Τεχνητή Νοημοσύνη και Μουσική Εκπαίδευση Προβολή και Διαφύλαξη Μουσικής Παράδοσης και Πολιτιστικής Κληρονομιάς


Δημοσιευμένα: Μαρ 4, 2025
Λέξεις-κλειδιά:
ΤΝ Μουσική Εκπαίδευση Μουσική Παράδοση Πολιτιστική Διαφύλαξη Εκπαιδευτικό Περιεχόμενο
Δημήτρης Χατζηγιαννάκης
Αγνή Παπαδοπούλου
Περίληψη

Η παρούσα εισήγηση εξετάζει τον ρόλο της Τεχνητής Νοημοσύνης (ΤΝ) στην παραγωγή και διανομή περιεχομένου για τη μουσική εκπαίδευση, δίνοντας βάρος στη διαφύλαξη και προβολή τοπικών μουσικών παραδόσεων και πολιτιστικών στοιχείων. Η ΤΝ επαναπροσδιορίζει τον τρόπο με τον οποίο αλληλεπιδρούμε με τη μουσική, προσφέροντας δυνατότητες εκπαίδευσης και εμπλοκής των εκπαιδευομένων (Wang, 2022). Αναλύονται περιπτώσεις χρήσης όπου η ΤΝ ενισχύει την αλληλεπίδραση μεταξύ των εκπαιδευόμενων και του μουσικού περιεχομένου, αυξάνοντας την προσβασιμότητα και τη σχέση με τη μουσική (Nazir, 2022), καθιστώντας τη μουσική εκπαίδευση πιο προσβάσιμη και προσαρμοσμένη στις ανάγκες κάθε μαθητή. Η ισορροπημένη ενσωμάτωση της ΤΝ στη μουσική εκπαίδευση διασφαλίζει ότι η τεχνολογία υποστηρίζει και ενισχύει την ανθρώπινη δημιουργικότητα, την εκφραστικότητα και γενικότερα τη μαθησιακή εμπειρία (Lagerlöf, 2022).

Λεπτομέρειες άρθρου
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Αναφορές
Abcjs-editor. (n.d.). Abcjs-editor. https://editor.drawthedots.com/
Arvin, N. (2023). Teacher experiences with ai-based educational tools. aitechbesosci, 1(2), 26-32. https://doi.org/10.61838/kman.aitech.1.2.5
Baker, R. S.; Siemens, G. Educational data mining and learning analytics. In:Cambridge Handbook of the Learning Sciences. 2nd ed. New York: CambridgeUniversity Press, 2014. p. 253-272. (16) (PDF) Desafios e oportunidades da integração da Inteligência Artificial no currículo acadêmico. Available from: https://www.researchgate.net/publication/383170027_Desafios_e_oportunidades_da_integracao_da_Inteligencia_Artificial_no_curriculo_academico [accessed Sep 04 2024].
Brown, A., & Bischoff, J. (2020). Machine Musicianship: Artificial Intelligence in Music Practice. Cambridge University Press.
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018). Next-generation machine learning for biological networks. Cell, 173(7), 1581-1592. https://doi.org/10.1016/j.cell.2018.05.015
Casillo, M., Santo, M. D., Mosca, R., & Santaniello, D. (2022). An ontology-based chatbot to enhance experiential learning in a cultural heritage scenario. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.808281
Chatzigiannakis, D., & Fevgalas, S. (2023). From "Panharmonium" to "Arduinonium". The unheard music intervals reproduced with an Arduino Uno. 17th Edition of the EUTIC [13-15/10/2022] Corfu-Online, October 13-15 Ionian University - Department of Audio & Visual Arts, 2022, 22–32.
Chen, W. (2022). Design of music teaching system based on artificial intelligence. Mathematical Problems in Engineering, 2022, 1-7. https://doi.org/10.1155/2022/2627395
Cheng, M. (2022). The creativity of artificial intelligence in art. The 2021 Summit of the International Society for the Study of Information, 15, 110. https://doi.org/10.3390/proceedings2022081110
Feenberg. A. (2016). Concretizing Simondon and Constructivism: A Recursive Contribution to the Theory of Concretization. Science, Technology and Human Values 42(1), 62-85. https://doi.org/10.1177/0162243916661763
Hashim, S., Omar, M., Jalil, H., & Sharef, N. (2022). Trends on technologies and artificial intelligence in education for personalized learning: systematic literature review. International Journal of Academic Research in Progressive Education and Development, 11(1). https://doi.org/10.6007/ijarped/v11-i1/12230
Hoffmann, P. (2002). Towards an “automated art”: Algorithmic processes in Xenakis’ compositions. Contemporary Music Review, 21(2–3), 121–131. https://doi.org/10.1080/07494460216650
Lagerlöf, P. (2022). Interprofessional dialogue and the importance of contextualising children’s participation: a collaboration between different disciplines around new technology. Methodology for Research With Early Childhood Education and Care Professionals, 121-131. https://doi.org/10.1007/978-3-031-14583-4_8
LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Li, S. (2024). Intelligent construction of university music education teaching system based on artificial intelligence technology. Journal of Electrical Systems, 20(3s), 530-539. https://doi.org/10.52783/jes.1326
McCormack, J., Gifford, T., Hutchings, P., Rodriguez, M. T. L., Yee-King, M., & d’Inverno, M. (2019). In a silent way. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300268
Melody maker. ChatGPT. (n.d.). https://chatgpt.com/g/g-nspjq6tbN-melody-maker
Nazir, S. (2022). A comprehensive overview of ai-enabled music classification and its influence in games. Soft Computing, 26(16), 7679-7693. https://doi.org/10.1007/s00500-022-06734-4
Novelli, N. and Proksch, S. (2022). Am i (deep) blue? music-making ai and emotional awareness. Frontiers in Neurorobotics, 16. https://doi.org/10.3389/fnbot.2022.897110
Ou, B. (2023). Investigating midi data simplification by ai models. Applied and Computational Engineering, 21(1), 114-120. https://doi.org/10.54254/2755-2721/21/20231129
Papadopoulou, A. (2019). Art, Technology, Education: Synergy of Modes, Means, Tools of Communication. Educ. Sci. 9 (3), 237; https://doi.org/10.3390/educsci9030237
Piaget, J. (1972). Intellectual Evolution from Adolescence to Adulthood. Human Development, 15, 1-12. http://dx.doi.org/10.1159/000271225
Pogalin, D. V. R. and Lestari, D. T. (2023). Unveiling the melodic traditions of mahzani: an ethnomusicological investigation of tombulu music in indonesia's minahasa region. Gelar : Jurnal Seni Budaya, 21(1), 36-46. https://doi.org/10.33153/glr.v21i1.5282
Rohwer, D. (2023). Research-to-resource: chatgpt as a tool in music education research. Update: Applications of Research in Music Education. https://doi.org/10.1177/87551233231210875
Sarwari, A. (2024). Assessment of the impacts of artificial intelligence (ai) on intercultural communication among postgraduate students in a multicultural university environment. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-63276-5
Sturm, B. L., Iglesias, M., Ben-Tal, O., & Miron, M. (2019). Artificial intelligence and music: open questions of copyright law and engineering praxis. Arts, 8(3), 115. https://doi.org/10.3390/arts8030115
Ülger, K. (2018). The effect of problem-based learning on the creative thinking and critical thinking disposition of students in visual arts education. Interdisciplinary Journal of Problem-Based Learning, 12(1). https://doi.org/10.7771/1541-5015.1649
Wang, X. (2022). Design of vocal music teaching system platform for music majors based on artificial intelligence. Wireless Communications and Mobile Computing, 2022, 1-11. https://doi.org/10.1155/2022/5503834
Wang, Y. (2024). Influence of the development of internet big data on college students' music education. International Journal of Information Systems and Supply Chain Management, 17(1), 1-17. https://doi.org/10.4018/ijisscm.343260
Xu, N. and Zhao, Y. (2021). Online education and wireless network coordination of electronic music creation and performance under artificial intelligence. Wireless Communications and Mobile Computing, 2021, 1-9. https://doi.org/10.1155/2021/5999152
Yang, J. (2021). Research on the artificial intelligence teaching system model for online teaching of classical music under the support of wireless networks. Wireless Communications and Mobile Computing, 2021(1). https://doi.org/10.1155/2021/4298439
Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and applications of artificial intelligence in music education. Technologies, 11(2), 42. https://doi.org/10.3390/technologies11020042
Yuan, N. (2024). Does ai‐assisted creation of polyphonic music increase academic motivation? the deepbach graphical model and its use in music education. Journal of Computer Assisted Learning, 40(4), 1365-1372. https://doi.org/10.1111/jcal.12957