Design of AI + Curriculum for Primary and Secondary Schools in Qingdao
Abstract
We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things; therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality.
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?
Han et al. (2018) enhanced students' scientific and technological knowledge which then was applied in scientific research-based learning to solve practical problems.
“For example, Han et al. (2018) enhanced students' scientific and technological knowledge which then was applied in scientific research-based learning to solve practical problems.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Han et al. (2018)
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
AI literacy in K-12: a systematic literature review
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023) · International Journal of STEM Education · doi:10.1186/s40594-023-00418-7 · Cited by 724 · see citing works
“…be based on clearly defined competencies in a particular AI literacy framework, such as those proposed in the literature (Alexan-dre et al., 2021; Han et al., 2019; Long & Magerko, 2020; Malach & Vicherková, 2020; Micheuz, 2020; Ottenbreit-Leftwich et al., 2021; Touretzky et al., 2019a; Wong et…”
Background<scp>AI</scp> Literacy: Definition, Teaching, Evaluation and Ethical Issues
Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.487 · Cited by 537 · see citing works
Using digital story writing as a pedagogy to develop AI literacy among primary students
Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100054 · Cited by 266 · see citing works
- Authors
- Han, X., Hu, F., Xiong, G., Liu, X., Gong, X., Niu, X., Shi, W., & Wang, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Qingdao Academy of Intelligent Industries (CN)
- Beijing Normal University (CN)
- Venue
- 2018 Chinese Automation Congress (CAC)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 4135-4140
- OpenAlex
- W2914333217
- Cited by
- 40
- References
- 0
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 10 · 2023: 6 · 2022: 6 · 2021: 2 · 2020: 2
- FWCI
- 0.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Chinese Automation Congress (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 Technology and Pedagogy
- Keywords
- artificial intelligence in education, curriculum development, primary and secondary schools, AI literacy, computational thinking, project-based learning, practical skills training, student quality
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
