From Artificial Intelligence to Artificial Assistants in Education: Theoretical Foundations and First Applications in Teacher Education

Titolo Rivista EDUCATION SCIENCES AND SOCIETY
Autori/Curatori Luca Ballestra Caffaratti, Alessandro Monchietto
Anno di pubblicazione 2025 Fascicolo 2024/2
Lingua Inglese Numero pagine 13 P. 105-117 Dimensione file 0 KB
DOI 10.3280/ess2-2024oa18478
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The article explores the potential and limitations of Generative Artificial Intelligence (GenAI) in teacher training and inclusive education, emphasizing the importance of specific training for educators on the pedagogical use of these technologies. Experiments conducted at the University of Turin highlight the role of GenAI in creating personalized teaching materials and supporting student learning, particularly for those with Special Educational Needs (SEN). However, it is clearly evident that careful teacher supervision is essential to ensure pedagogical validity and alignment with educational objectives. The article concludes that GenAI should be considered a teaching assistant, integrated into a critical, human-centered approach aimed at fostering inclusive and student-centered learning.

The article explores the potential and limitations of Generative Artificial Intelligence (GenAI) in teacher training and inclusive education, emphasizing the importance of specific training for educators on the pedagogical use of these technologies. Experiments conducted at the University of Turin highlight the role of GenAI in creating personalized teaching materials and supporting student learning, particularly for those with Special Educational Needs (SEN). However, it is clearly evident that careful teacher supervision is essential to ensure pedagogical validity and alignment with educational objectives. The article concludes that GenAI should be considered a teaching assistant, integrated into a critical, human-centered approach aimed at fostering inclusive and student-centered learning.

Parole chiave:; Artificial Intelligence in Education; Inclusive Education; Teacher Training; Personalised Learning; Learning Technologies

  1. Alba Pastor C. (2019). Diseño Universal para el Aprendizaje: un modelo teórico-práctico para una educación inclusiva de calidad. Participación educativa, 6(9): 55-68. Retrieved on October, 24, 2024 from https://redined.educacion.gob.es/xmlui/handle/11162/190783.
  2. Amershi B. (2020). Culture, the process of knowledge, perception of the world and emergence of AI. AI & Society, 35: 417-430. DOI: 10.1007/s00146-019-00885-z
  3. Atzei A., Ballestra Caffaratti L., Borghetti C., Bozzetto L., Forno A., Secchia M. and Zanzo A. (2023). Impostare percorsi di formazione di insegnanti sull’assistenza artificiale alla mediazione didattica. Loescher Editore. Retrieved on October, 24, 2024 from https://laricerca.loescher.it/impostare-percorsi-di-formazione-di-insegnanti-sullassistenza-artificiale-alla-mediazione-didattica/.
  4. Bandura A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2): 164-180.
  5. Brauner P., Hick A., Philipsen R., and Ziefle M. (2023). What does the public think about artificial intelligence? A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science, 5, 1113903. DOI: 10.3389/fcomp.2023.1113903
  6. Brod G., Kucirkova N., Shepherd J., Jolles D., and Molenaar I. (2023). Agency in Educational Technology: Interdisciplinary Perspectives and Implications for Learning Design. Educational Psychology Review, 35, 25. DOI: 10.1007/s10648-023-09749-x
  7. Buzan T., Buzan B. (1993). The mind map book. BBC Books.
  8. CAST (2018). Universal Design for Learning Guidelines version 2.2. Retrieved on October, 24, 2024 from http://udlguidelines.cast.org.
  9. Chih-Yuan Sun J., Tsai H.-E., and Cheng W. K. R. (2023). Effects of integrating an open learner model with AI-enabled visualization on students’ self-regulation strategies usage and behavioural patterns in an online research ethics course. Computers and Education: Artificial Intelligence, 4, 100120. DOI: 10.1016/j.caeai.2022.100120
  10. Cristianini N. (2023). La scorciatoia. Come le macchine sono diventate intelligenti senza pensare in modo umano. Bologna: il Mulino.
  11. Damiani P., Curto N., Monchietto A., and Marchisio C. M. (2021). The training of trainers of the specialization course in special educational needs. The Turin experience of the University Course of Professional Development (CUAP). Italian Journal of Health Education, Sports and Inclusive Didactics, 5(4). Retrieved on October, 24, 2024 from https://ojs.gsdjournal.it/index.php/gsdj/article/view/586.
  12. De Mauro T. (1980). Guida all’uso delle parole. Roma: Editori Riuniti.
  13. European Parliament (2023). Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI. Retrieved on October, 24, 2024 from https://www.europarl.europa.eu/news/en/press-room/20231206IPR15699/artificial-intelligence-act-deal-on-comprehensive-rules-for-trustworthy-ai.
  14. Flores-Vivar J.-M., García-Peñalvo F.-J. (2023). Reflections on the ethics, potential, and challenges of artificial intelligence in the framework of quality education (SDG4). Comunicar, 30(74): 35-44. Retrieved on October, 24, 2024 from https://www.revistacomunicar.com/index.php?&idioma=en.
  15. Gardner H. (1983). Frames of Mind: The Theory of Multiple Intelligences. New York: Basic Books.
  16. Goleman D. (1996). Emotional Intelligence: Why It Can Matter More Than IQ. Bloomsbury Paperbacks.
  17. Guastavigna M. (2015). Non solo concettuali. Mappe, schemi, apprendimento. I Quaderni della Ricerca, 23.
  18. Guastavigna M. (2020). Multimedialità e inclusione. In Pavone M. (Ed.), Didattiche da scoprire. Linguaggi, diversità, inclusione (pp. 244-267). Milano: Mondadori Università.
  19. Hrastinski S., Olofsson A. D., Arkenback C. et al. (2019). Critical Imaginaries and Reflections on Artificial Intelligence and Robots in Postdigital K-12 Education. Postdigital Science and Education, 1: 427-445. DOI: 10.1007/s42438-019-00046-x
  20. Lampos V., Mintz J., and Qu X. (2021). An artificial intelligence approach for selecting effective teacher communication strategies in autism education. NPJ Science of Learning, 6, 25. DOI: 10.1038/s41539-021-00102-x
  21. Lumbelli L. (2014). La comprensione come problema: il punto di vista cognitivo. Bari: Laterza.
  22. Novak J. D., Gowin D. B. (1984). Learning how to learn. Cambridge University Press.
  23. Piemontese M. E. (1996). Capire e farsi capire. Teorie e tecniche della scrittura controllata. Napoli: Tecnodid.
  24. Pogorskiy E., Beckmann J. F. (2023). From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning. Computers and Education: Artificial Intelligence, 4, 100111. DOI: 10.1016/j.caeai.2022.100111
  25. Radanliev P., Santos O., Brandon-Jones A., and Joinson A. (2024). Ethics and responsible AI deployment. Frontiers in Artificial Intelligence, 7, 1377011.
  26. Reiss M. J. (2021). The Use of AI in Education: Practicalities and Ethical Considerations. London Review of Education, 19(1): 5, 1-14. DOI: 10.14324/LRE.19.1.05
  27. Robila M., Robila S. A. (2020). Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences. Journal of Child and Family Studies, 29: 2954-2966. DOI: 10.1007/s10826-019-01689-x
  28. Russell S. J., Norvig P. (2010). Artificial intelligence: A modern approach. 3rd ed. Upper Saddle River, New Jersey: Pearson Education, Inc.
  29. Salas-Pilco S. Z., Xiao K., and Oshima J. (2022). Artificial Intelligence and New Technologies in Inclusive Education for Minority Students: A Systematic Review. Sustainability, 14, 13572. DOI: 10.3390/su142013572
  30. Sanger C. S. (2020). Inclusive Pedagogy and Universal Design Approaches for Diverse Learning Environments. In Sanger C., Gleason N. (Eds.), Diversity and Inclusion in Global Higher Education. Singapore: Palgrave Macmillan. DOI: 10.1007/978-981-15-1628-3_2
  31. Searle J. R. (1990). Is the brain’s mind a computer program?. Scientific American, 262(1): 25-31.
  32. Smith E. M., Graham D., Morgan C., and MacLachlan M. (2023). Artificial intelligence and assistive technology: risks, rewards, challenges, and opportunities. Assistive Technology, 35(5): 375-377. DOI: 10.1080/10400435.2023.2259247
  33. Sternberg R. J. (1988). The Triarchic Mind. A New Theory of Human Intelligence. New York: Viking Press.
  34. Strzelecki A. (2023). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education. DOI: 10.1007/s10755-023-09686-1
  35. Ullrich A., Vladova G., Eigelshoven F., and Renz A. (2022). Data mining of scientific research on artificial intelligence in teaching and administration in higher education institutions: a bibliometrics analysis and recommendation for future research. Discover Artificial Intelligence, 2, 16. DOI: 10.1007/s44163-022-00031-7
  36. UNESCO (2019). Artificial Intelligence in education: Challenges and opportunities for sustainable development. UNESCO Working Papers on Education Policy. Retrieved on October, 24, 2024 from https://bit.ly/3z6BQvN.
  37. UNESCO (2021a). International Forum on AI and the futures of education developing competencies for the AI era. UNESCO. Retrieved on October, 24, 2024 from https://bit.ly/3zoB6AS.
  38. UNESCO (2021b). Recommendation on the ethics of artificial intelligence. UNESCO. Retrieved on October, 24, 2024 from https://unesdoc.unesco.org/ark:/48223/pf0000380455_spa.locale=en.
  39. UNESCO (2024). AI competency framework for teachers. United Nations Educational, Scientific and Cultural Organization. DOI: 10.54675/ZJTE2084
  40. Watanabe A. (2023). Exploring Totalitarian Elements of Artificial Intelligence in Higher Education with Hannah Arendt. International Journal of Technoethics (IJT), 14(1): 1-15. DOI: 10.4018/IJT.329239
  41. Xia Q., Chiu T. K. F., Zhou X.-Y., Chai C. S., and Cheng M.-T. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. DOI: 10.1016/j.caeai.2022.100118
  42. Yang T.-C., Chen J.-H. (2023). Pre-service teachers’ perceptions and intentions regarding the use of chatbots through statistical and lag sequential analysis. Computers and Education: Artificial Intelligence, 4, 100119. DOI: 10.1016/j.caeai.2022.100119
  43. Zawacki-Richter O., Marín V. I., Bond M., and Gouverneur F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. DOI: 10.1186/s41239-019-0171-0
  44. Zhai X., Panjwani-Charania S. (2023). AI for Students with Learning Disabilities: A Systematic Review. In Zhai X., Krajcik J. (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.

Luca Ballestra Caffaratti, Alessandro Monchietto, From Artificial Intelligence to Artificial Assistants in Education: Theoretical Foundations and First Applications in Teacher Education in "EDUCATION SCIENCES AND SOCIETY" 2/2024, pp 105-117, DOI: 10.3280/ess2-2024oa18478