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University Students’ Research on Artificial Intelligence and Knowledge Management. A Review and Report of Multi-case Studies

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Artificial Intelligence for Knowledge Management (AI4KM 2021)

Abstract

Leading technologies are very attractive for students preparing their theses as the completion of their studies. Such an orientation of students connected with professional experiences seems to be a crucial motivator in the research in the management and business areas where these technologies condition the development of professional activities. The goal of the paper is the analysis of students’ thesis topics defended in the last 10 years in business informatics and computer science in two selected universities in Poland. Our study relies on a detailed review and analysis of qualitative data, obtained from a literature review and multi-case-study research. In the case of the artificial intelligence domain, we have identified six areas of research, namely: general AI, machine learning (ML), natural language processing (NLP), artificial neural networks (ANNs), expert systems, and hybrids. In the case of the knowledge management domain, we have recognized eleven areas of research, regarding the following sectors: e-government, technology, space exploration, social media, manufacturing, healthcare, finance, entertainment, education, e-commerce, and business. Future research will be directed toward extending the scope by including other regions and universities as well as identifying and analyzing students’ motivational factors, associated with research pro jects and higher education.

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References

  1. Giuntini, F.T., et al.: How do i feel? Identifying emotional expressions on facebook reactions using clustering mechanism. IEEE Access 7, 53909–53921 (2019)

    Article  Google Scholar 

  2. Owoc, M.L., Weichbroth, P.: Dynamical aspects of knowledge evolution. In: Mercier-Laurent, E., Boulanger, Danielle (eds.) AI4KM 2017. IAICT, vol. 571, pp. 52–65. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29904-0_5

    Chapter  Google Scholar 

  3. Weichbroth, P., Brodnicki, K.: The lemniscate knowledge flow model. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1217–1220. IEEE (2017)

    Google Scholar 

  4. PSSI: Polish artificial intelligence society (2021). https://pssi.org.pl/en:membership

  5. NTIE: Naukowe Towarzystwo Informatyki Ekonomicznej (2021). http://sartosfera.pl/ntie/

  6. Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, pp. 1–10 (2014)

    Google Scholar 

  7. Gagnon, Y.C.: The Case Study as Research Method: A Practical Handbook. PUQ (2010)

    Google Scholar 

  8. Anyoha, R.: The history of artificial intelligence (2017). http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

  9. Ginsberg, M.: Essentials of artificial intelligence. Newnes (2012)

    Google Scholar 

  10. Merriam-Webster Dictionary: Intelligence (2021). https://www.merriam-webster.com/dictionary/intelligence

  11. Méndez Fernández, D., et al.: Artefacts in software engineering: a fundamental positioning. Softw. Syst. Model. 18(5), 2777–2786 (2019). https://doi.org/10.1007/s10270-019-00714-3

    Article  Google Scholar 

  12. Lexico: Artefact (2021). https://www.lexico.com/definition/artefact

  13. IGI Global: What is it artifact (2021). https://www.igi-global.com/dictionary/it-artifact/15828

  14. Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Mind. Mach. 17(4), 391–444 (2007)

    Article  Google Scholar 

  15. Hutter, M.: Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Springer, Heidelberg (2004). https://doi.org/10.1007/b138233

  16. Coppin, B.: Artificial Intelligence Illuminated. Jones & Bartlett Learning (2004)

    Google Scholar 

  17. The Economic Times: Sophia, world’s first humanoid citizen, focuses on saving the planet, plans to conquer mt everest (2018). https://economictimes.indiatimes.com/magazines/panache/sophia-worlds-first-humanoid-citizen-focuses-on-saving-the-planet-plans-to-conquer-mt-everest/articleshow/63409249.cms?from=mdr

  18. Waldrop, M.M.: Man-made minds: The promise of artificial intelligence (1987)

    Google Scholar 

  19. Marcinkowski, B., Kuciapski, M.: A business process modeling notation extension for risk handling. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds.) CISIM 2012. LNCS, vol. 7564, pp. 374–381. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33260-9_32

    Chapter  Google Scholar 

  20. Aristodemou, L., Tietze, F.: The state-of-the-art on intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Inf. 55, 37–51 (2018)

    Article  Google Scholar 

  21. Zurada, J., Karwowski, W., Marras, W.S.: A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design. Appl. Ergon. 28(1), 49–58 (1997)

    Article  Google Scholar 

  22. Korczak, J., Hernes, M., Bac, M.: Collective intelligence supporting trading decisions on FOREX market. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 113–122. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_12

    Chapter  Google Scholar 

  23. Brzeski, A.: Parameters optimization in medicine supporting image recognition algorithms (2011)

    Google Scholar 

  24. Boiński, T.M., Ambrożewicz, A., Szymański, J.: Knowledge base suitable for answering questions in natural language (2014)

    Google Scholar 

  25. Waloszek, A., Waloszek, W.: A model for describing and classifying sentiment analysis methods (2017)

    Google Scholar 

  26. Ambroziak, A., Kłosowski, P.: Autodesk Robot Structural Analysis: Podstawy obliczeń. Politechnika Gdańska (2010)

    Google Scholar 

  27. Ficht, G., Piotrowski, R.: Micromouse robot-technical design and construction (2012)

    Google Scholar 

  28. Remagnino, P., Hagras, H., Velastin, S., Monekosso, N.: Ambient intelligence: a gentle introduction (2005)

    Google Scholar 

  29. Teixeira, M.S., Maran, V., de Oliveira, J.P.M., Winter, M., Machado, A.: Situation aware model for multi-objective decision making in ambient intelligence. Appl. Soft Comput. 81, 105532 (2019)

    Google Scholar 

  30. Messika, E.: Mapping the world artificial intelligence landscapes (2017). https://medium.com/@eytanmessika/mapping-the-world-artificial-intelligence-landscapes-223f752efa4

  31. Owoc, M.L., Weichbroth, P.: A note on knowledge management education: towards implementing active learning methods. In: Mercier-Laurent, E. (ed.) AI4KM 2018. IAICT, vol. 588, pp. 124–140. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52903-1_10

    Chapter  Google Scholar 

  32. White, S.: Different types of research and research skills (2020). https://www.al lassignmenthelp.com/blog/research-skills/

  33. McCombes, S.: The main types of research compared (2019). https://www.scribbr.com/methodology/types-of-research/

  34. Mach, M.A., Owoc, M.: Knowledge granularity and representation of knowledge: towards knowledge grid. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds.) IIP 2010. IAICT, vol. 340, pp. 251–258. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16327-2_31

    Chapter  Google Scholar 

  35. Owoc, M., Marciniak, K.: Knowledge management as foundation of smart university. In: 2013 Federated Conference on Computer Science and Information Systems, pp. 1267–1272. IEEE (2013)

    Google Scholar 

  36. Marciniak, K., Owoc, M.L.: Usability of knowledge grid in smart city concepts. In: ICEIS (3), pp. 341–346 (2013)

    Google Scholar 

  37. Owoc, M., Weichbroth, P., Żuralski, K.: Towards better understanding of context-aware knowledge transformation. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1123–1126. IEEE (2017)

    Google Scholar 

  38. Owoc, M.L., Sawicka, A., Weichbroth, P.: Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. arXiv preprint arXiv:2102.09365 (2021)

  39. Hernes, M.: Consensus theory for cognitive agents’ unstructured knowledge conflicts resolving in management information systems. In: Nguyen, N.T., Kowalczyk, R., Hernes, M. (eds.) Transactions on computational collective intelligence XXXII. LNCS, vol. 11370, pp. 1–119. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-662-58611-2_1

    Chapter  Google Scholar 

  40. Taniar, D.: Data Mining and Knowledge Discovery Technologies. IGI Global (2008)

    Google Scholar 

  41. Johansson, J., Elgh, F.: Applying connectivism to engineering knowledge to support the automated business. In: 24th ISPE International Conference on Transdisciplinary Engineering, Singapore, 10 July to 14 July, 2017. pp. 621–628. IOS Press (2017)

    Google Scholar 

  42. Nouri, J., Larsson, K., Saqr, M.: Identifying factors for master thesis completion and non-completion through learning analytics and machine learning. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds.) EC-TEL 2019. LNCS, vol. 11722, pp. 28–39. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29736-7_3

    Chapter  Google Scholar 

  43. Weichbroth, P.: Odkrywanie reguł asocjacyjnych z transakcyjnych baz danych. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, pp. 301–309 (2009)

    Google Scholar 

  44. Pondel, M., Korczak, J.: A view on the methodology of analysis and exploration of marketing data. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1135–1143. IEEE (2017)

    Google Scholar 

  45. Dhamdhere, S.N.: Knowledge management strategies and process in traditional colleges: a study. Int. J. Inf. Libr. Soc. 4(1), 34–42 (2015)

    Google Scholar 

  46. Zinzou, E.F., Doctor, T.R.: Knowledge management practices among the internal quality assurance network (iqan)-member higher education institutions (heis) in thailand. World J. Educ. 10(5) (2020)

    Google Scholar 

  47. Owoc, M., Hauke, K., Weichbroth, P.: Knowledge-grid modelling for academic purposes. In: Mercier-Laurent, Eunika, Boulanger, Danielle (eds.) AI4KM 2015. IAICT, vol. 497, pp. 1–14. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55970-4_1

    Chapter  Google Scholar 

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Correspondence to Mieczysław L. Owoc .

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Owoc, M.L., Weichbroth, P. (2021). University Students’ Research on Artificial Intelligence and Knowledge Management. A Review and Report of Multi-case Studies. In: Mercier-Laurent, E., Kayalica, M.Ö., Owoc, M.L. (eds) Artificial Intelligence for Knowledge Management. AI4KM 2021. IFIP Advances in Information and Communication Technology, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-030-80847-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-80847-1_5

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