1Reflect on where AI/algorithmic systems appear in food/ag systems and daily life, and analyze potential consequences (i.e., benefits, harms, and tradeoffs) for stakeholders and their intended career paths.

2Explain foundational concepts of AI/algorithmic systems (data, models, training/validation, uncertainty) and social/ethical concepts (i.e., privacy, bias, accountability, transparency) that shape AI-enabled workflows in food/ag contexts.

3Describe key debates about AI capabilities, strengths, biases, and limitations, including automation bias, "black box" concerns, and generative AI reliability, especially in regulated or high-consequence settings (i.e., food safety/quality).

4Evaluate AI/algorithmic outputs for quality and trustworthiness using evidence-based checks (i.e., data provenance, reasonableness checks, triangulation, uncertainty, documentation) and identify risks/unintended impacts.

5Make informed and ethical decisions about when/how to use AI tools in food supply chain consistent with academic integrity and relevant professional/institutional policies.

6Communicate transparently about AI use by documenting tools, prompts/inputs, assumptions, verification steps, and how AI influenced decisions, following academic/disciplinal conventions.

7Apply AI literacy to food/ag case studies (i.e., R&D, safety/quality, manufacturing, supply chain, sustainability) and deliver a project pitch proposing an AI-enabled solution with a verification and governance plan.