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Building AI Awareness & Readiness: A Spiral Standards-Based K-12 CS Curriculum

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HBGCC - Posters, Table 42

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Session description

A comprehensive approach to teaching AI in a K-12 spiral standards-based Computer Science curriculum: Level Up through Digital Discoveries. We’ll explore how AI concepts, tools, and ethical considerations can be progressively introduced, empowering students to prepare for the future through age-appropriate skills and real-world applications.

Outline

1. Introduction: The Importance of AI in K-12 Education
1.1 Overview of the significance of AI in today’s educational landscape, and why it should be integrated progressively across K-12. Highlight existing US and international standards in AI education.
1.2 Briefly introduce the concept of a spiral curriculum and how it aligns with developmental learning.
2. How to Integrate AI in a Spiral K-12 Curriculum
2.1 Provide an overview of how AI can be progressively integrated into the K-12 curriculum, starting from simple AI awareness and simple usage activities in lower grades to advanced AI concepts and applications in high school. Briefly share Binary’s experience in different countries.
2.2 Break down specific content for three levels:
Elementary (1-5): Understanding AI concepts through hands-on examples like voice assistants and basic coding.
Middle School (6-8): Introducing data, algorithms, and AI’s role in daily life.
High School (9-12): Advanced topics like machine learning and AI ethics.
3. Identifying AI Tools and Resources for Practical Student Engagement
3.1 Present a curated list of AI tools, apps, and platforms that can be used in classrooms for different age groups.
3.2 Highlight resources that include ethical AI practices. - Show a brief demo of one AI tool.
4. Teaching Ethical AI: Bias, Privacy, and Societal Impact
4.1 Discuss strategies for embedding ethical AI principles into lessons, including issues around bias in data, privacy concerns, and the societal impact of AI.
4.2 Share case studies of AI misuse (e.g., biased algorithms) and lead a brief discussion on how educators can guide students to think critically about AI through a real-world ethical dilemma in AI (e.g., facial recognition in schools).
5. Evaluating Readiness for AI Integration in Schools
5.1 Discuss assessment of a school’s or district’s readiness to integrate AI education, including access to technology, teacher professional development, and student needs.
5.2 Provide steps for schools to enhance AI education readiness.
6. Closing & Q&A
6.1 Recap key takeaways and encourage participants to take immediate steps in integrating AI.
6.2 Point participants to additional resources and support for AI education.
Discussion and peer-to-peer interaction are essential, although time is limited.

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Supporting research

CSTA & TeachAI. (2024). Guidance on the future of computer science education in an age of AI. https://teachai.org/cs.
Mills, K , Ruiz, P , Lee, K , Coenraad, M , Fusco, J , Roschelle, J & Weisgrau, J (2024). AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. https://doi.org/10.51388/20.500.12265/218
Shuchi, G.. (2024). Teaching AI to K-12 Learners: Lessons, Issues, and Guidance. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). Association for Computing Machinery, 422–428. https://doi.org/10.1145/3626252.3630937
TeachAI.org. (2023). AI guidance for schools toolkit. https://teachai.org/toolkit.
Touretzky, D., Gardner-McCune, C., & Seehorn, D. (2023). Machine learning and the five big ideas in AI. International Journal of Artificial Intelligence in Education, 33(2), 233-266.
UNESCO. (2024). AI competency framework for students. https://unesdoc.unesco.org.
UNESCO. (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula. https://unesdoc.unesco.org.

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Presenters

Andreas Tsouchlaris

VP, Academic & Research

Binary Logic SA

aris kypriotis

Operations Manager

Binary logic

Spyros Tsouchlaris

Instructional Standards Lead

Binary Logic SA

Session specifications

Topic:

Computer Science and Computational Thinking

Grade level:

PK-12

Audience:

District Level Leadership, School Level Leadership, Teacher

Attendee devices:

Devices not needed

Subject area:

Computer Science, Technology Education

ISTE Standards:

For Students:
Computational Thinker

Formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.

For Educators:
Facilitator

Create learning opportunities that challenge students to use a design process and/or computational thinking to innovate and solve problems.

Disclosure:

The submitter of this session has been supported by a company whose product is being included in the session

Influencer Disclosure:

This session includes a presenter that indicated a “material connection” to a brand that includes a personal, family or employment relationship, or a financial relationship. See individual speaker menu for disclosure information.