Chatbots in maritime education – the potential of chatbot technology in the maritime industry

• Autorzy: Hristova Veneta , Wiśnicki Bogusz , Wołejsza Piotr

Identyfikatory

DOI

10.17402/593

• Języki publikacji: EN

Abstrakty

EN

The presented research shows the possibilities of using chatbot technology in the maritime industry. The authors pay special attention to maritime education, broken down into standard and complementary education. The research is based on the results of a survey, which addresses students of five European maritime universities and examines their opinions about chatbots. Additionally, analogies are applied to the case studies of the successful implementation of chatbots in non-maritime businesses. This research determines the current status and development opportunities of maritime chatbots.

Słowa kluczowe

EN

chatbot; maritime education; educational technologies; maritime industry; automation; communication process

• Wydawca: Wydawnictwo Naukowe Politechniki Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Politechniki Morskiej w Szczecinie
• Rocznik: 2024
• Tom: nr 77 (149)
• Strony: 21--28
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Hristova Veneta

• St. Cyril and St. Methodius University of Veliko Tarnovo, Bulgaria

• https://orcid.org/0000-0002-2511-2987

autor

Wiśnicki Bogusz

• Maritime University of Szczecin, Poland

• https://orcid.org/0000-0003-2932-8969

autor

Wołejsza Piotr

• Maritime University of Szczecin, Poland

• https://orcid.org/0000-0003-4284-5223

Bibliografia

• 1. Adamopoulou, E. & Moussiades, L. (2020a) An Overview of Chatbot Technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology 584. Springer, Cham, doi: 10.1007/978-3-030-49186-4_31.
• 2. Adamopoulou, E. & Moussiades, L. (2020b) Chatbots: History, technology, and applications. Machine Learning with Applications 2, 100006, doi: 10.1016/j.mlwa.2020.100006.
• 3. AI Multiple (2022) Top 15 Chatbot Benefits in 2022 For Companies & Customers. [Online]. Available from: https:// research.aimultiple.com/chatbot-benefits [Accessed: May 27, 2022].
• 4. Artificial Solutions (2020) Chatbots: The Definitive Guide (2020). [Online]. Available from: www.artificial-solutions. com [Accessed: May 27, 2022].
• 5. Berg, J., Gilson, K. & Phalin, G. (2016) Winning the expectations game in customer care. [Online] September 7. Available from: https://www.mckinsey.com/businessfunctions/operations/our-insights/winning-the-expectations -game-in-customer-care [Accessed: May 27, 2022].
• 6. Brinton, C.G., Rill, R., Ha, S., Chiang, M., Smith, R. & Ju, W. (2015) Individualization for Education at Scale: MIIC Design and Preliminary Evaluation. IEEE Transactions on Learning Technologies 8 (1), pp. 136–148, doi: 10.1109/ TLT.2014.2370635.
• 7. Brown, T., Mann, B., Ryder, N., Subbiah, M. et al. (2020) Language Models are Few-Shot Learners. In NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems, Article No. 159, pp. 1877‒1901, doi: 10.48550/arXiv.2005.14165.
• 8. Caldarini, G., Jaf, S. & McGarry, K. (2022) A Literature Survey of Recent Advances in Chatbots. Information 13 (1), 41, doi: 10.3390/info13010041.
• 9. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014) Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734, Doha, Qatar, Association for Computational Linguistics, doi: 10.3115/ v1/d14-1179.
• 10. Deloitte (2018) Chatbots point of view. [Online]. Available from: https://www2.deloitte.com/content/dam/Deloitte/nl/ Documents/deloitte-analytics/deloitte-nl-chatbots-moving -beyond-the-hype.pdf [Accessed: May 27, 2022].
• 11. Devlin, J., Chang, M.W., Lee, K. & Toutanova, K. (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics, doi: 10.18653/v1/N19-1423.
• 12. ERASMUS (2021) Projects connected with a bot. [Online]. Available from: https://ec.europa.eu/programmes/ erasmus-plus/projects_en#search/project/keyword=chatbot &matchAllCountries=false [Accessed: February 14, 2021].
• 13. Gartner (2016) Gartner Predicts a Virtual World of Exponential Change ‒ Smarter With Gartner. [Online] October 18. Available from: https://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/ [Accessed: May 27, 2022].
• 14. Google (2022) Chatbots 2004‒2022. [Online]. Available from: https://trends.google.com/trends/explore?date=all&q =chatbot [Accessed: May 27, 2022].
• 15. Heryandi, A. (2020) Developing Chatbot For Academic Record Monitoring in Higher Education Institution. IOP Conference Series: Materials Science and Engineering 879 (1), 012049, doi:10.1088/1757-899X/879/1/012049.
• 16. Jovic, D. (2023) The Future is Now ‒ 37 Fascinating Chatbot Statistics. [Online] June 17. Available from: https://www.smallbizgenius.net/by-the-numbers/chatbotstatistics/#gref [Accessed: July 27, 2023].
• 17. Maruti Techlabs (2022) Why can chatbots replace Mobile Apps immediately? [Online]. Available from: https://www. marutitech.com/why-can-chatbots-replace-mobile-apps-immediately/ [Accessed: May 27, 2022].
• 18. Nicol, D.J. & Macfarlane-Dick, D. (2006) Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education 31 (2), pp. 199–218, doi: 10.1080/03075070600572090.
• 19. Nimavat, K. & Champaneria, T. (2017) Chatbots: An overview Types, Architecture, Tools and Future Possibilities. IJSRD ‒ International Journal for Scientific Research & Development 5 (07), pp. 1019‒1026.
• 20. Oeste, S., Lehmann, K., Janson, A., Sollner, M. & Leimeister, J.M. (2015) Redesigning University Large Scale Lectures: How To Activate The Learner. Academy of Management Proceedings 2015 (1), 14650, doi: 10.5465/ AMBPP.2015.14650abstract.
• 21. Phaneuf, A. (2020) 7 real examples of brands and businesses using chatbots to gain an edge. [Online] February 12. Available from: https://www.businessinsider.com/ examples-brands-companies-chatbots-business-2017-10 [Accessed: May 27, 2022].
• 22. Science4People (2022) BOT-Learning as a modern teaching method of GEN Z. Strategic Partnership for Higher Education, ERASMUS +, project number 2020-1-PL01- KA203-081777. [Online]. Available from: https://bot. science4people.eu/ [Accessed: May 27, 2022].
• 23. Smutny, P. & Schreiberova, P. (2020) Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education 151 (5), 103862, doi: 10.1016/j.compedu.2020.103862.
• 24. Speeflow (2017) What is Chatbot and how will it help your business? [Online]. Available from: https://speedflow.bg/ blog/what-is-a-chatbot/ [Accessed: May 27, 2022].
• 25. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. & Polosukhin, I. (2017) Attention is all you need. In NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010, doi: 10.5555/3295222.3295349.
• 26. Winkler, R. & Söllner, M. (2018) Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Proceedings 2018 (1), 15903, doi: 10.5465/AMBPP.2018.15903abstract.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).