Italian Journal of Health Education, Sports and Inclusive Didactics
EMOTION AND LEARNING, ALGORITHMS AND ETHICAL FORGETTING. A NECESSARY REFLECTION IN THE AGE OF DIGITIZATION
Abstract
We are living through a season of a strong push for the digitization of educational processes, of a development of digital culture in which AI applied to the educational field is, undoubtedly, a priority area that challenges the theoretical and methodological assumptions of Educational Technology demanding a redefinition of it. One of the most debated topics concerns "Affective Computing" a multidimensional and multidisciplinary approach centered on emotions and the possibility of machines or robots acquiring the ability to perceive, express and generate emotions useful to support learning processes. Such research, which is based on the subject's facial expressions and muscle movements, is perhaps the most typical expression of human beings and requires ethical and legal reflections about possible privacy violations. This article aims, on the one hand, to analyze the risks and critical issues related to a possible uncontrolled dissemination of these personal data on the other hand, to reflect on the educational paradigm that attends emotional human learning and the capabilities of algorithms.
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Chiu, T.K.F., (2021), Digital Support for Student Engagement in Blended Learning Based on Self-Determination Theory, in Computers in Human Behavion, vol. 124, 106909, https://doi.org/10.1016/j.chb.2021.106909.
Christen, P., (2014), Privacy Aspects in Big Data Integra-tion: Challenges and Opportunities, in Proceedings of the First International Workshop on Privacy and Security of Big Data, pp. 1-1.
Cope, B. and Kalantzis, M. and Searsmith, D., (2021), Artificial Intelligence for Education: Knowledge and its Assessment in Ai-En-abled Learning Ecologies, in Educational Philosophy And Theory, pp. 1229-1245, https://doi.org/10.1080/00131857.2020.1728732.
Cucchiara R., (2021), L'intelligenza non è artificiale, Mondadori, Milano.
D’Aloia A., (2019), Il diritto verso “il mondo nuovo”. Le sfide dell’Intelligenza Artificiale, in Rivista di Biodiritto, pp. 3-31.
De la Higuera, C., (2019), A Report About Education, Train- X ing Teachers and Learning Artificial Intelligence: Overview of Key Issues, in Education, Computer Science, pp. 1-11, https://www.k4all.org/wpcontent/uploads/2019/11/.
Debord, G., (1967), La società dello spettacolo, tr. it. Baldini - Castoldi, Milano 2017.
Dede, C., (1986), A Review and Synthests of Recent Research in Intelligent Computer-Assisted Instruction, in International Journal of Man-Machine Studies, 24, 4, April 1986, pp. 329-353.
Duan, Y. - Edwards, J.S. - Dwivedi, Y., (2019), Artificial Intelligence for Decision Making in the Era of Big Data - Evolution, Challenges and Research Agenda, in International Journal of Information Management, 48, pp. 63-71.
Dyson, G., (1997), L'evoluzione delle macchine. Da Darwin all’intelligenza globale, tr. it. Raffaello Cortina, Milano 2000.
E.J. Kindt, (2021), Transparency and Accountability Mechanisms for Facial Recognition, in German Marshall Foundation Transatlantic dialogue.
Eguchi, A., (2014), Robotics as a Learning Tool for Educational Transformation. Human-Computer Interaction, in Proceedings of 4th International Workshop Teaching Robotics, Teaching with Robotics& 5th International Conference Robotics in Education, Padova, pp. 27–34.
Facer, K. and Selwyn, N., (2021), Digital Technology and the Futures of Education: Towards Non-Stupid' Optimism, UNESCO.
Ferrari, L. and Macauda, A. and Soriani, A. and Russo, V., (2020), Educational Robotics and Artificial Intelligence Education: What Priorities for Schools?, in Form@re - Open Journal Per La Formazione in Rete, pp. 68-85.
Floridi, L., (2022), Etica dell'intelligenza artificiale. Sviluppi, opportunità, sfide, tr. it. Raffaello Cortina, Milano 2022.
Francalanci, L., (2020), Dall’algocrazia all’algoretica. Il potere degli algoritmi, in Italiano digitale, XIV, 3, pp. 97-103.
Gerritsen, D. and Zimmerman, J. and Ogan, A., (2018), Towards A Framework For Smart Classrooms That Teach Instructors To Teach, in Proceedings International Conference on Learning Sciences, 3, pp. 1-4.
Gheibi, O. and Weyns, D. and Quin, F., (2021), Applying Machine Learning in Self-adaptive Systems: A Systematic Literature Review, in ACM Transactions on Autonomous and Adaptive System, 15, 3, Article 9 (August 2021), p. 37.
Ioannou, A. and Makridou, E., (2018), Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work, in Education and Information Technologies, pp. 2531-2544.
J.D. Woodward, (2003), Biometrics: A Look at Facial Recognition, in Documented Briefing Rand Corporation, Rand Corporation, p. 293.
J.P. Woodward and Rand Corporation, (2019), Facial Recognition: Defining Terms to Clarify Challenges, in Documented Briefing Rand Corporation, Rand Corporation.
Keane, T. and Chalmers, C. and Williams and M., & Boden, M., (2016), The impact of humanoid robots on students’ computational thinking. Australian Council for Computers, in Education 2016 Conference: Refereed Proceedings, pp. 93-102.
Lehmann, H. and Rossi, P. G., (2019), Social robots in educational contexts: Developing an application in enactive didactics, in Journal of E-Learning and Knowledge Society, pp. 27-41.
Panciroli C. and Rivoltella P.C., Pedagogia algoritmica. Per una riflessione sull’Intelligenza Artificiale, pp. 104-105.
Vivona A. and Ali L. and Sorrentino C. and Martiniello L., (2023), Bridging the Gap between the Body and the Machine: Embodied Learning with Interventional Brain Computer Interfaces?, in Sport Mont, pp. 109-116.
Vivona A. and Caruccio I. and Consalvo P., (2023), Digital and special educational needs, in gsdjournal.it, ISSN:2532-3296.
Vivona A. and Raffone M. and Ambretti A., (2023), Beyond Boundaries: the holistic learning approach through Diversity, and Creativity, in gsdjournal.it, ISSN: 2532-3296.
Woolf, B.P., (2008), Building Intelligent Interative Tutors. Stu-dent-Centered Strategies for Revolutionizing E-Learning, Elsevier-Morgan Kaufmann, Amsterdam.
Zanellati, A. and Zingaro, S.P. and Del Bonifro, F. and Gabbrielli, M. and Levrini, O. and Panciroli, C., (2021), Informing Predictive Models against Students Dropout, in Atti Didamatica, pp. 18-25; https://rb.gy/dxye7c.
DOI: https://doi.org/10.32043/gsd.v8i3.1175
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Italian Journal of Health Education, Sports and Inclusive Didactics
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