Why Your AI Tool Still Feels Like a Digital Worksheet
—and how to smash the “functional fixedness” trap in edtech.
1 | The déjà-vu of substitution
You invest in a shiny new AI assistant, open MagicSchool, Khanmigo, or ChatGPT, and within minutes, you’re churning out the same worksheets you used to type in Word. The tool is faster, but the learning experience is barely different. When this happens, you’ve probably hit the psychological speed-bump known as functional fixedness.
2 | What is functional fixedness?
First described by Gestalt psychologist Karl Duncker, functional fixedness is our tendency to see objects (or tools) only in the way we’ve always used them. In Duncker’s famous candle problem, participants struggled to realise the box of tacks could become a candle-holder because they were fixated on its original purpose as a container. When the tacks were removed from the box, almost everyone solved the task—proving the obstacle was in their mental model, not their skill set.
The Candle Problem; Karl Duncker
The same mindset shows up in classrooms. Ask AI to “write ten comprehension questions”, and you’ve simply swapped your pen for a chatbot.
3 | The SAMR lens: stuck at ‘S’
Ruben Puentedura’s SAMR model categorises tech use into four rungs: Substitution, Augmentation, Modification and Redefinition. Most teachers linger on the first two levels: PDFs instead of photocopies; recorded lecture instead of live talk.
Functional fixedness helps explain why we plateau. We see AI primarily as a faster authoring tool rather than a partner capable of reshaping pedagogy.
4 | Spotting fixedness in today’s AI tools
Table generated in ChatGPT o3
5 | Breaking the box: five moves
Verbal judo – Re-name the tool’s role. Instead of “worksheet generator”, call ChatGPT a “learning pathway architect” and ask, “Design three radically different routes to the same outcome.”
SAMR remix – Force yourself to prototype one activity at each SAMR level. Can you redefine learning so pupils do something impossible without AI—e.g., co-write a multilingual podcast transcript with instant accuracy checks?
Function hunting – List every component of the tool’s interface and brainstorm two alternative functions. (In the candle problem, the box became a shelf.)
Evidence pairing – Marry the AI with a proven strategy such as retrieval practice or dual coding. Ask MagicSchool to produce spaced flash-cards, not a one-off list.
Time-boxed sprints – Limiting exploration to 30 minutes reduces the overwhelm and nudges creative risk-taking. Overly open-ended tinkering often defaults back to safe, familiar outputs.
6 | Mini case study: from worksheet to re-definition in 20 minutes
Substitution – Teacher prompts ChatGPT: “Write 10 Newton-law questions.”
Augmentation – Adds auto-generated solutions.
Modification – AI (Magicschool) converts each question into a “confident/unsure” branching quiz; immediate feedback alters the next item’s difficulty.
Redefinition – Exports quiz data to NotebookLM, which drafts a metacognitive reflection guide. Pupils analyse their own misconception patterns and plan next-step experiments.
The end product is no longer a worksheet—it’s a self-regulating learning loop, impossible without AI.
7 | Pulling it together
Functional fixedness is not a tech problem; it’s a cognitive bias. By naming it, applying the SAMR ladder, and deliberately rehearsing alternative functions, teachers can unlock the deeper promise of AI: time recouped, feedback personalised, and tasks re-imagined, not just digitised.
Next step
Download our 1-page “SAMR × AI Reflection Sheet”. Tick off where your current lesson sits, jot one idea to climb a rung, and share your best ‘box-breaking’ moment.
(Download link coming in the next post.)
