FEELING AND LISTENING CLOSER WITH ARTIFICIAL INTELLIGENCE: AN EXPERIMENTAL STUDY

Giorgia Del Bianco, Lucia Monacis, Giuseppe Annacontini

Abstract


Aim: This study aimed to analyze the effect of the application of Artificial Intelligence (AI) on emotional well-being in educational context among adolescents with special needs

Method: The sample was composed by 135 Austrian deaf adolescents (Mage=13, SD=1.38) who completed a self-report questionnaire assessing levels of self-esteem before and after intervention based on a specific technological device (Storysign software that is attentive to emotional skills)

Results: Findings showed significant difference in pre-post test score, thus demonstrating an increased level on the mean values of the self-esteem (M=14, SD=2.34 vs M=21 SD=1.38, p < 0.005). Furthermore, no significant difference emerged between males and females.

Conclusions: Although other studies are needed to confirm such positive effect of an AI-implemented tool among deaf adolescents, this investigation provides initial empirical evidence of how AI could be integrated in the framework of the Universal Design for Learning


Keywords


Artificial intelligence, learning, self-esteem, inclusive education

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References


El Naggar, A., Gaad, E., & Inocencio, S. A. M. (2024). Enhancing inclusive education in the UAE: Integrating AI for diverse learning needs. Research in Developmental Disabilities, 147, 104685. https://doi.org/10.1016/j.ridd.2024.104685

Haug, T., Mann, W., & Holzknecht, F. (2023). The Use of Technology in Sign Language Testing: Results of a Pre-Pandemic Survey. Sign Language Studies, 23(2), 243–281. https://doi.org/10.1353/sls.2023.0003

Kohli, R., Phutela, S., Garg, A., & Viner, M. (2021). Artificial Intelligence Technology to Help Students With Disabilities: Promises and Implications for Teaching and Learning. In A. Singh, C. J. Yeh, S. Blanchard, & L. Anunciação (A c. Di), Advances in Early Childhood and K-12 Education (pp. 238–255). IGI Global. https://doi.org/10.4018/978-1-7998-7630-4.ch013

Kudrinko, K., Flavin, E., Zhu, X., & Li, Q. (2021). Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review. IEEE Reviews in Biomedical Engineering, 14, 82–97. https://doi.org/10.1109/RBME.2020.3019769

Papastratis, I., Chatzikonstantinou, C., Konstantinidis, D., Dimitropoulos, K., & Daras, P. (2021). Artificial Intelligence Technologies for Sign Language. Sensors, 21(17), 5843. https://doi.org/10.3390/s21175843

Parton, B. S. (2005). Sign Language Recognition and Translation: A Multidisciplined Approach From the Field of Artificial Intelligence. Journal of Deaf Studies and Deaf Education, 11(1), 94–101. https://doi.org/10.1093/deafed/enj003

Wadhawan, A., & Kumar, P. (2021). Sign Language Recognition Systems: A Decade Systematic Literature Review. Archives of Computational Methods in Engineering, 28(3), 785–813. https://doi.org/10.1007/s11831-019-09384-2




DOI: https://doi.org/10.32043/gsd.v8i3.1127

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