Paving the Way for Novices: How to Teach AI for K-12 Education in China

Source publication / research team or educational organization described in paper

China

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Course implementation or course design

Not specified in extracted text

ools, universities, and industry in designing and implementing AI courses for K-12 students, as shown in Figure 3. Fol- lowing this cooperation model, we have launched a num- ber of courses and modules online fo

AI literacy / AI concepts

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• In response to the trend that artificial intelligence (AI) is be- coming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

AI literacy / AI concepts

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design.
• AI capability focus: AI literacy / AI concepts.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational dis- parities.

• The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational dis- parities.

In response to the trend that artificial intelligence (AI) is be- coming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important. The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational dis- parities.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Engagement / motivation
• Curriculum / course design
• Learning tool / resource design
• AI literacy / AI concepts

• Completed

K-12

In-school (K-12)

AI literacy / AI concepts

Instructional / curriculum-based learning

Low to Medium

Medium

Paving the Way for Novices: How to Teach AI for K-12 Education in China

• Jiachen Song
• Linan Zhang
• Jinglei Yu
• Yan Peng
• Anyao Ma
• Yu Lu

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 No. 11, EAAI-22

2022

10.1609/aaai.v36i11.21565

https://ojs.aaai.org/index.php/AAAI/article/view/21565

https://ojs.aaai.org/index.php/AAAI/article/view/21565/21314

104_Paving the Way for Novices_ How to Teach AI for K-12 Education in China.pdf

6

In response to the trend that artificial intelligence (AI) is be- coming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important. The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational dis- parities. However, the AI knowledge and technical skills are still limited for not only students but also the school teach- ers. Furthermore, many local schools in China, especially in the rural areas, are lack of the necessary software and hard- ware for teaching AI. Hence, we designed and implemented a structured series of AI courses, built on an online block- based visual programming platform. The AI courses are free and easily accessible for all. We have conducted the experi- mental classes in a local school and collected the results. The results show that the learners in general gained significant learning progress on AI knowledge comprehension, aroused strong interests in AI, and increased the degree of satisfaction towards the course. Especially, our practices significantly in- creased computational thinking of the students who were ini- tially staying at a lower level.

• In-school (K-12)

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.