Designing AI-Allowed Assignments That Invite Curiosity
A reusable design pattern for AI-allowed assignments that separates automatable production from non-delegable inquiry, defended by a worked example and a boundary check.
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How can teachers redesign assignments so AI can do the routine work, yet curiosity-driven thinking remains the student's own work?
A student's essay that an AI could write in seconds—and a question that still needs the student's own thinking.
If AI can produce the assignment, what does the assignment actually test—and what part is still genuinely the student's?
Side-by-side comparison of an AI-flat assignment and a curiosity-sparking one, showing where original thinking must move.
A concrete design pattern: AI handles the draftable surface, while the student's job becomes choosing, defending, and extending what only they can do.
The best way to stop AI cheating is to ban AI or make questions harder; curiosity has no room left once AI is allowed.
- AI detection tools and plagiarism enforcement
- Specific policy or institutional rules for AI use
- Prompt-engineering tutorials for students
- Subject-matter content unrelated to assignment design
- 01When AI Can Write the Essay, What Is the Assignment For?slideQuestion
Open with the core tension: a typical essay prompt and a student's instant AI draft, then frame the driving question about redesigning for curiosity.
- Show a prompt an AI completes in seconds
- Name the unspoken assumption that 'more work' equals 'more learning'
- Pose the driving question clearly on screen
- 02Your First GuessquizPrediction
Ask the learner to commit to one main move before the investigation explains the design pattern.
- Commit to a single design strategy
- Make an implicit assumption explicit
- 03Compare Two Assignments Side by SideinteractiveEvidence
Let the learner toggle between an AI-flat essay prompt and a redesigned prompt, and see which cognitive moves survive when AI is used.
- Toggle 'AI allowed' to see how each prompt collapses
- Highlight which steps remain student-only in the redesign
- Notice the moved cognitive load
- 04What AI Can Do in Seconds vs What It Cannot InferslideEvidence
Concrete examples showing which parts of an assignment an AI completes instantly and which parts depend on the student's local context and choices.
- Summarize a stable text: AI-ready
- Defend a personal choice tied to local evidence: student-only
- Generate a generic argument: AI-ready
- Justify why this argument fits this case: student-only
- 05The Design Pattern: Shift Load, Keep CuriosityslideExplanation
Introduce the reusable pattern that moves cognitive load from production to justification, extension, and local evidence.
- Move the prompt from 'produce X' to 'defend a specific X you chose'
- Require local evidence the AI cannot guess
- Add an extension step that asks the student to react to the AI draft
- Keep one open question with no single right answer
- 06Redesign Your Own AssignmentinteractiveTransfer
Give the learner a draft assignment prompt and let them apply the four-move pattern to produce an AI-allowed, curiosity-sparking version.
- Start with a familiar assignment type
- Add a 'specify the choice' move
- Attach a local evidence requirement
- Insert an open-ended question the AI cannot resolve
- 07Where the Pattern Stops WorkingslideBoundary
Show the limits: highly standardized exams, pure recall tasks, and assignments with no local context still collapse under AI even with the pattern applied.
- Standardized recall tests lack local evidence to require
- Subjects with no shared text to defend against
- The pattern improves design but does not replace assessment redesign
- 08Curiosity Survives AI When the Question Is Yours to AnswerslideResolution
Answer the driving question directly and restate the pattern as a portable rule for designing AI-allowed assignments.
- AI handles the draftable surface
- The student owns the choice, the local evidence, and the open question
- Curiosity is preserved when the assignment asks for justification, not just production
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