Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
Języki publikacji
Abstrakty
Recent research has underscored the pivotal role of soft skills in navigating the complexities of today's workplace dynamics. Soft skills encompass a broad spectrum of attributes, such as effective communication, adept collaboration, nimble adaptability, and profound emotional intelligence, all of which are integral to fostering productive team environments and driving organizational success. Despite their acknowledged importance, quantifying and evaluating soft skills has traditionally been hindered by their inherently subjective nature. However, the emergence of artificial intelligence (AI) technologies has revolutionized the landscape of skill assessment, presenting novel opportunities to address these longstanding challenges. By leveraging AI-powered algorithms, organizations can now analyze vast datasets encompassing various facets of human interaction, enabling a more nuanced and objective evaluation of individuals' soft skill proficiencies. Moreover, AI driven assessments offer scalability, allowing for the efficient evaluation of large cohorts of employees or candidates. Nonetheless, this intersection of AI and soft skills measurement is not without its obstacles. Ethical considerations surrounding data privacy, algorithmic bias, and the potential for automation induced job displacement necessitate careful scrutiny and regulation. Furthermore, the dynamic nature of soft skills presents a continuous challenge, as individuals must continually adapt and refine their abilities to meet evolving workplace demands. Despite these challenges, the synergistic relationship between AI and soft skills measurement holds immense promise for the future of talent assessment and development. By embracing AI-driven approaches, organizations can cultivate a workforce equipped with the diverse skill set necessary to thrive in an ever-changing professional landscape.
Rocznik
Tom
Strony
573--578
Opis fizyczny
Bibliogr. 19 poz., il.
Twórcy
autor
- National Research Council (CNR)
autor
- University Giustino Fortunato
Bibliografia
- 1. Mitchell D. (2008). What really Works in Special and Inclusive Education. London: Routledge. (Cit. in Lucio Cottini (2017). Pedagogia speciale e didattica per l’inclusione. Roma: Carocci Editore).
- 2. Ranieri, M. (2010). La Media Literacy nei documenti dell'Unione Europea. Studi e ricerche MED Media Education.
- 3. Rivoltella, P. C. (2005). Multimedia training, media education, and co-operative learning: new professional scenarios for educators. In A. Ascenzi, M. Corsi (Eds.), Educator/Trainer Profession. New educational needs and new pedagogical professionalism (pp. 3-23). Milan: Vita e Pensiero.
- 4. Rivoltella, P. C. (2006). Teacher, mentor, tutor. A framework for reflection on e-learning professionalism. In P. Crispiani, P. G. Rossi (Eds.), E-Learning. Training, models, proposals (pp. 55-74). Rome: Armando.
- 5. Rivoltella, P. C. (2017). Community Technologies. Brescia: ELS La Scuola.
- 6. Rivoltella, P. C. (2020). New Alphabets. Education and Cultures in the Post-Media Society. Brescia: Scholé - Morcelliana.
- 7. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78(2), 311-328.
- 8. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185-211.
- 9. Zeidner, M., Roberts, R. D., & Matthews, G. (2008). The science of emotional intelligence: Knowns and unknowns. Oxford University Press.
- 10. Goleman, D., Boyatzis, R., & McKee, A. (2013). Primal leadership: Realizing the power of emotional intelligence. Harvard Business Press.
- 11. Mayer, J. D., Salovey, P., & Caruso, D. R. (2002). Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) user's manual. Toronto, Canada: MHS Publishers.
- 12. Jackson, G. S. (2016). The art of empathetic leadership: How leaders can drive engagement and build trust. Palgrave Macmillan.
- 13. Lee, J., Kim, J., Kim, J., Kim, S. J., & Kim, G. (2020). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 59, 110-125.
- 14. Sackett, P. R., Walmsley, P. T., Lievens, M. L., & Highhouse, M. R. (2017). Assessment centers and the prediction of managerial performance. Walter de Gruyter.
- 15. M Naeem, STH Rizvi, A Coronato; A gentle introduction to reinforcement learning and its application in different fields, IEEE access 8, 209320-209344 http://dx.doi.org/10.1109/ACCESS.20220.3038605
- 16. M Naeem, A Coronato, G Paragliola; Adaptive treatment assisting system for patients using machine learning; 2019 sixth international conference on social networks analysis, management http://dx.doi.org/10.1109/SNAMS.2019.8931857
- 17. Naeem, Muddasar and Coronato, Antonio; An AI-empowered home-infrastructure to minimize medication errors, Journal of Sensor and Actuator Networks, V-11.1, P-13, 2022 https://doi.org/10.3390/jsan11010013
- 18. A semantic context service for smart offices A Coronato, G De Pietro, M Esposito 2006 International Conference on Hybrid Information Technology 2, 391-399
- 19. A reinforcement learning based intelligent system for the healthcare treatment assistance of patients with disabilities A Coronato, M Naeem International Symposium on Pervasive Systems, Algorithms and Networks, 15-28 http://dx.doi.org/10.1007/978-3-030-30143-9_2
Uwagi
Thematic Sessions: Short Papers
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-81b06a01-24df-475e-87dc-ea6b051518ec
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