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AI can save time on drafting or searching while adding work elsewhere: deciding what to ask, judging the answer, checking important claims, and choosing how to use the result. That shift can make an interaction feel mentally tiring even when the tool has completed part of the task. The effect depends on the task, the system, and how well the output fits what you need.

Why can using AI feel mentally exhausting?

Working with AI is not just a matter of entering a prompt and receiving an answer. You may need to clarify your goal, decide what information to provide, assess the response, and choose whether to rely on it or revise it. A 2024 CHI paper describes these as metacognitive demands: the monitoring and control involved in directing a task. Those demands can contribute to overall mental effort, or cognitive load. Different interfaces and ways of interacting with AI can also impose different demands. The paper’s account of generative AI and metacognition helps explain why the strain is not necessarily a sign that you are “bad at prompting.”

Unclear goals create extra decisions

If you are unsure what a finished result should look like, each prompt can turn into another round of reconsidering the task. The AI may produce a response, but it cannot settle what you actually need unless you define that for yourself.

More output can mean more to evaluate

A long answer or a menu of alternatives may look helpful but still leave you with work: compare the options, spot what is missing, and decide what to keep. This is a practical implication of the metacognitive-demand framework, not a consumer-tested rule about an ideal answer length.

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Review changes your role

When AI generates content, your job may shift from creating it to supervising it. In healthcare, the Agency for Healthcare Research and Quality (AHRQ) notes that clinicians may need to watch for errors without seeing how a system arrived at an answer. Its clinical human-AI interaction brief discusses automation complacency, confirmation bias, and automation bias as possible human-factors concerns. These are clinical examples, not direct evidence about every consumer tool, but they illustrate why checking a plausible-sounding response can take effort. AHRQ’s Human-AI Interaction brief cautions that human reviewers are not always effective or consistent.

Can AI reduce some work and increase other work?

Yes. AI may make one part of a task faster while adding prompting, review, correction, or approval work. Whether the overall workload falls depends on the application and workflow—not on a general property that all AI tools share.

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A 2026 systematic review and meta-analysis by Eun Jeong Gong, Chang Seok Bang, and Jae Jun Lee examined 21 studies involving 2,885 healthcare professionals in seven countries. The findings differed by clinical application. For ambient AI documentation, two studies in the review found lower pooled NASA Task Load Index (NASA-TLX) temporal-demand scores (standardized mean difference, or SMD, −1.46; 95% confidence interval −2.81 to −0.11) and effort scores (SMD −1.29; 95% CI −2.16 to −0.42). These are estimates for clinician populations and particular documentation tools, not evidence that consumer chatbots reduce stress.

The review found mixed or sometimes increased workload with diagnostic imaging AI and clinical decision-support systems. It rated evidence certainty as moderate for cognitive-workload reduction with ambient documentation, low for burnout reduction, and very low for imaging AI and clinical decision support. The authors also noted that the ambient-documentation evidence came from few studies and voluntary early-adopter cohorts, with wide confidence intervals; longer-term evidence across more products remains limited. Their review does not establish a general net benefit for healthcare workers, much less a general effect on everyday AI users. Read the 2026 systematic review and meta-analysis.

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How can you make AI use less overwhelming?

The following are low-risk ways to simplify an interaction, grounded in the metacognitive-demand framework and responsible-use guidance. They have not been established as treatment for overwhelm or tested as a fixed routine for consumers.

  1. Choose one concrete outcome first. Before opening the tool, decide what you want to have at the end—for example, a draft outline, a summary of a specific document, or a list of questions to investigate. This can reduce repeated goal-setting during the interaction. The CHI paper describes goal awareness as part of the work involved in using generative AI.
  2. Set limits on the response. Ask for a format and scope that match the task, such as a short outline, checklist, or a few alternatives. Treat this as a way to keep evaluation manageable, not as a proven formula or a scientifically established number of options.
  3. Decide what needs verification. Identify important claims before using the answer, then check them against a suitable primary or authoritative source. The UK government’s guidance on adopting generative AI includes quality-assuring accuracy and assessing whether a task is appropriate for AI. See The People Factor guidance.
  4. Stop when the result is good enough for its purpose. If the answer meets the task’s requirements, another prompt may create more output to compare and integrate. This is a practical application of the metacognitive framework, not a measured treatment effect. For high-stakes uses, “good enough” still includes the checks the decision requires.
  5. Keep judgment involved where errors matter. Human review is important for consequential decisions, but it is not effortless or infallible: reviewers can miss errors or give automated suggestions too much weight. AHRQ’s clinical examples explain why oversight should be paired with suitable verification rather than treated as a guarantee. AHRQ’s brief discusses these limits in healthcare settings.
  6. Skip AI when it adds more work than it removes. If a task is straightforward to do directly, or the answer would require extensive checking, another method may fit better. UK government guidance explicitly includes assessing tasks for AI suitability and disregarding tasks where it is unsuitable. The People Factor guidance addresses task fit.

When does AI fit the task?

A quick comparison can help you decide whether to use AI, use it in a narrower way, or work without it. These are practical decision axes inferred from the prompting, verification, suitability, and workload sources—not a validated consumer scale.

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Question AI may fit better when… Another approach may fit better when…
Is the goal clear? You can describe the finished output you need. You are still deciding what problem to solve.
How much review is needed? You can check the result efficiently for the task. Checking and correcting would take more work than doing the task directly.
What happens if the answer is wrong? The task is low-stakes and mistakes are easy to catch. An error could have serious consequences or requires expert or primary-source verification.
How much output do you need? A concise answer can meet the need. More alternatives would create extra comparison and decision work.
Does AI fit the workflow? It removes a real bottleneck without adding unnecessary handoffs. It introduces an extra review or approval step without a clear benefit.
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What should workplaces do about AI-related workload?

Workload is also a design and implementation issue, not just an individual’s prompting problem. ISO 10075-2:2024 describes design principles for work systems that cover tasks and equipment, the workplace, and social and organizational working conditions. The standard’s stated aim includes preventing both overload and underload. See ISO 10075-2:2024.

The UK government’s 2025 People Factor guidance recommends human-centred planning, workforce engagement, training and support, risk management, and monitoring as organizations adopt and optimize generative AI. It cautions that access to tools and guidance alone does not ensure effective use. In practice, teams should clarify which tasks suit AI, teach people what to review, and measure whether a new workflow removes work or simply moves it into checking and approval. Read the UK government guidance.

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Context and transparency matter too. The European Commission’s May 2026 update to its living guidance for responsible generative AI use in research discusses AI interactions with third parties in meetings and information management, including hidden prompts that users may not see. That is guidance for research settings, not evidence that hidden prompts commonly cause consumer overwhelm. See the European Commission update.

What the evidence does—and does not—show

The available workload evidence here is strongest for healthcare professionals using particular clinical AI tools. It does not provide a prevalence estimate for how often the general public feels overloaded by AI, and it does not show that consumer AI causes or treats anxiety, burnout, or another mental-health condition. The 2024 metacognitive-demand paper offers a research-based framework for understanding the extra work that can accompany AI use; it is not a randomized trial of the practical habits above. Read the CHI 2024 paper.

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