Sometimes the question isn’t about AI: What a calculator reveals about technology and learning
We asked online learning professionals a question about calculators. Some of them answered a question about artificial intelligence. That unexpected turn may tell us nearly as much as the poll results themselves.
In Part 2 of OLC’s July 2026 Snap Survey, respondents considered the following statement:
A student who correctly interprets and applies a calculator’s output demonstrates meaningful learning, even if they cannot perform all calculations by hand.
Of the 64 respondents, 67% agreed or strongly agreed. Thirty-two percent disagreed or strongly disagreed.
The question was intentionally about a familiar, bounded technology, not generative AI. Yet AI quickly entered the conversation. One commenter objected that abacuses, adding machines, cash registers, slide rules, and calculators “are not A.I.” and characterized the comparison as “apples and oranges.”
The commenter was correct that calculators and generative AI are different technologies. Calculators perform defined operations according to established rules. Generative AI systems produce probabilistic outputs that may be incomplete, biased, or confidently incorrect. The risks and the judgment required to use them are not equivalent.
But the poll was not making an argument about AI. Nor was the calculator a proxy intended to make a disguised point about AI. The survey was genuinely about technology tools more broadly and the kinds of cognitive work we routinely delegate to them. Calculators offered a familiar example through which to examine the relationship among tool use, foundational knowledge, and meaningful learning without making any single emerging technology the center of the question.
It was asking a more fundamental question: What does a student need to be able to do personally for us to conclude that meaningful learning has occurred?
Technology and foundational skills are not opposites
Part 1 of the survey asked respondents which statement best reflected their view of how students should learn with technology. Among 108 respondents:
- 46% said students should learn to use technologies alongside foundational skills.
- 32% said the appropriate approach depends on the discipline and learning objectives.
- 15% said students should master tasks without technology before using technological tools.
- 6% said students should primarily learn with the technologies used in professional practice.
Together, 78% favored either integrating technology with foundational learning or making the decision according to the context. Relatively few endorsed either end of the continuum: requiring complete mastery before introducing technology or allowing professional tools to become the primary means of learning.
This suggests that most respondents do not see technology use and foundational learning as an either-or choice. The more difficult question is how much foundational knowledge students need, and which knowledge they need to retain, when a tool performs part of the task.
The calculator question makes that tension visible. A student might be unable to complete a lengthy calculation by hand but still understand what operation is needed, select the correct inputs, interpret the result, and apply it appropriately. Is that meaningful learning?
For two-thirds of respondents, the answer was yes.
We already offload work to technology
Calculators are only one example of the work we routinely delegate to technology.
Writers use spellcheck without independently identifying every misspelled word. Drivers use GPS without plotting routes on a map. Spreadsheet users apply formulas without performing every calculation by hand. Search engines retrieve information we do not remember. Digital calendars store commitments so we do not have to hold them in memory. Autofill recalls addresses and account information, and translation tools generate language a user may understand better than they can independently produce.
Even writing itself is a form of cognitive offloading: It allows us to store ideas outside our memories so we can return to them, revise them, and share them with others.
These tools do not eliminate the need for knowledge. They change which knowledge matters during the task.
A writer needs to recognize when spellcheck substitutes the wrong word. A driver needs to question a route that leads toward a closed road or unsafe destination. A spreadsheet user needs to notice when a formula produces an implausible result. A researcher needs to evaluate the credibility of sources returned by a search. A traveler using translation software still needs enough contextual awareness to recognize an inappropriate phrase.
As a former language teacher, I can attest that translation software caused considerable concern when it first became widely available. Educators worried that students would use it to bypass the difficult work of learning vocabulary, grammar, and sentence construction. Those concerns were not unfounded, but they also forced language educators to distinguish between using translation software as a substitute for learning and using it as a tool that still requires linguistic and cultural judgment.
Using a tool does not automatically demonstrate, or prevent, learning. The more useful question is what the learner understands before, during, and after the technology performs its part of the task.
These tools offload different kinds of work, but they leave the learner with a common responsibility: judgment.
Interpretation is not the same as unquestioning acceptance
A second commenter raised an important concern: Students may learn to press the correct buttons and accept whatever result appears without asking whether it makes sense.
That concern does not necessarily contradict the poll statement. In fact, it helps clarify what “correctly interprets and applies” should mean.
Meaningful use of a technological tool involves more than obtaining an output. Students may need to:
- Understand the problem they are trying to solve.
- Select an appropriate tool or process.
- Provide suitable inputs.
- Interpret the output in context.
- Recognize when a result is implausible.
- Verify the result when the consequences of error are significant.
- Explain or act on the result appropriately.
A student who enters numbers into a calculator and copies the answer has demonstrated tool operation. A student who can explain what the result means, determine whether it is reasonable, and use it to make a sound decision has demonstrated something more substantial.
The foundational skill, therefore, may not always be the manual reproduction of every procedure. It may be the conceptual understanding and evaluative judgment required to supervise the tool.
Familiar tools make cognitive offloading easier to overlook
Cognitive offloading is not new. What changes is how we perceive it. Once a technology becomes familiar, the work delegated to it often becomes nearly invisible. We stop asking whether using a calculator is “cheating” in every context and begin asking when calculator use is appropriate.
Generative AI has not yet reached that point. It carries legitimate concerns about accuracy, authorship, bias, privacy, and the erosion of learning. It also carries enough cultural anxiety that even a question about calculators can be interpreted as a disguised argument about AI.
That reaction is useful. It reveals how thoroughly AI now frames our conversations about educational technology. It may also make it harder to examine underlying learning questions separately from the capabilities, limitations, and risks of a particular tool.
Sometimes the question really is about AI. Sometimes it is about what students must still know when any technology performs part of the work.
Moving from tool rules to learning decisions
The survey results do not provide a universal boundary between acceptable support and excessive cognitive offloading. They point instead toward decisions grounded in the discipline, learning objectives, characteristics of the tool, and consequences of error.
Educators might ask:
- Is completing the process manually an essential learning outcome?
- What conceptual knowledge must students retain to evaluate the output?
- What errors should students be able to detect?
- How reliable is the tool for this particular task?
- What happens if the output is wrong?
- Does the assessment measure students’ judgment or merely their ability to operate the technology?
The answers will differ across contexts. A student developing basic number sense may need to calculate manually. A professional interpreting a complex dataset may need to demonstrate analytical judgment rather than reproduce every calculation. In a high-stakes setting, learners may need both procedural competence and the ability to verify a tool’s result.
The July Snap Survey offers a snapshot of professional sentiment rather than a representative measure of the field. Participants self-selected, the two polls had different response totals, and we cannot determine whether the same individuals answered both questions.
Still, the results suggest a productive direction. The central question is not simply whether students should use technology. It is what students need to understand, retain, and evaluate when technology does part of the work.
And sometimes, despite what the comments might lead us to believe, the question isn’t about AI.
About OLC Snap Surveys
OLC Snap Surveys provide quick insights into emerging trends, challenges, and perspectives in digital learning. Surveys are distributed to the OLC community and offer a rapid way to capture practitioner experiences across institutions.
OLC welcomes participation in future Snap Surveys to help inform ongoing conversations about the future of online and digital learning. Follow OLC on LinkedIn to participate the latest Snap Survey, shared monthly.
As senior researcher at OLC, Carrie designs, conducts and manages the portfolio of research projects that align with the mission, vision, and goals of the Online Learning Consortium. She brings with her over 15 years of experience as an online educator and instructional designer with a passion for research. She has peer-reviewed publications covering a variety of topics such as open educational resources, online course best practices, and game-based learning. In addition to a strong background in higher education teaching and instructional design, Carrie brings with her extensive experience in customer service and small business management. She holds a PhD in Educational Technology from Arizona State University, an MS in French from Minnesota State University, and BA in French from Arizona State University.
