AI Made by Youth: A Conversational AI Curriculum for Middle School Summer Camps

Source publication / research team or educational organization described in paper

Not specified in extracted text

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

• In-school (K-12)
• Informal learning

Curriculum design or implementation

Not specified in extracted text

lthough there are recent studies on conversational AI curricula and tools for K-12 learners (Van Brummelen, Heng, and Tabunshchyk 2021; Zhu and Van Brummelen 2021), they have primarily focused on online workshop; 0; Lin et al. 2020) or face-tracking (Jordan et al. 2021), to introduce AI to K-12 students. Conversational AIs are computer programs with the abil- ity to interact with humans using natural languages. Con- vers

LLM/Chat

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

6-8

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.
• As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engag- ing way.

• 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.

LLM/Chat

Language context discussed in source publication

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Outreach / informal learning.
• AI capability focus: LLM/Chat.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.

• 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.

• Unplugged learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Unplugged 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.
• By learning about conversational AIs, learners are introduced to AI con- cepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human inter- actions.

• By learning about conversational AIs, learners are introduced to AI con- cepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human inter- actions.
• In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers de- velop their own conversational agents.
• The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning.

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engag- ing way. One way to do so is to leverage familiar and per- vasive technologies such as conversational AIs.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.

• 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
• Curriculum / course design
• Outreach / informal learning
• LLM/Chat

• Completed

6-8

In-school (K-12), Informal learning

LLM/Chat

Unplugged learning

Low to Medium

Medium

AI Made by Youth: A Conversational AI Curriculum for Middle School Summer Camps

• Yukyeong Song
• Gloria Ashiya Katuka
• Joanne Barrett
• Xiaoyi Tian
• Amit Kumar
• Tom McKlin
• Mehmet Celepkolu
• Kristy Elizabeth Boyer
• Maya Israel

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26882

https://ojs.aaai.org/index.php/AAAI/article/view/26882

https://ojs.aaai.org/index.php/AAAI/article/view/26882/26654

073_AI Made by Youth_ A Conversational AI Curriculum for Middle School Summer Camps.pdf

9

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engag- ing way. One way to do so is to leverage familiar and per- vasive technologies such as conversational AIs. By learning about conversational AIs, learners are introduced to AI con- cepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human inter- actions. In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers de- velop their own conversational agents. The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning. We conclude with a discussion of how conversational AI can be used as an entry point to K-12 AI education.

• In-school (K-12)
• Informal learning

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• 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.