If We’re Going to Adapt to the Age of AI, We Need to Chip Away at Transactional Education
Source: Higher AI Substack
Author: Higher AI
Original source: https://higherai.substack.com/p/if-were-going-to-adapt-to-the-age
Source type: essay
Private backup: the full article text is archived in the private repository at archives/articles/higherai-substack-com-if-were-going-to-adapt-to-the-age.source.md. It is not published on the public Quartz site.
Summary
The author argues that the main barrier to adapting education to GenAI is not technical illiteracy or rapid technological change, but the transactional model of education. Drawing on Jack Schneider and Ethan Hutt’s Off the Mark, the article describes a system in which students trade products for grades, grades for credentials, and credentials for employment. In that model, learning becomes incidental and students reasonably seek maximum return on investment. GenAI fits this logic because it offers shortcuts to grade-bearing products, so educators must chip away at transactional structures through alternative assessment, ungraded learning-only work, self-assessment, and edit-to-mastery structures.
Big ideas
- Learning still needs some struggle, even when AI can make things easier
- Schools should start with learning values before choosing AI tools
- Students need to bring the purpose; AI should not supply it for them
- District AI work is a long-term redesign project
Claims
- AI can make school feel more transactional
- Student AI misuse may signal pressure or unclear purpose
- Learning requires some productive struggle that AI can remove
- AI-assisted homework requires redesign, not just policing
- In an AI world, assessment should focus on watching students think
Key evidence and examples
- The article quotes Schneider and Hutt’s description of education as valuable because it can be traded for something else.
- It describes the chain of student products to grades to degrees or certifications to employment.
- The author gives classroom examples of students asking what they can submit or what extra credit they can do to raise a grade.
- Proposed interventions include learning-only assignments, self-assessment, edit-to-mastery, and Complete/Incomplete structures tied to learning objectives.
Pull quotes
The real obstacle
It’s the transactional model of education.
AI fits the transaction
GenAI plays directly into the logic of transactional education.
Toward learning, not grades
They’re steps towards a classroom that focuses more on learning, and less on grades.
Education relevance
Very relevant for grading reform, ungrading, mastery learning, AI-era academic integrity, and reframing student AI misuse as a rational response to incentive structures.
Durability note
This piece is most durable as a grading-and-incentives argument: specific AI tools will change, but students will keep responding rationally to systems that reward product completion over learning.