An AI assistant offers to draft a report and presents a tidy plan before it starts. The headings look sensible, the proposed evidence sounds authoritative and the user clicks approve. But a flawed plan can lead to a flawed document no matter how polished the writing becomes. Researchers have been testing how people can examine AI writing plans before confidence turns into misplaced trust.
A controlled experiment described by Microsoft Research compared different prompts that encourage users to scrutinise AI-generated plans during writing tasks. Its January 2026 research summary found that a condition requiring people to consider assumptions reduced overreliance without increasing measured cognitive load, while a different condition asking what might happen under alternatives was perceived as especially helpful. The findings concern the study's writing setting; they do not establish a universal rule for every AI product.
AI writing plans can carry a hidden mistake
When people review a completed AI answer, they may notice obvious errors. A plan is more difficult to evaluate because it describes work that has not yet been done. A proposal to compare two policies, quote particular sources and reach a conclusion can look impressively organised even if the initial comparison is unsuitable or a crucial perspective has been left out.
That matters because early decisions shape the rest of the task. If a report begins with the wrong audience in mind, all the following paragraphs may be beautifully written and entirely unhelpful. If a plan assumes that a change was caused by a new policy, the resulting narrative may search only for supporting evidence. The problem is not poor grammar; it is misplaced confidence in the structure of the reasoning.
Modern assistants increasingly present plans, checklists or staged workflows before acting. That can give users more control, but only if they are willing and able to examine the proposals. A brightly coloured approval button is no guarantee of meaningful human oversight. The person may be asked to sign off on a process without being shown which parts are uncertain.
One prompt asked people to question the assumptions
The Microsoft study examined techniques sometimes called cognitive forcing functions. The phrase sounds mechanical, but the idea is simple: introduce a moment that requires active thought before accepting what the system proposes. Rather than letting the user wave through an attractive outline, the interface asks them to consider what the plan is taking for granted.
The researchers compared a condition focused on assumptions, a condition asking what-if questions, a combination of the two and a control condition without that intervention. They also used follow-up think-aloud work and interviews to understand participants' experiences. In this experiment, the assumption-focused approach was most effective at reducing overreliance without increasing the measured cognitive burden.
That does not mean an 'Assumptions' box solves the problem of trustworthy AI. The study measured performance within specified tasks and conditions. A user confronted with an unfamiliar tax issue, a complex diagnosis or a sensitive personnel decision could face entirely different challenges. But the result offers a concrete design direction: questions that reveal the foundation of a plan may be more useful than a general instruction to check everything.
The what-if condition had a different advantage. Participants perceived it as the most helpful of the tested approaches. Asking how the plan would change under a different circumstance can make weak choices visible. It can also expand the work unnecessarily, so designers need to balance thoughtfulness against friction. A useful interface prompts enough consideration to matter without turning every routine email into a philosophical exercise.
Overreliance is not the same as using AI often
A person can rely heavily on an assistant and still check it carefully. Another person can use AI only occasionally but accept a dangerously wrong answer on the one occasion that matters. The central concern is inappropriate reliance: trusting a suggestion beyond what the evidence and the user's understanding justify.
A separate Microsoft Research review of more than 120 academic papers explores why people over-trust AI systems and the approaches researchers have tested to reduce the problem. It identifies mechanisms such as automation bias, where the presence of a system's recommendation can influence judgement even when warning signs are available. The review also examines techniques for communicating uncertainty and prompting more active engagement.
This explains why telling employees to 'use common sense' is inadequate. Interfaces influence decisions. If the software speaks with certainty, hides what it has not verified and rewards rapid approval, people can drift into treating output as an instruction. A design that exposes its assumptions gives them something specific to check. It makes oversight a task with substance rather than a box to tick.
Trust is also contextual. A writer may reasonably accept help with a neutral first draft but insist on checking a quotation, legal claim or financial calculation against its source. The appropriate level of scrutiny rises with the cost of being wrong. A good system should accommodate that range rather than make every approval feel identical.
How a better writing plan might sound
Imagine a small company preparing an explanation of why customers have stopped renewing a service. A weak AI plan might decide immediately that price is the cause, then propose a report showing how customers reacted to the latest increase. The structure appears logical because every section follows the same assumption. But the underlying cause could instead be poor support, a competitor or a change in customer needs.
An assumption check would ask what evidence establishes price as the main driver. A what-if check would explore how the plan changes if the loss of customers started before the price increase. Neither question requires advanced mathematics. Both interrupt the comfortable slide from plausible outline to confidently written conclusion.
In a school setting, a student could ask which part of an essay plan is supported by the assigned reading and which part is interpretation. In an office, an employee might question whether the proposed summary reflects the people who will read it. These are illustrations of the technique, not separate outcomes established by Microsoft's experiment. Their purpose is to show how a simple pause can change the quality of the questions asked.
The same approach may also help with revisions. If an AI editor recommends rewriting a passage for a more forceful tone, the author can ask what assumption that recommendation makes about the audience. For professional communications, LiveAIWire's coverage of AI-rewritten work emails suggests that wording affects how messages are received. The choice of tone is not a minor technical preference when a relationship is at stake.
A challenge should not become a ritual
There is a risk that companies will convert the finding into yet another compliance step. An employee might be forced to click through an assumptions prompt for every trivial sentence, eventually ignoring it completely. If the exercise is always the same, it stops encouraging thought and becomes background noise.
Effective friction should appear where it earns its place. A request to paraphrase a routine appointment reminder does not need the same examination as a proposal to dismiss a member of staff. The tool could focus attention on uncertain claims, missing evidence or decisions with substantial consequences. The human should retain the ability to say that a particular task is outside the software's proper role.
There is a related danger in treating the assistant's proposed alternative as trustworthy merely because it sounds sceptical. A model can manufacture a plausible objection just as readily as a plausible plan. Users must still compare any factual claim with real evidence. The value of asking questions lies in making the reasoning inspectable, not in assuming that the second answer is automatically better than the first.
Earlier LiveAIWire reporting on AI assistants that push back addressed a related idea: helpful tools need not agree with everything. The Microsoft research focuses more narrowly on how a user can be prompted to exercise judgement over a generated plan. Together, the stories show how the conversation design can influence whether human oversight is active or merely ceremonial.
The best question comes before the confident answer
For a practical writing workflow, try asking the assistant to state the objective, intended audience, evidence needed and assumptions it is making before producing a full draft. Check those points, adjust any that are wrong and then let the software help with execution. Later, review material facts and the final argument rather than judging success only by how quickly a document appears.
This need not make the process slower overall. Discovering a false assumption before writing a long report can save extensive revision later. However, any time saving in real workplaces would need to be tested rather than inferred from the study. One controlled experiment offers a useful technique, not a universal productivity guarantee.
The problem is familiar from cases where chatbots repeat confident but incorrect answers. A smooth explanation can discourage further questions. The most effective defence may be to ask an awkward question before the explanation becomes polished: what must be true for this plan to work? That simple habit keeps the person making the decision in control of its direction.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity and the social impact of emerging technology. LiveAIWire is an independent, human-led technology publication using AI-assisted research, editorial production and original AI-assisted editorial illustrations under his direction.