UX Research Report: 1 in 2 Omni Calculator users see AI as usable for calculations, but not yet reliable

As AI chatbots like ChatGPT, Gemini, and Perplexity become everyday tools, many users are starting to use them not only for writing or ideation but also for solving mathematical and logical problems.

At Omni Calculator, we wanted to see how people actually experience this shift. We set out to explore:

• How often do users turn to AI chatbots for calculations?
• Do they trust the answers when numbers are involved?
• And do chatbot interfaces make these tasks easier or more confusing?

This short study summarizes what we found out about how users perceive AI chatbots when solving numerical problems.

Between September and October 2025, we conducted an online survey among Omni Calculator users, gathering 1,202 valid responses. Participants were asked a mix of quantitative questions (Likert-scale ratings) and open-ended qualitative questions about their experiences using AI chatbots for math or logic tasks.

Nearly half of respondents (46.5%) reported using an AI chatbot (like ChatGPT, Gemini, or Perplexity) to solve a calculation problem similar to those they usually perform on a calculator. Slightly more than half still haven't tried AI chatbots for such tasks — could it suggest hesitation and/or slower adoption? If around 1 in 6 internet users use standalone generative AI tools every month, that is a pretty good result for Omni Calculator users.

When asked to evaluate trust, accuracy, ease, and clarity of explanation, users showed generally positive attitudes, but trust and explanation lagged a bit behind.

The table below represents the satisfaction level of AI chatbots across all 4 aspects:

Across all dimensions, more than half of users reported being satisfied or very satisfied with the AI-generated results. However, trust and clarity of explanation received significantly lower ratings than accuracy and ease of use, showing the highest levels of dissatisfaction. This suggests that while users find the AI generally usable, they do not yet perceive it as trustworthy or transparent. Interestingly, despite high overall scores for accuracy, it also received a notable share of dissatisfied ratings — indicating that Omni Calculator users hold mixed opinions about how reliable the AI's answers truly are.

This result may be acceptable when discussing a Minecraft calculator, yet for users of a pediatric dose calculator, this level of trust in a tool may not be enough. On the same note, would an AI explanation with hallucinations be sufficient for someone counting discounted cash flow? That's a subject for discussion.

💡 From a UX perspective: could AI's efficiency satisfy the functional dimension, but the absence of transparent reasoning leaves an emotional gap in perceived trustworthiness?

So, how about the visual aspects of chatbots? We asked users to rate their satisfaction and share what worked well or was missing in chatbot interfaces.

✨ The 69% satisfaction score for AI chatbot interfaces. Interestingly, users who expressed trust in AI rated the chatbot interface much higher — 85.4% satisfaction.
This suggests that interface design plays a crucial role in shaping trust, much like it does in deterministic tools such as calculators.

👍 Users appreciated the chatbot's ability to break down reasoning ("Gives explanations", "Showed how the math worked") and its simplicity of use ("Ease of use", "Ease of access", "Easy to input").

As UX/UI & Product Leader Shauna Graham notes:

"Usually, when people turn to an LLM or chatbot, it's for simplicity. They just want the output and the AI can often handle the calculation and provide the answer without the user even needing to know the formula's name."

👎 However, many also mentioned errors or uncertainty in answers, overly long or confusing responses, and the lack of clear calculation logic — gaps that limit trust despite good usability. Some directly suggested that the lack of form inputs was not convenient.

The comments reveal an interesting phenomenon: users oscillate between too much and too little explanation in chatbot responses to calculable problems. For experts, following the reasoning behind an AI-generated result is rarely an issue. However, for novices, excessive detail can be overwhelming, while insufficient detail leaves them confused.

Although natural language input feels intuitive, users also highlight its downside — the lack of transparency in the calculation logic, which makes it harder for less experienced people to understand how results were obtained.

When comparing overall satisfaction, calculators continue to hold an edge. Omni Calculator shows consistently higher satisfaction with 72.8% of users on average being satisfied or very satisfied, compared to 46.9% for AI chatbots. Dissatisfaction is relatively low for both, though slightly higher for AI chatbots (≈19%) than for Omni Calculator (≈9%).

The table above shows a comparison of average satisfaction levels between Omni Calculator and AI chatbots.

Yes, Omni Calculator users might simply be reaffirming why they prefer the tool. But there's a bigger story here: those who've tried both worlds — AI and traditional tools — tend to drift back to what feels certain. Prioritizing consistent, verifiable answers over conversational flexibility.

➡️ Trust is the new north star of usability.
Confidence in the output matters as much as interface clarity. Users want to see how AI arrived at an answer, yet for now, they mention errors or uncertainty in the chatbot's results.

➡️ Perceived precision can be enhanced by the UI structure.
Structured input — fields, buttons, and overall hierarchy — isn't just familiar. It allows users to repeat, compare, and validate results, which is crucial when confidence in accuracy is part of the experience itself. For decades, calculators trained users to trust through structure. Chatbots disrupted that schema by introducing fluid conversation. The result? Even when AI is correct, without visible logic, users lose the sense of control, and it just doesn't feel correct. Chatbot interface, as a text box, strips away the visual grammar of precision that calculators, spreadsheets, and forms have perfected. To feel reliable, AI needs to look reliable as well.

➡️ Users get lost between too much and too little explanation.
When chatbots over-explain, users get lost in all the content, yet when they under-explain, they lack transparency. The real challenge is calibrating explanation depth to user confidence. In other words, the next UX frontier for AI is making reasoning adaptive: showing just enough of how the answer was reached to make it feel trustworthy.

So, is the AI chatbot interface suitable for solving calculable problems?

Not yet. But it is promising, engaging, and very capable. The foundations are there, but trust, perceived precision, and the transparency of reasoning require further design attention to make AI interfaces feel fully reliable.

In design terms, chat-based formats could work best when paired with visual aids (steps, formulas, units) that reintroduce the reliability calculators are known for. What if AI could benefit from interactive calculation widgets to provide an even better user experience? Traditional interfaces still outperform chat for complex or repeatable tasks, suggesting that the future of calculative UX lies in hybrid solutions. ✨

When AI interfaces can explain their reasoning as clearly as calculators display results, that's when conversation will truly meet computation.