Evaluation of an Online Intervention to Teach Artificial Intelligence with LearningML to 10-16-Year-Old Students
Abstract
The inclusion of artificial intelligence (AI) in education is increasingly highlighted by international organizations and governments around the world as a cornerstone to enable the adoption of AI in society. That is why we have developed LearningML, aiming to provide a platform that supports educators and students in the creation of hands-on AI projects, specifically based on machine learning techniques. In this investigation we explore how a workshop on AI and the creation of programming projects with LearningML impacts the knowledge on AI of students between 10 and 16 years. 135 participants completed all phases of the learning experience, which due to the COVID-19 pandemic had to be performed online. In order to assess the AI knowledge we created a test that includes different kinds of questions based on previous investigations and publications - resulting in a reliable assessment instrument. Our findings show that the initiative had a positive impact on participants? AI knowledge, being the enhancement especially important for those learners who initially showed less familiarity with the topic. We observe, for instance, that while previous ideas on AI revolve around the term robot, after the experience they do around solve and problem. Based on these results we suggest that LearningML can be seen as a promising platform for the teaching and learning of AI in K-12 environments. In addition, researchers and educators can make use of the new instrument we provide to evaluat…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Rodríguez-García et al. selected and modified 14-item questions from other available tests and online resources, such as Machine Learning for Kids website and MOOC platforms on AI.
“Rodríguez-García, Moreno-León, Román-González, & Robles, 2021 selected and modified 14-item questions from other available tests and online resources, such as Machine Learning for Kids website and MOOC platforms on AI.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Rodríguez-García et al. (2021)
Students' AI knowledge acquisition and retention of AI skills was assessed in Lin et al. (2021), Wan et al. (2020) and Rodríguez-García et al. (2021) studies via some pre-post knowledge tests.
“Students' AI knowledge acquisition and retention of AI skills was assessed in Lin et al. (2021), Wan et al. (2020) and Rodríguez-García, Moreno-León, Román-González, & Robles, 2021 studies via some pre-post knowledge tests.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Rodríguez-García et al. (2021)
Cites
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · cited 3× · see citing works
“such results may seem counterintuitive at first sight, they are in line with previous investigations positing that AI literacy is independent from computational literacy [28].”
BackgroundResult
Flagged first, then by citations
Cited by
Sorted by citations
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · see citing works
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100014 · Cited by 488 · see citing works
Examining artificial intelligence literacy among pre-service teachers for future classrooms
Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100179 · Cited by 238 · see citing works
Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100178 · Cited by 228 · see citing works
A systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212 · see citing works
Meta AI literacy scale: Further validation and development of a short version
Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39686 · Cited by 44 · see citing works
- Authors
- Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G.
- Affiliations
- Instituto Nacional de Tecnologías Educativas y Formación del Profesorado, Madrid, Spain
- Programamos, Sevilla, Spain
- Universidad Nacional de Educación a Distancia, Madrid, Spain
- Universidad Rey Juan Carlos, Madrid, Spain
- ORCID
- Moreno-León, Jesús
- Venue
- Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
- Publisher
- ACM
- Published
- 2021-03-03
- Pages
- 177-183
- OpenAlex
- W3134442660
- Cited by
- 104
- References
- 1
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 35 · 2023: 17 · 2022: 14 · 2021: 4 · 2020: 1
- FWCI
- 14.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Keywords
- artificial intelligence in education, online intervention, K-12 students, machine learning education, programming education, COVID-19 pandemic
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
