CoChat
Sign Up
Cited by 132Teaching and Learning ProgrammingArtificial Intelligence in EducationDigital literacy in education

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Teachers’ Perspective on Artificial Intelligence Education: an Initial Investigation

Open access
Polak, Sara, Schiavo, Gianluca, Zancanaro, Massimo·2022·CHI Conference on Human Factors in Computing Systems Extended Abstracts
DOI 10.1145/3491101.3519866
Open this paper in CoChat — free account

Abstract

Learning about Artificial Intelligence (AI) from a young age can help students become competent citizens able to move through our increasingly digital world with confidence and responsibility. This contribution presents a preliminary investigation in Bulgaria, Greece, Italy, and Romania to understand middle school teachers’ perspective on how to best teach digital competencies for AI. It uses the Will, Skill, Tool model as a theoretical lens, and it aims to inform the design of educational content and online platforms to enable teachers to integrate AI education into their classroom. Through a human-centred design process – including focus groups and a survey – needs and requirements were identified for a supportive online educational platform that aids teachers in AI education. The research results showed a positive attitude towards AI education and high motivation to introduce AI-related content at school, which translates to a positive Will factor. Regarding the Skill factor, teachers seem to have a basic level of digital skills but low AI-related skills. No significant problems emerged regarding the availability of resources, but further research investigating whether the Tool factor is accounted for would be desirable. Based on the results, six design implications for a web-based educational platform on AI have been formulated: (i) provide the required basics; (ii) make it relevant; (iii) make it interactive and collaborative; (iv) keep everyone in the loop; (v) make it accessible; (vi) motivate the user. These implications are further discussed to extend computational thinking frameworks to incorporate AI-related concepts and perspectives.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3491101.3519866

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 16 references

Research works printed but not linked: 5

  1. Stephanie Carretero , Riina Vuorikari , and Yves Punie . 2017. The Digital Competence Framework for Citizens . Publications Office of the European Union( 2017 ). Stephanie Carretero, Riina Vuorikari, and Yves Punie. 2017. The Digital Competence Framework for Citizens. Publications Office of the European Union(2017).
  2. Tamara Heck , Luzian Weisel , and Sylvia Kullmann . 2021. Information Literacy and Its Interplay with AI . In Learning Information Literacy across the Globe. Frankfurt Am Main, May 10th 2019 . 129–131. Tamara Heck, Luzian Weisel, and Sylvia Kullmann. 2021. Information Literacy and Its Interplay with AI. In Learning Information Literacy across the Globe. Frankfurt Am Main, May 10th 2019. 129–131.
  3. Gerald Knezek , Rhonda Christensen , Roger Hancock , and Al Shoho . 2000 . Toward a Structural Model of Technology Integration . In Proceedings of the Annual Hawaii Educational Research Association. Gerald Knezek, Rhonda Christensen, Roger Hancock, and Al Shoho. 2000. Toward a Structural Model of Technology Integration. In Proceedings of the Annual Hawaii Educational Research Association.
  4. Natalie Lao . 2020. Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens. Ph. D. Dissertation . Massachusetts Institute of Technology Cambridge , MA , USA. Natalie Lao. 2020. Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens. Ph. D. Dissertation. Massachusetts Institute of Technology Cambridge, MA, USA.
  5. Xiaofei Zhou , Jessica Van Brummelen , and Phoebe Lin . 2020. Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework. arXiv:2009.10228 [cs] (Sept . 2020 ). arxiv:2009.10228 [cs] Xiaofei Zhou, Jessica Van Brummelen, and Phoebe Lin. 2020. Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework. arXiv:2009.10228 [cs] (Sept. 2020). arxiv:2009.10228 [cs] · arXiv

Cited by

Sorted by citations

Items 101–132 of 132

Related

See all

Metadata

Authors
Polak, S., Schiavo, G., & Zancanaro, M.
Affiliations
  • University of Trento (IT)
  • Fondazione Bruno Kessler (IT)
ORCID
Venue
CHI Conference on Human Factors in Computing Systems Extended Abstracts
Publisher
ACM
Published
2022-04-27
Pages
1-7
DOI
10.1145/3491101.3519866
OpenAlex
W4225141765
Cited by
132
References
16
Citations by year
2026: 26 · 2025: 40 · 2024: 49 · 2023: 12 · 2022: 5
FWCI
34.58
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Education › Digital literacy in education
Keywords
artificial intelligence in education, teacher perceptions, junior high school teachers, digital skills, digital competence of teachers, AI skills gap, human-centered design, computational thinking, teacher motivation, educational content design

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review