an unexplored field. Higher education centers quality is a very complex problem because it is difficult to measure the quality since there are a lot of academic processes as enrollment, matriculation, teaching-learning with a lot of stakeholders as students, teachers, authorities even society; in a lot of locations as campuses, buildings, laboratories with different resources. Each process generates a lot of records and documentation. This information has a varied nature and it is present at a structured and no-structured form. In this context, artificial intelligence techniques can help us to analyze and management knowledge. Our work presents a new approach to audit academic information with machine learning and information retrieval. In our experiments, we used information about syllabus, grades, assessments and online content from a Latin American University. We conclude that using artificial intelligence techniques minimize the decision support time, it allows full data analysis instead of a data sample and it finds out patterns never seen in the case study university.


Quality Management Systems Artificial intelligence Audit techniques 


  1. 1.
    Fiore, M., Contò, F.: The Quality Concept, Advances in Dairy Products. Wiley, New York (2017)Google Scholar
  2. 2.
    International Organization for Standardization: ISO 9001:2015 quality management systems-requirements (2015)Google Scholar
  3. 3.
    Pesántez, F.: Indicadores de gestión y calidad en la educación superior: un modelo de evaluación para la Universidad Politécnica Salesiana, Abya-Yala (2011)Google Scholar
  4. 4.
    Tanweer, M., Qadri, M.M.: Quality assurance in higher education: a framework for distance education. JDER J. Distance Educ. Res. 1(1), 6–24 (2016)Google Scholar
  5. 5.
    Kanji, G., Malek, A., Tambi, B.: Total quality management in UK higher education institutions. Total Qual. Manage. 10(1), 129–153 (1999)CrossRefGoogle Scholar
  6. 6.
    Massy, W.F.: Honoring the Trust. Quality and Cost Containment in Higher Education. Anker Publishing, Bolton (2003)Google Scholar
  7. 7.
    Pratasavitskaya, H., Stensaker, B.: Quality management in higher education: towards a better understanding of an emerging field. Qual. Higher Educ. 16(1), 37–50 (2010)CrossRefGoogle Scholar
  8. 8.
    International Organization for Standardization: ISO 9000:2015 quality management systems-fundamentals and vocabulary (2015)Google Scholar
  9. 9.
    Kaplan, A., Haenlein, M.: Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 62(1), 15–25 (2019)CrossRefGoogle Scholar
  10. 10.
    Government Office for Science: Artificial intelligence: opportunities and implications for the future of decision making, 9 November 2016Google Scholar
  11. 11.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mobile Netw. Appl. 32(2), 368–375 (2018)CrossRefGoogle Scholar
  12. 12.
    Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)CrossRefGoogle Scholar
  13. 13.
    Alrajeh, A., Loret, J.: Intrusion detection systems based on artificial intelligence techniques in wireless sensor networks. Int. J. Distrib. Sens. Netw. 9, 351047 (2013)CrossRefGoogle Scholar
  14. 14.
    Idris, N.B., Shanmugam, B.: Artificial intelligence techniques applied to intrusion detection. In: 2005 Annual IEEE India Conference – Indicon, pp. 52–55 (2005)Google Scholar
  15. 15.
    Landau, D.: Artificial intelligence and machine learning: how computers learn. iQ, 17 August 2016. Accessed 7 Dec 2016
  16. 16.
    Information Commissioner’s Office: Big data, artificial intelligence, machine learning and data protection (20170904 Version: 2.2) (2017). Accessed 1 Nov 2018
  17. 17.
    Berry, M., Castellanos, M.: Survey of text mining. Comput. Rev. 5(9), 548 (2004)Google Scholar
  18. 18.
    Chassignol, M., Khoroshavin, A., Klimova, A., Bilyatdinova, A.: Artificial Intelligence trends in education: a narrative overview. In: 7th International Young Scientists Conference on Computational Science, vol. 136, pp. 16–24 (2018)CrossRefGoogle Scholar
  19. 19.
    Omoteso, K.: The application of artificial intelligence in auditing: looking back to the future. Expert Syst. Appl. 39(9), 8490–8495 (2012)CrossRefGoogle Scholar
  20. 20.
    Zerbino, P., Aloini, D., Dulmin, R., Mininno, V.: Process-mining-enabled audit of information systems: methodology and an application. Expert Syst. Appl. 110, 80–92 (2018)CrossRefGoogle Scholar
  21. 21.
    Holland, P., Rae, S., Taylor, P.: Why AI must be included in audits. KPMG (2018). Accessed 1 Dec 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Politécnica SalesianaCuencaEcuador

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