They’re Not Necessarily Trying To…
Source: AI+Edu=Simplified
Author: Lance Eaton
Original source: https://aiedusimplified.substack.com/p/theyre-not-necessarily-trying-to
Published: 2025-11-13
Source type: interview
Private backup: the full article text is archived in the private repository at archives/articles/aiedusimplified-substack-com-theyre-not-necessarily-trying-to.source.md. It is not published on the public Quartz site.
Summary
This interview with Tawnya Means focuses on play, agency, student clarity, and meaningful AI use rather than treating student AI use primarily as cheating. Means argues that students often turn to AI for efficiency when assignments lack visible purpose, so educators should clarify expectations, explain value, and model how to think with AI through research, drafting, verification, and iterative revision. The conversation highlights AI’s capacity to help students build real artifacts, explore multiple perspectives, and follow curiosity while preserving human relationships as essential for prompting, nudging, ethical development, and deeper learning. AI becomes a catalyst for more purposeful assessment, agency-rich work, and relational education.
Pull quotes
Purpose comes before experimentation
“You have to tie it back to purpose: What is this intended to do? What’s its value? Then you combine that reflection with active experimentation. AI gives us more opportunities to make those connections than we’ve ever had before.”
Efficiency can masquerade as cheating
“They’re not necessarily trying to cheat; they’re just being efficient.”
Human connection pushes learning deeper
“That human connection allows for prompting, modeling, nudging—pushing deeper in a way AI can’t replicate.”
Big ideas
- Students need to bring the purpose; AI should not supply it for them
- Learning still needs some struggle, even when AI can make things easier
- Schools should start with learning values before choosing AI tools
Claims
- AI-assisted homework requires redesign, not just policing
- AI literacy should teach students what to do with AI, not just what to think about it
Key evidence and examples
- Faculty workshops emphasize hands-on experimentation so participants can connect aspirational ideas to actual tool behavior.
- A student app-building project produced functional apps in eight weeks, moving beyond mockups or written descriptions.
- Means describes a workflow that uses AI for research, source verification, drafting, and multiple rounds of revision while preserving human voice and judgment.
- The interview emphasizes teachers’ role in asking follow-up questions, noticing bias, pushing deeper, and helping students make meaning.
Education relevance
Relevant to assessment redesign, student AI use policies, faculty development, and pedagogy because it shifts the frame from detection and prohibition toward purpose, process, modeled AI workflows, and relational teaching.
Durability note
The article is durable as a pedagogy frame: it treats student AI use as a design, purpose, and relationship problem rather than only a detection problem.