AI Chef Trainer: Introducing Students to the Importance of Data in Machine Learning

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)

Classroom, course, or resource-based AI education activity

Not specified in extracted text

ce in how AI systems learn and adapt over time based on new data. Forty-five of 52 students entered recipes, and 26 of the 52 tested their own recipes using the specific ingredients they entered. Students were i; students at a STEM public charter school in a major Texas city. The school had 665 students in grades 6–8 (aged 10–14 years). Of these, parents of 222 students consented to their children’s participation and 124; school had 665 students in grades 6–8 (aged 10–14 years). Of these, parents of 222 students consented to their children’s participation and 124 students participated in one of the two afterschool program session

ML concepts / supervised learning

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

K-5, 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.
• We developed AI Chef Trainer, an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation.

• 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.

ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Outreach / informal learning.
• AI capability focus: ML concepts / supervised learning.

• 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.

• Hands-on / experiential 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: Hands-on / experiential 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.
• After observing the recommen- dations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name— which were used to retrain the model and finally re-tested recipe suggestions.

• After observing the recommen- dations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name— which were used to retrain the model and finally re-tested recipe suggestions.

We developed AI Chef Trainer, an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation. We tested our software with middle school students.

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

• Design / conceptual evidence

• 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.

• AI literacy
• Conceptual understanding
• Outreach / informal learning
• ML concepts / supervised learning

• Completed

K-5, 6-8

In-school (K-12)

ML concepts / supervised learning

Hands-on / experiential learning

Low to Medium

Medium

AI Chef Trainer: Introducing Students to the Importance of Data in Machine Learning

• Saniya Vahedian Movahed
• Fred Martin

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35196

https://ojs.aaai.org/index.php/AAAI/article/view/35196

https://ojs.aaai.org/index.php/AAAI/article/view/35196/37351

033_AI Chef Trainer_ Introducing Students to the Importance of Data in Machine Learning.pdf

8

We developed AI Chef Trainer, an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation. We tested our software with middle school students. The re- sults indicated that the students recognized the importance of both data quantity and specificity in the training process. Initially, students tested the AI Chef’s capabilities by select- ing from a list of ingredients to see what the system recom- mended as possible recipes. After observing the recommen- dations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name— which were used to retrain the model and finally re-tested recipe suggestions. This cyclical process of testing, contribut- ing, retraining, and post-training testing provided students with hands-on experience in how AI systems learn and adapt over time based on new data. Forty-five of 52 students entered recipes, and 26 of the 52 tested their own recipes using the specific ingredients they entered. Students were introduced to the concept of confidence percentages via the AI recipe suggestions. Even as the primary focus was on the data’s role in ML, the AI Chef Trainer software also served as a window into students’ cultural expression and personal preferences. Live Demo — tinyurl.com/AI-Chef Source Code — github.com/saniavn/AI-Chef-Trainer

• 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.