Rethinking Routines - Retrieval, Scaffolding and Quiz Tasks in an AI World
Post 5 of 8 - How to adapt low-stakes routines and daily learning checks so they encourage authentic thinking, resist AI shortcuts, and deepen reasoning.
First published in response to the UAE’s 2025 AI education mandate, this series explores how teachers globally can evolve their pedagogy to maintain authenticity in student work while embracing purposeful AI use where appropriate. Whether you teach in the UAE or elsewhere, the strategies apply wherever academic integrity matters.
When Retrieval Becomes Guesswork
A Year 8 student completes a daily quiz in record time. Every answer is correct. But during the follow-up discussion, they falter when asked to explain why their choice was correct or how they arrived at it. Later, you discover that an AI tool has been trained on past quiz formats and can now generate instant answers for similar questions. The retrieval task, designed to strengthen memory and reasoning, has instead become a shortcut exercise.
This is the risk with predictable routines. AI can quickly learn patterns, anticipate answers, and complete tasks faster than any human, without the understanding we are trying to build. If we want retrieval and scaffolded thinking tasks to retain their impact, we have to design them in ways that make genuine engagement the only route to success.
Why These Routine Tasks Are Vulnerable
Daily routines like quizzes, retrieval practice, and scaffolded “fill-the-gaps” activities are some of the easiest for AI to complete convincingly. This is especially true when:
The question bank follows predictable patterns over time.
Questions can be answered without explanation or reasoning.
There’s no requirement for students to show their thinking before, during, or after.
Tasks are completed outside of class with no live element.
When this happens, students can lean on AI to produce perfect answers without building the underlying knowledge or cognitive connections these routines are meant to strengthen.
Redesigning Our Approach
A stronger design for AI-conscious retrieval and scaffolding could:
Use AI in the planning stage to generate question stems or challenge prompts you can adapt. This keeps your workload manageable while allowing you to review, edit, and increase cognitive demand before students see them. Always verify AI-generated items for accuracy, bias, and alignment; log the tool, date, and edits made before use.
Add unpredictability by varying formats, sequencing, or including unseen prompts that connect new material to prior knowledge. Explicitly use interleaving (mixing topics) and spacing (revisiting material over time) to reduce predictability across weeks.
Require reasoning or ranking rather than recall alone. Students could compare two possible answers, explain why one is better, or rank solutions by effectiveness.
Integrate oral reflection by pairing a quick written retrieval with a verbal “reasoning round” where students justify answers to peers or the teacher. EAL scaffolds: sentence stems such as “I chose this because…” and “Compared with… this is stronger because…” help all students, particularly those developing English proficiency, to articulate their reasoning.
Build in self-check moments where students identify their own errors and explain corrections before you review as a class.
Reduce over-scaffolding in later stages, shifting from heavy supports to more independent synthesis as the topic develops.
School leader tip – Department coordination: Standardise a minimum reasoning requirement in all retrieval tasks across subjects. Record in planning which stages permit teacher AI use in resource preparation but prohibit student AI during in-class consolidation.
Remove AI before: The in-class consolidation, discussion, and independent application stages.
Subject Anchors - Separating Recall from Reasoning
Embedding prompts that clearly distinguish between recalling facts and reasoning about them helps reveal genuine understanding:
Maths: Recall – “State Pythagoras’ theorem.” Reasoning – “Explain why it only works for right-angled triangles.”
Science: Recall – “List the three states of matter.” Reasoning – “Describe how particle movement changes during condensation.”
Humanities: Recall – “Name two causes of the Cold War.” Reasoning – “Explain which cause you think had the greater long-term impact and why.”
Making Thinking Visible in Daily Tasks
Even in a two-minute retrieval starter, you can capture authentic thinking if the design is right. Examples include:
A “justify your choice” box after each multiple-choice question. (AI consultant note: Consider two-tier questions – the answer plus a brief justification or confidence rating – to raise cognitive demand and detect AI shortcuts.)
A mini mind-map created from memory before revealing the correct answer.
An error-spotting task where students explain what’s wrong in a given answer.
These small tweaks slow students down, encourage reasoning, and leave a visible trail of thinking for you to review.
School leader tip – Moderation evidence: Keep a small portfolio of photographed starter and exit tasks with visible reasoning to evidence cognitive challenge in monitoring and inspection.
Teacher-Led Twists
Routine tasks can quickly lose their challenge if they follow the same predictable structure. As teachers, we can keep retrieval fresh and AI-resistant by making small, intentional changes to format, timing, and reasoning requirements, without overhauling the whole routine. The aim is to build unpredictability and deeper thinking into even the shortest tasks, so students are constantly rehearsing understanding, not just recalling facts.
Shuffle the Structure: Mix question types mid-task (multiple choice → short answer → diagram) so there’s no predictable pattern for AI to exploit.
Reason First, Answer Second: Show the question and ask students to jot down their reasoning before they commit to an answer.
Live Swap: Midway through, replace one question with a brand-new unseen one linked to the lesson content.
Think Aloud Modelling: Occasionally answer one question yourself on the board, verbalising your thought process so students see the reasoning standard you expect.
Peer Justification Swap: After the task, students swap papers and write a brief comment agreeing or questioning the reasoning given.
Student-Led Mini Challenges
When students take the lead in shaping retrieval, they become more invested in the process and more aware of what good reasoning looks like. These mini challenges put responsibility in their hands, while still giving you visibility of their thinking and understanding. By handing over small elements of control, you can make routines more interactive, unpredictable, and resistant to AI shortcuts.
Build the Question: Students create one new question from today’s lesson and add the correct answer with a short justification.
Fastest Three Links: In 60 seconds, students write down three ways the current topic connects to something learned earlier in the year.
One-Minute Teacher: A student explains an answer to the class as if they were teaching it, peers can ask one clarifying question.
Evidence Hunt: After answering, students find a piece of classwork, a note, or a diagram that supports their answer.
Redesign the Quiz: In pairs, students change one question to make it more challenging, then swap with another pair to answer.
What to Watch For
Overly rapid completion: If a student finishes before most peers and with perfect accuracy, check the reasoning section. Authentic reasoning usually takes time to write or verbalise, and AI-generated shortcuts tend to produce answers with minimal visible working. Compare the pace and detail to that student’s usual output; a sudden leap in speed and polish without prior evidence of mastery should prompt follow-up questions. You might, for example, ask them to re-explain one answer orally on the spot to confirm understanding.
Practical teacher actions: Pause the student and ask for a verbal walkthrough of two or three answers, recording brief notes on whether they can explain without prompts.Copy-paste style phrasing: Repeated use of identical wording or overly technical language may indicate AI influence, especially if it does not match the student’s normal in-class vocabulary. Authentic student work often shows small errors, varied phrasing, and personal quirks in expression. If all the answers sound like they came from the same polished source, have the student paraphrase or reframe one response in their own words. Keep a short record of their natural speech patterns and writing style for comparison.
Practical teacher actions: Keep a sample of the student’s unassisted work for side-by-side comparison, and ask them to rewrite one answer using simpler vocabulary.Reasoning gaps: Correct answers with missing or vague explanations suggest the task design needs stronger prompts for thinking visibility. Without a clear requirement for students to justify, compare, or apply their answer, it is easier for AI-generated responses to slip in unnoticed. If reasoning is missing, have the student extend their response with “because…” or “this matters because…” and note whether they can do so easily or struggle to connect the dots.
Practical teacher actions: Add a quick oral follow-up question like “What makes you certain?” or “How does this link to our last topic?” to assess depth of understanding.Over-reliance on scaffolds: If students can only succeed when heavily guided or with pre-filled structures, it can mask gaps in independent understanding. Gradually reduce supports, for example remove a word bank or hint step-by-step, to see whether they can still complete the task with accuracy. If performance drops significantly, the retrieval may be testing reliance on a format rather than genuine knowledge. Over time, shift from structured supports to open-ended prompts to confirm retention and transfer.
Practical teacher actions: Remove one scaffold from the next retrieval activity and observe whether the student’s reasoning remains accurate and complete.
Sample Prompt to Try
How to Use the Sample Prompt – Use this when building your own retrieval questions with AI. It helps you quickly generate prompts that demand reasoning, not just recall.
“Generate 10 retrieval questions on the causes of World War I for Year 9 history students. For each question, include a follow-up prompt that requires the student to explain why their answer is correct or to compare it to an alternative answer.”
Resources
🔓 AI-Resistant Retrieval Question Bank – A collection of 40+ adaptable prompts designed to require genuine recall and reasoning, covering multiple subjects and question types.
🔒 Cognitive Challenge Template – Exit-task templates that make students justify, rank, or adapt answers, ensuring thinking is visible and AI misuse is harder to hide.
Both resources help you keep daily routines authentic while maintaining cognitive challenge and reducing AI shortcut potential.
Reflective Prompt
Which of your regular quiz or retrieval activities could be redesigned to make reasoning as important as recall?
🗂️ Full Series: Teaching Smarter – Designing Lessons for the Age of AI
✅ Post 1: The AI Dilemma: Why Pedagogy Needs to Adapt – Why traditional task design is no longer fit for purpose in an AI-enabled world.
✅ Post 2: Redesigning Written Work in the Age of AI: Essays, Reflections and Reports – How to adapt extended writing tasks so AI supports pre-writing, not replaces original thinking.
✅ Post 3: AI and Oral Tasks: Structuring Authentic Discussion and Verbal Responses – How to safely integrate AI into planning for presentations, interviews and spoken assessments without losing student voice.
✅ Post 4: Project-Based Tasks and AI – Making the Thinking Visible – How to redesign project-based learning so AI supports the research phase but not overshadow the process and originality.
✅ Post 5: Rethinking Routines – Retrieval, Scaffolding and Quiz Tasks in an AI World (You are here) – How to adapt daily learning checks to reduce AI misuse and deepen reasoning.
🔜 Post 6: Assessment in the AI Era: Tracking Thinking, Not Just Outcomes – Strategies for building process-driven, AI-aware assessments that showcase genuine student learning.
🔜 Post 7: Building a Culture of Integrity in an AI-Enabled Classroom – How to lead conversations, policies and shared expectations that embed responsible use of AI without resorting to bans.
🔜 Post 8: Your AI-Aware Lesson Design Framework: Practical Planning for the Future – A printable, teacher-ready planning model to embed everything from this series into daily practice.
📢 If this post helped you rethink how you approach retrieval practice, share it with your department or include it in your next teaching and learning briefing.