Project-Based Tasks and AI – Making the Thinking Visible
Post 4 of 8 - How to redesign project-based learning so AI supports research and planning without replacing the thinking, process, and originality that make student work authentic.
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 the Project Looks Perfect but the Thinking is Missing
A Year 10 student presents a stunning history project. The layout is flawless, the research is thorough, and the visuals could have come straight from a professional design studio. But as you ask follow-up questions, something feels off. They hesitate when explaining why they chose certain sources. Their reasoning is vague. Eventually, you discover that AI helped generate not only the visuals but most of the text. The work looks impressive, but the thinking is hidden.
This is where project-based learning needs a rethink. AI can be a fantastic partner for brainstorming, formatting, and even suggesting research pathways. However, if we only assess the end result, we miss the most important part of the journey. The decision-making, problem-solving, and creative risks that students take along the way are what truly show learning.
Why Project-based Tasks Are Vulnerable
Project-based tasks often reward the final look and feel of a piece of work more than the thinking that shaped it. AI can now produce compelling content, flawless layouts, and polished presentations in minutes. Without deliberate design, it becomes almost impossible to tell where AI support ends and student originality begins.
The problem is that many project briefs:
Focus exclusively on the final product, leaving no requirement to show how it was created.
Allow most of the work to happen outside the classroom, where AI can do the heavy lifting.
Reward style and polish more than evidence of thought and originality.
Rarely prompt students to explain or defend their choices, making it easy for AI-generated decisions to slip through unnoticed.
To truly protect authenticity, we need to make the process as visible and as valued as the product itself.
Redesigning Our Approach
A stronger design for AI-conscious project work could:
Allow structured AI use at the start so students can experiment with ideas, try out layout options, and explore different research angles. This shows them how AI can widen possibilities without letting it take over the deeper work.
Embed visible checkpoints where students pause to document what they have done, the reasoning behind their choices, and any adaptations made to AI input. This builds a clear, reviewable trail of the learning journey.
Require AI critique so students must assess the reliability, accuracy, and bias of AI suggestions. This builds their discernment and gives them practice in making judgement calls.
Include process-focused assessment criteria that explicitly reward originality, iteration, and reflection. Students know from the start that how they work will be graded, not just the final result.
Integrate live elements such as in-progress presentations, peer reviews, or teacher Q &A sessions. These checkpoints allow you to confirm understanding and uncover misconceptions before the final submission.
Ask for a final reflection in which students share where AI helped, where it was set aside, and what they learned from those decisions. This metacognitive step reinforces ownership and personal responsibility for their work.
School leader tip – Policy alignment: In schemes of work, record which stages of each project permit AI use, and link this explicitly to your school’s acceptable-use and data-protection policies so expectations are consistent and compliant.
Remove AI before: the final curation, editing, and live presentation or submission stage.
Showing the Process, Not Just Telling It
To make process tracking meaningful, it helps to model exactly what it looks like. You might share an anonymised Project Thinking Log page from a past student, showing their week-by-week notes, decision-making, and AI critiques. Alongside it, display a process portfolio spread that illustrates a real example of progression: a first draft, the feedback received, and the subsequent improved iteration. These concrete examples show students what you expect and help them see that the journey is as important as the destination.
School leader tip – Moderation evidence: Store a small sample set of completed process portfolios with teacher annotations for internal moderation and to use during inspection walk-throughs. This creates a clear evidence trail of how your curriculum protects authenticity while allowing purposeful AI use.
Making Checkpoints Tangible
To ensure the process is consistently visible, your could schedule three fixed checkpoints with specific outputs to collect:
Checkpoint A: An annotated research map or source triage sheet, showing how sources were selected, rejected, and linked to the project aim.
Checkpoint B: Before-and-after design mock-ups with a 60-second audio rationale explaining changes and design decisions.
Checkpoint C: Two pieces of feedback received from peers or the teacher, plus one specific change made in response to that feedback.
These checkpoints keep the process transparent, give students structured moments to reflect, and provide you with authentic evidence of their independent thinking.
What to Watch For
Overly polished work too early - If a project looks highly finished within the first few stages, it may be a sign that AI has replaced authentic drafting and refinement. Most student projects should show natural progression, with rougher early stages improving over time. When this polish appears immediately, it is worth investigating how much of the early work was independently generated.
Language that does not match the student’s voice - Repetitive, overly formal, or overly technical phrasing can signal that text has been lifted directly from AI output without adaptation. Students tend to have a distinct writing style, and sudden shifts in tone or vocabulary are a red flag. Comparing the project language to the student’s in-class writing can quickly reveal discrepancies.
Gaps between verbal and written explanations - When a student struggles to explain their own content verbally, it may suggest that they did not fully create or understand it. A strong project creator should be able to discuss decisions, justify changes, and respond to questions confidently. If answers feel vague or hesitant, further questioning is needed to understand the true source of the work.
Lack of documented decision-making - If there is no clear record of why choices were made or how the project evolved, then the process is invisible. Without checkpoints, students can complete the bulk of the task outside class, making it easier for AI to dominate the work. Building decision logs into the task design from the start helps make the student’s reasoning visible and reviewable.
Sample Prompt to Try
How to Use the Sample Prompt - The sample prompt below works best at the very start of a project. You can give it directly to students or adapt it to your subject area. Ask them to share their AI-generated suggestions, explain how they would adapt them, and identify which ideas they would reject and why. This keeps the AI stage visible, turns it into a teachable moment, and ensures it is a starting point rather than the whole process.
"You are a Year 9 student working on a science fair project about renewable energy. Use AI to suggest three possible project directions. For each suggestion, explain how you would adapt it, what extra research you would do yourself, and why you might reject parts of the AI output."
Resources
🔓 Project Thinking Log Template – A structured weekly reflection sheet where students log progress, decisions, and AI use. Includes space for decision rationales, challenges encountered, and planned next steps, making the thinking visible for teachers and students alike.
🔒 Process Portfolio Examples – Three subject-specific models that illustrate how to capture drafts, feedback, and iterations. Includes visual annotations showing what changed and why, plus examples of AI critiques to demonstrate how to record rejection or adaptation of AI input.
Both resources are designed to make process tracking a natural part of project work rather than an extra burden, while giving leaders and inspectors clear evidence of authentic student learning.
Reflective Prompt
How could you adapt your next project brief so that the process is as visible and valuable as the final product?
🗂️ 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 (You are here) – How to redesign project-based learning so that AI can support the research phase but not overshadow the process and originality.
🔜 Post 5: Rethinking Routines: Retrieval, Scaffolding and Quiz Tasks in an AI World – How to adapt daily classroom activities and low-stakes tasks to reduce AI misuse and deepen cognitive demand.
🔜 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 project-based tasks, forward it to a colleague who is exploring AI in their classroom or share it with your department during planning.
P.S. If your school has a project-based learning coordinator or a design and technology lead, forward this to them. It could spark valuable conversations about how your curriculum uses AI while protecting authentic student thinking.