=Paper=
{{Paper
|id=Vol-3902/8
|storemode=property
|title=From Teacher Training to Classroom Activities: The USR-EFT Piemonte Experience on AI in 2020 - 2024 (full paper)
|pdfUrl=https://ceur-ws.org/Vol-3902/8_paper.pdf
|volume=Vol-3902
|authors=Andrea Piccione,Anna A. Massa
|dblpUrl=https://dblp.org/rec/conf/edu4ai/PiccioneM24
}}
==From Teacher Training to Classroom Activities: The USR-EFT Piemonte Experience on AI in 2020 - 2024 (full paper)==
https://ceur-ws.org/Vol-3902/8_paper.pdf
From teacher training to classroom activities: the USR-EFT
Piemonte experience on AI in 2020 - 2024
Andrea Piccione1,2, *,† and Anna A. Massa1,†
1
Ufficio Scolastico Regionale per il Piemonte, Corso V. Emanuele II 70, Torino, TO, Italy
2
Équipe Formativa Territoriale per il Piemonte
Abstract
The rapid evolution of technology in education presents both opportunities and challenges,
making continuous professional development essential for teachers. Staying up to date with
new tools is difficult due to the fast pace of change, but crucial for integrating technology
into teaching and enhancing digital competence, which is vital for citizenship rights.
Artificial Intelligence (AI) literacy, although an emerging topic globally, is largely missing
from teacher education. Many educators lack the technical knowledge to use AI tools
effectively in classrooms, highlighting the growing need for improved digital skills and AI
training.
In this contribution, we will begin with the international and national background and
frameworks, and then focus on the developments in Piedmont with an overview of teacher
training programs on AI in recent years. We will explore how these programs have adjusted
to the rapidly changing technological environment by integrating new tools, methods, and
approaches to prepare educators with the necessary knowledge and skills to incorporate AI
into their teaching practices effectively. Furthermore, we will outline the class activities with
students that have been implemented based on these training paths and how these activities
have evolved over the years.
Keywords
Artificial Intelligence, Teacher training, Classroom activities
1. Introduction
The rapid emergence and evolution of new technologies present both opportunities and
challenges, particularly in the field of education. It is crucial that any training or professional
development related to these technologies remains up to date and adaptable, as what is cutting-edge
today could quickly become obsolete. Implementing emerging technologies in educational settings
presents its own challenges [1]. Educators may find it difficult to stay current with the latest tools
and methodologies due to the rapid rate of technological change. This is why continuous and
responsive professional development for teachers is essential [2]. Moreover, new technologies are
essential for improving digital competence, which is increasingly important for exercising citizenship
rights. Without regular training, educators may struggle to effectively integrate new technologies
into their teaching practices. As a result, this could potentially hinder the school's efforts to support
students in promoting their rights of citizenship. Teachers often feel a strong need to thoroughly
understand and become proficient in using new technological tools before they can confidently
incorporate them into their daily lessons. Therefore, ongoing professional development is not only
about keeping pace with technological advancements but also about empowering teachers to feel
confident and competent in using these tools to enhance their teaching and enrich their students'
learning experiences.
1st Workshop on Education for Artificial Intelligence (edu4AI 2024, https://edu4ai.di.unito.it/), co-located with the 23nd International
Conference of the Italian Association for Artificial Intelligence (AIxIA 2024). 26-28 November 2024, Bolzano, Italy
*
Corresponding author.
†
These authors contributed equally.
piccione.eft@istruzionepiemonte.it (A. Piccione); annaalessandra.massa@istruzione.it (A. A. Massa)
0009-0006-5448-3548 (A. Piccione); 0009-0003-6667-8949 (A. A. Massa)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
AI literacy is an emerging research topic in global education but is largely absent in the context of
teacher education [3]. Many of these AI tools are new to teachers. They may not have rich technical
knowledge to use AI educational applications to facilitate their teaching, and to develop students’ AI
digital capabilities. As such, there is a growing need for teachers to equip themselves with adequate
digital competencies to use and teach AI in their teaching environments [4].
In this scenario in 2016 the Ministry of Education, University and Research launched the Italian
National Plan for Digital Education (Piano Nazionale Scuola Digitale — PNSD) for setting up a
comprehensive innovation strategy across Italy's school system and bringing it into the digital age.
As a part of this plan in 2018 the Territorial Training Teams (Équipe Formative Territoriali - EFT)
were constituted to encourage and support the exploration of new organizational models and
innovative teaching methods. Each Italian region has an EFT, whose members work both at regional
level by supporting schools and teachers, and at a national level by developing training projects. In
2022 the EFT became a part of the Italian National Recovery and Resilience Plan/Next Generation EU
program (PNRR).
In Piedmont, the EFT has always operated under the umbrella of the Regional Education Office
(USR), which supports organizational flexibility, teaching, educational research, and gathers local
needs to create training opportunities. Additionally, the USR is responsible for organizing and
disseminating courses and resources. Since its beginning the EFT of Piedmont has focused its efforts
on developing projects related to digital education, digital citizenship, media education, and Artificial
Intelligence (AI). The team also promotes, supports, and oversees the design and implementation of
training courses for teachers during the transition into the digital age. In this contribution, we will
present the evolution of teachers' training on Artificial Intelligence in recent years and the effect of
the training in classroom activities. Over the past five years, we have collaborated with local
educational institutions. We supported the testing of an educational app developed by the Polytechnic
of Turin [5]. Additionally, we worked with the University of Turin on applying AI to Astronomy [6]
and developing media literacy [7]. We also organized three editions of a team web-based competition
on AI with the Liceo Pellico-Peano training hub [8]. These collaborations resulted in training facilities
for teachers, which will be discussed along with other courses below.
2. Background and frameworks
The background and the frameworks of our project are outlined by the Ministry of Education
(Piano Scuola 4.0 [9]). Here we focus on some aspects about Artificial Intelligence in Education from
UNESCO [10] and the EU Digital Education Action Plan (DEAP) [11]. The actions that we promoted
can be classified in three different categories [12].
• Learning with AI involves using Artificial Intelligence-based tools in teaching to provide
personalized and adaptive learning experiences.
• Learning about AI includes acquiring knowledge and skills related to Artificial Intelligence
techniques (e.g. Machine Learning, ML) and AI technologies (e.g. Natural Language
Processing), as well as understanding statistics and coding.
• Learning for AI means ensuring that all citizens are ready for the possible impacts that
Artificial Intelligence may have on their lives. This involves helping them understand and
delve into ethics, potential data distortions, and the potential impact on jobs from a
human-centric perspective.
We will not cover the use of AI for analyzing data to understand how students learn, their
progress, effective learning paths, supporting admissions, planning learning programs, and
educational data mining.
In September 2020, the Digital Education Action Plan was adopted and subsequently two actions
about AI were implemented. Action 6 defined ethical guidelines on the use of AI and data in teaching
and learning for educators [13]; action 8 updated the European Digital Competence Framework to
include AI and data-related skills (DigComp 2.2 [14]). We used these documents as a reference when
planning our courses. We began by sharing their knowledge at the start of our meetings, and
eventually we demonstrated how our practical suggestions aligned with the examples and guidelines
outlined in these documents.
The InnovaMenti project [15] was launched in 2021 with the aim of training teachers in
educational methodologies. The project was developed by members of the EFT from all regions and
was structured into modules, each focusing on a different methodology: gamification, inquiry-based
learning, storytelling, tinkering, and hackathon. Each module included an introduction, an overview
of digital tools and resources for implementing classroom activities, and four easy-to-use kits
designed for different educational levels, from kindergarten to secondary school. All the modules
were designed in accordance with the areas and competency levels of DigCompEdu [16]. Initially, the
introduction was delivered through webinars, but the project later transitioned into a MOOC.
Participants were asked to experiment with the kits in their classrooms and share the outcomes of
the experimentation, such as students' projects. In the following years, the same approach was used
to design other training projects focused on new technologies, STEM, and other methodologies.
In early 2024, the "InnovaMenti_Intelligenza Artificiale" [17] project was launched. It introduces
an innovative framework for education that supports AI Education. The project not only applies to
the InnovaMenti approach to AI, but also presents AI in a way that strongly relates to our daily lives,
and it offers a valuable opportunity for educators and learners to gain insights into the integration of
AI in educational settings. The project aims to improve teachers' and students' understanding and
practical skills by demonstrating how this technology can be used in various situations and for
problem-solving. It is divided into four modules, each focusing on everyday activities that promote
learning through hands-on engagement. These modules are communicating (analyzing and creating
texts), seeing (classifying and generating images), feeling (interacting with the real and virtual
environment), and acting (managing complexity with automation). Each module is based on examples
of AI from DigComp 2.2.
3. Teacher training
The USR-EFT Piemonte training on AI began in 2020, with the first sessions taking place in
February at the PSD final at Liceo Cavour in Vercelli [18] and in March during the Riconnessioni
Project webinars [19]. However, the specific training on AI had to be paused due to the pandemic
emergency. During this time, the focus was on supporting teachers using basic online tools to engage
students while schools were closed. In this section, we will outline the courses we organized from
2021 to 2024. We will start by discussing the main topics and how they have evolved over the years.
Then, we will highlight some common features. In Table 1, we provide a summary of the courses,
including the main topics without translating the Italian titles.
All our courses have been designed for any teacher, with a special focus on those in the humanities
disciplines. Artificial Intelligence is a complex and rapidly advancing technology that includes a wide
range of concepts, methodologies, and applications; however, AI provides several opportunities for
cross-disciplinary projects. With this in mind, we have thoughtfully selected activities from both the
STEM and humanities sectors. During the first two years the training project focused on introducing
AI with these main goals: presenting resources and techniques to explain AI in simple terms (learning
about AI), introducing and discussing ethical aspects, and promoting awareness (learning for AI). In
the last two years, we have been training on specific tools and topics, introducing teaching
methodologies, and discussing the challenges and opportunities of AI in education (learning with AI).
In the first two years, the courses mainly focused on the first three areas of DigCompEdu. This
included emphasis on digital continuous professional development, selection, creation, and
modification of digital resources, planning and implementation of digital resources in the teaching
process, as well as experimenting with and developing new formats and pedagogical methods for
instruction. Recently, we have also extended other areas, specifically assessment strategies,
differentiation and personalization, active engagement of learners, information and media literacy,
responsible use, and digital problem-solving.
All the courses follow a similar structure, despite differences in the number of meetings and total
duration. They begin with a brief introduction to the topics, followed by a demonstration of tools for
implementing learning activities. Participants were then encouraged to use these tools under the
guidance of the trainers. Activities provided for various levels of digital competence and different
school grades. Teachers were also offered kits in the InnovaMenti style, which were ready and easy
to use in classrooms. If necessary, teachers could receive support from trainers during classroom
activities (see next section). Upon completion of the course, teachers were required to briefly share
their experiences with students by filling out reports, forms, or uploading images on an online board.
Table 1
List of all planned courses, including topics, number of sessions, competence level based on
DigCompEdu reference, and years. Further details like the original title are available on the USR and
EFT Piemonte websites. Trainers involved are listed in the footnotes; if no indication is written, the
authors themselves provided the training.
Topics Sessions Level Year
Data analysis and visualization; introduction to computational 3 B1 2021, 2022,
notebooks and Python programming 2023
History of AI; basic principles; ethics; image recognition and 4 B1 2021
classification; chatbots
Assessment; AI literacy 3 B1 2021
History of AI; basic principles; ethics and chatbots in 3 A2 2022, 2023
humanities1
Basic principles; ethics; image recognition and classification, 4 B1 2022, 2023
chatbots, and IoT applied to STEM2
Algorithms; Big Data; filter bubbles and echo chambers3 3 A2 2022, 2023
Gamification and AI applied to art and music4 2 A1 2022, 2023
Unplugged activities on games for kids5 1 A1 2023
Ethics and new technologies6 1 A1 2023
Models; basic principles; gaming and gamification; unplugged 4 A1 2023
tools7
Natural language processing and prompt engineering8 2 A1 2024
Image generation and STEM for kids 9
1 A1 2024
Storytelling and AI in teaching/learning languages10 4 B1 2024
AI and podcasts to promote reading11 2 A1 2024
Most of the courses were held online because they were offered to the entire region. We used the
Google Meet remote conferencing platform, which was provided by USR. When the number of
participants exceeded 100, and we needed less interaction with the participants as well as attendance
tracking, we used Cisco WebEx, also provided by USR. In the most recent courses, we began using
WebEx also with smaller groups of teachers because it allows for parallel sessions. While teachers
were always comfortable with Google Meet, they encountered some issues with WebEx. The courses
were offered in person only twice.
The courses were structured according to the six levels of digital competence development
outlined in the DigCompEdu progression model. Initially, we focused on courses for intermediate
level teachers (B1 Integrator). This level was chosen because the teachers already had some IT
knowledge, making it easier for them to use tools such as Learning Management Systems and remote
conference systems. Additionally, they were open to learning new ideas and tools and could
implement them autonomously. However, they were new to AI and required support in this area as
they were not proficient enough to learn about it on their own. Level B1 teachers have been shown
to be a good group to use for testing new tools before introducing them to a wider group of teachers.
1 Barbara Baldi, Luca Basteris, Emilia De Maria, Carlo Valentini
2
AP, Luca Basteris, Andrea Goia, Carlo Valentini
3 Luca Basteris, Carlo Valentini, Daria Romiti
4
Barbara Baldi, Luca Basteris, Germano Zurlo
5 AP, Maria Rosa Rechichi
6
AM, AP, Vera Tripodi (Politecnico di Torino)
7 AM, AP, Maria Rosa Rechichi, Carlo Valentini, Giulia Ballatore (Politecnico di Torino), Martina Vanelli (Politecnico di Torino)
8
Simonetta Siega in collaboration with EFT Lazio, EFT Lombardia, and EFT Toscana
9
Simonetta Siega in collaboration with EFT Lombardia, EFT Sicilia, EFT Umbria, and EFT Veneto
10
Raffaella Castellina, Mara Rechichi in collaboration with EFT Lazio, EFT Puglia, EFT Sicilia, EFT Toscana, and EFT Veneto
11 Maria Rosa Rechichi in collaboration with EFT Marche
Later, we began with initial level courses (A1 Newcomer and A2 Explorer) to engage a larger number
of teachers. The activities offered for these lower levels were also unplugged to offer resources and
techniques to explain AI in simple terms.
We initially began with teachers from secondary schools (grades 6+) because it was easier to
address topics such as the principles of AI and ethics. Recently, we have also introduced activities for
kindergarten teachers, focusing on how machines can perform tasks autonomously through hands-
on unplugged activities, mainly based on educational games. Alternatively, we have encouraged the
use of machines to bring children’s drawings or stories to life. In these cases, the online tools need to
be used by the teachers.
In all our courses, we emphasized the use of online platforms, comparing the various opportunities
available online; whenever possible, we recommended open-source tools. For some more details on
the tools and the platforms used, see the following references [20, 21].
Table 2
List of all classroom activities, including topics, school name, number of students and teachers
involved, number of sessions, year, and school grade. Trainers involved are indicated in the footnotes,
as well the number of classes, if it is larger than one. If no indication is written, the author AP provided
the training.
Topics School Year Stud. Teach. Sessions Grade
Basic principles, data LS Gobetti, Torino 2021 20 1 5 11
analysis, image recognition,
ethics
Basic principles, Natural LS Gobetti, Torino 2022 25 3 4 11
Language Processing,
chatbots, sentiment analysis
Basic principles, image LS Antonelli, Novara 2022 38 1 2 12
recognition, awareness1
Basic principles, image IIS Curie, Collegno 2022 18 2 3 10
recognition, awareness (TO)
Image recognition and IIS Darwin, Rivoli 2023 16 1 1 12
Astronomy (TO)
Basic principles and IC Bellini, Novara 2023 15 3 1 2
storytelling2
Image recognition and L Alberti, Novara 2023 36 7 1 10
generation in Art3
Basics principles, Deep Fake, L Pellico-Peano, 2024 49 2 3 10
awareness4 Cuneo
Generative AI and book IC Tommaseo, 2024 43 3 1 2-4
trailer5 Torino
Text 2 Image6 IC S. Ignazio, Santhià 2024 34 5 1 6-7
(VC)
Support to teaching mother DD2, Domodossola 2024 65 6 1 5
tongue language 7
(VB)
1 AP and Carlo Valentini, 2 classes
2
Maria Rosa Rechichi and Carlo Valentini
3 AP, Luca Basteris and Germano Zurlo, 2 classes
4
Luca Basteris, 2 classes
5
Maria Rosa Rechichi, 2 classes
6
Raffaella Castellina, 2 classes
7 Simonetta Siega, 3 classes
4. Classroom activities
Classroom activities were planned with some participants of the training courses who were
interested in experimenting with new ideas and tools in their classrooms but were not yet comfortable
doing so on their own. To address this, the trainers collaborated with the teachers to plan the activities
and co-taught the lessons. This allowed the trainers to handle any technical difficulties while the
teachers could focus on the educational aspects. Additionally, in this way the suggested kits were not
just theoretical tools but were implemented in the teachers' daily reality.
Details of all the activities are listed in Table 2. The initial projects were more structured than the
later ones because they involved a lot of experimentation for both students and teachers. They
consisted of numerous meetings where the focus was gradually moving from the fundamental
principles of AI (learning about AI) to the implementation of machine learning in current problems
(learning for AI). For example, this included analyzing chest x-rays for pneumonitis diagnosis and
applying Natural Language Processing to sentiment analysis. Over time, teachers became more
proficient and were able to conduct their own experimentations, so classroom activities became more
specialized and focused on applications or issues, such as the study of language (learning with AI). In
recent years, we also supported the use of kits from the national projects InnovaMenti, which were
designed for specific lesson activities.
The classroom activities had been designed not just for IT experts, but for all kinds of students.
They were suitable for both humanities and STEM courses. We began experimenting with dual
training (learning and working, PCTO) with students of grade 11+. In the past two years, we have
also started working with children in kindergarten.
The teaching methodologies we adopted varied depending on the context, students' age, and the
frequency of meetings. Storytelling and gamification were the most used methods, especially with
younger students. In our approach to storytelling, we focused on a technique that was employed in
one of the training modules of the InnovaMenti project [22]. The activity involved writing the
continuation or the end of a story based on the outcome of a machine learning classification. The goal
was to emphasize certain aspects of machine learning, including the potential effects of improper
training. This approach worked not only with children but also with older students. On the other
hand, gamification was primarily used with younger students, not only because it engaged them
effectively, but also because it served as a good starting point to explain in a simple way how a
machine can perform tasks in an automated manner.
In our initial activities, we extensively utilized online platforms, primarily for tasks such as image
classification and developing chatbots. As we progressed, we integrated offline tools into our
approach, incorporating unplugged activities for children whenever feasible and utilizing offline
programming tools for older students. At present, we mainly use online tools for specific lesson
activities focused on image classification or generative AI.
5. Conclusions and perspectives
The teacher training and the classroom activities seemed to respond to the needs. At the end of
every training course, participants filled out a feedback form. The format of these forms has changed
over the years to gather more detailed feedback from teachers, provide structured information, and
make it easier to compare activities across different regions. However, these changes have made it
difficult to compare certain data. Overall, teachers were satisfied with the courses, especially
regarding the topics covered and the clarity of the language used by the trainers. Many expressed
interests in delving deeper and improving, with some of them attending multiple courses to advance
from lower to higher levels of competence. Some teachers found certain topics too complex, but this
was mostly due to an inaccurate self-assessment of their skill level. While there are free tools available
to help teachers assess themselves [23], only a few of them use these resources. As a result, teachers
at an A1 level attended courses designed for B1 level. During online sessions, there were difficulties
in the practical part, where teachers were asked to both follow and complete exercises
simultaneously.
The InnovaMenti_Intelligenza Artificiale project introduces a new framework. It offers resources
and techniques to explain AI in simple terms, as well as AI literacy kits, educational games, and
evaluation tools. The project emphasizes creating best practices, and it was initially tested in
secondary schools but can now be expanded to include younger students. The modules are based on
everyday activities such as communication, observation, perception, and action, and they are
connected to examples from DigComp 2.2. This sets the foundation for interdisciplinary activities
that are well-suited for kindergarten and primary school. Given our experience over the past few
years, we are going to support teachers in their classroom activities and help spread these activities.
When introducing new innovations in schools, it is crucial to assess their impact [1]. It is not
enough to simply start using new methods and technologies; they must be integrated coherently with
the curriculum, students' knowledge, and teachers' skills. Teachers need support and tools to
incorporate these new technologies and methodologies into their lessons, which is important for both
the teaching and assessment aspects. The activities we proposed present challenges that require
students to combine knowledge and skills from various subjects. Implementing these innovations is
easier in primary schools than in secondary schools. However, there is currently no comprehensive
framework that seamlessly aligns teaching practices with pedagogical insights and assessment
strategies in nontraditional settings. Developing and implementing an effective evaluation model for
future projects will be essential.
6. Acknowledgements
We thank Silvana Rampone for reading and making suggestions.
7. References
[1] A. Haleem, M. Javaid, M. A. Qadri, R.Suman. Understanding the role of digital technologies in
education: A review. Sustainable operations and computers 3 (2022) 275-285.
doi:10.1016/j.susoc.2022.05.004
[2] T. Barrios. Teaching competencies for the 21st century. Academia Letters 3183 (2021).
doi:10.20935/AL3183.
[3] K. Zhang, A. B. Aslan. AI technologies for education: Recent research & future directions.
Computers and Education: Artificial Intelligence 2 (2021) 100025.
[4] D. T. K. Ng, J. K. L. Leung, J. Su, R. C. W. Ng, S. K. W. Chu. Teachers’ AI digital competencies
and twenty-first century skills in the post-pandemic world. Educational technology research and
development 71 (2023) 137-161.
[5] Progetto di ricerca SMaILE app, 2022 URL: https://www.istruzionepiemonte.it/progetto-di-
ricerca-smaile-simple-methods-for-artificial-intelligence-learning-and-education-incontro-di-
presentazione-del-20-09-2022-e-richiesta-adesioni/
[6] A. Piccione, A. A. Massa, M. L. Ruggiero, M. Serio, M. Rinaudo, D. Marocchi, T. Marino. Training
teachers on new topics and new tools in Physics education. Journal of Physics: Conference Series
2693 (2024) 012010. IOP Publishing.
[7] A. A. Massa and A. Piccione, Educazione ai media a scuola: cosa cambia con l'Intelligenza
Artificiale, 2024. URL: https://www.dfe.unito.it/do/avvisi.pl/Show?_id=qvk5
[8] USR Piemonte, Gara IA:giochIAmo, 2022. URL: https://www.istruzionepiemonte.it/ai-
giochiiamo-gara-nazionali-online-sulle-tematiche-dellintelligenza-artificiale-scadenza-adesioni-
lunedi-4-aprile-2022/
[9] MIM, DM 161/2022, 2022. URL: https://pnrr.istruzione.it/infrastrutture/scuole-4-0-nuove-aule-
didattiche-e-laboratori/
[10] F. Miao, W. Holmes, R. Huang, H. Zhang. AI and education: A guidance for policymakers. Unesco
Publishing, 2021. doi:10.54675/PCSP7350
[11] European Commission, Digital Education Action Plan 2021-2027, COM(2020) 624 final
30/09/2020. URL: https://education.ec.europa.eu/focus-topics/digital-education/action-plan
[12] W. Holmes, M. Bialik, C. Fadel. Artificial Intelligence In Education: Promises and Implications
for Teaching and Learning, Boston, MA, Center for Curriculum Redesign, 2019.
[13] European Commission: Directorate-General for Education, Youth, Sport and Culture, Ethical
guidelines on the use of artificial intelligence (AI) and data in teaching and learning for
educators, Publications Office of the European Union, 2022. URL:
https://data.europa.eu/doi/10.2766/153756
[14] R. Vuorikari, S. Kluzer, Y. Punie. DigComp 2.2: The Digital Competence Framework for Citizens
- With new examples of knowledge, skills and attitudes. EUR 31006 EN, Publications Office of
the European Union, Luxembourg, 2022. doi:10.2760/115376.
[15] Équipe Formative Territoriali, Progetto InnovaMenti, 2021.
URL: https://scuolafutura.pubblica.istruzione.it/innovamenti
[16] Y. Punie, C. Redecker. European Framework for the Digital Competence of Educators:
DigCompEdu , EUR 28775 EN, Publications Office of the European Union, Luxembourg, 2017.
doi:10.2760/159770.
[17] Équipe Formative Territoriali, Progetto InnovaMenti_Intelligenza Artificiale, 2024.
URL: https://scuolafutura.pubblica.istruzione.it/innovamenti_intelligenza-artificiale
[18] A. Piccione, Intelligenza Artificiale e apprendimento, 2020.
URL: https://www.cavourvercelli.it/index.php/scuola/istituto-professionale-lanino/rassegna-
stampa/da-gennaio-2020/anno-2020-1/225-file-2020-02-06-104-tgvercelli/file
[19] A. Piccione, Cos’è l’intelligenza artificiale?, Video, 2020.
URL: https://www.youtube.com/watch?v=PaTg3nPNcL4&t=4718s
[20] A. Piccione, A. A. Massa, E. Biolcati, S. Labasin, T. Marino, M. Tomba. Formazione docenti e
nuovi percorsi didattici sull’Intelligenza Artificiale, in Atti Convegno Nazionale Didamatica
2021, pp. 102 – 109.
[21] A. Piccione, A. A. Massa, B. Barbara, L. Basteris, E. De Maria, A. Goia, M. R. Rechichi, C.
Valentini. Formare i docenti per creare percorsi didattici sull’Intelligenza Artificiale. BRICKS
13(3) (2023) 43 – 50.
[22] Équipe Formative Territoriali, Progetto InnovaMenti_TECH, 2023.
URL: https://scuolafutura.pubblica.istruzione.it/mooc-innovamenti_tech
[23] A. Economou. SELFIEforTEACHERS. Designing and developing a self-reflection tool for
teachers’ digital competence. EUR 31475 EN, Publications Office of the European Union,
Luxembourg, 2023. doi:10.2760/40528.