# Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

arXiv: https://arxiv.org/abs/2009.10228 · HTML: https://mlsanity.com/paper/2009.10228/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Constructing Dreams using Generative AI](https://mlsanity.com/paper/2305.12013/) (2023)
- [Unpacking Human-AI interactions: From interaction primitives to a design space](https://mlsanity.com/paper/2401.05115/) (2024)
- [Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration](https://mlsanity.com/paper/2407.16887/) (2024)
- [Collaborative Machine Learning Model Building with Families Using Co-ML](https://mlsanity.com/paper/2304.05444/) (2023)
- [Teaching Machine Learning in K-12 Computing Education: Potential and Pitfalls](https://mlsanity.com/paper/2106.11034/) (2021)
- [What Students Can Learn About Artificial Intelligence -- Recommendations for K-12 Computing Education](https://mlsanity.com/paper/2305.06450/) (2023)
- [A Review of Digital Learning Environments for Teaching Natural Language Processing in K-12 Education](https://mlsanity.com/paper/2310.01603/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and (2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how core competencies of AI literacy are applied successfully and organize them into an educator-friendly chart to enable educators to efficiently find appropriate resources for their classrooms. We also identify future opportunities and K-12 specific design guidelines, which we synthesized into a conceptual framework to support researchers, designers, and educators in creating K-12 AI learning experiences.