Technology has long changed how much time and effort students need to complete academic work. Calculators reduce the mechanics of computation. Search tools make information easier to locate. Word processors simplify drafting and revision. Statistical software can perform analyses that would be impractical to calculate by hand.
When technology reduces the effort required for a task, however, an important instructional question remains: What should happen to the time and cognitive capacity that technology frees up?
OLC’s August 2026 Snap Survey explored that question from two perspectives.
Is the Artifact Evidence of Learning?
The first question asked respondents whether they agreed with the statement, “A well-written paper is evidence that learning occurred.” Among 79 respondents, 8% strongly agreed and 33% agreed. In contrast, 46% disagreed and 14% strongly disagreed. In other words, approximately six in ten respondents rejected the idea that the quality of the finished paper, by itself, demonstrates that learning occurred and that distinction matters. A polished artifact may provide evidence of performance, but it does not necessarily reveal the cognitive processes that produced it. What did the learner understand? What connections did they make? What evidence did they evaluate? Could they defend their conclusions? Could they apply what they learned somewhere else? Those questions shift attention from the artifact itself toward the learning demonstrated through its creation and use.
When Technology Saves Effort, What Comes Next?
A second question asked what educators should primarily do when technology reduces effort on a task. Of 42 respondents, 52% selected increase real-world application, while another 29% chose increase reflection and evaluation. Only 12% favored reducing student workload, and 7% selected increasing task complexity. Together, 81% of respondents favored redirecting students toward application, reflection, or evaluation. That suggests an important distinction: reducing effort does not necessarily require reducing learning, and maintaining rigor does not necessarily require adding more work. Instead, educators can ask how student effort might be reinvested.
More Work Is Not Necessarily More Learning
Consider a course offered simultaneously at the upper-division undergraduate and graduate levels. One straightforward way to differentiate expectations is to require graduate students to write an additional paper or submit a longer version of the undergraduate assignment. The graduate students certainly do more work. But does that additional work necessarily demonstrate graduate-level learning? Adding five pages increases the quantity of work without necessarily changing its cognitive complexity. A different approach might ask graduate students to synthesize a broader scholarly conversation, evaluate competing interpretations, defend a methodological choice, apply concepts in a novel context, or create something for a professional or disciplinary audience. The assignment might not take the form of a longer paper at all. What distinguishes the graduate-level work is the thinking students are expected to demonstrate. The same distinction can be useful whenever technology makes an existing academic task more efficient.
Move Up the Taxonomy
Bloom’s taxonomy offers one way to think about that shift. If technology appropriately reduces the time students spend remembering, retrieving, organizing, calculating, or performing other routine elements of a task, educators can reconsider where students’ remaining effort will produce the greatest learning value.
Can students spend more time applying what they know?
Can they analyze relationships or competing perspectives?
Can they evaluate evidence, arguments, or outcomes?
Can they reflect on their reasoning and revise their approach?
Can they create something new from what they have learned?
In other words, when technology reduces effort, rigor does not have to disappear. It can move up the taxonomy.
Reinvesting Student Effort
There is an important complication. Courses do not exist independently of expectations for credit hours, instructional time, and student engagement. Greater efficiency cannot always, or necessarily should, translate directly into fewer hours devoted to a course. But that does not mean every hour of student effort has equal educational value. If technology reduces the time required for an existing task, simply adding pages, assignments, or procedural requirements to restore the previous workload may preserve time-on-task without improving learning. Increasing complexity merely to make a task harder presents a similar problem. A better question may be: How can the capacity created by technology be reinvested in learning of greater value?
That reinvestment has at least two dimensions.
Cognitive complexity asks whether students can use their capacity to engage in higher-order thinking: applying, analyzing, evaluating, and creating rather than simply doing more of the same work.
Learning reinvestment asks where students’ time and attention could be redirected: authentic application, reflection, feedback, iteration, discussion, experimentation, collaboration, or other experiences that deepen learning.
This distinction allows educators to preserve meaningful expectations for student engagement without treating inefficiency as evidence of rigor.
Rethinking the Value of Time
Technology will continue to change how students accomplish academic tasks. The instructional challenge is not necessarily to preserve every process that once required substantial time, nor is it to fill every saved hour with additional work. Instead, educators can reconsider the educational value of students’ time. What cognitive work has technology reduced? What learning was that work intended to produce? Is that learning still being demonstrated? And where might the resulting time and cognitive capacity be reinvested to produce something deeper?
The August Snap Survey results suggest that educators may already be thinking in those terms. Respondents were skeptical that a polished academic artifact alone demonstrates learning, and when technology reduced effort, they overwhelmingly favored real-world application, reflection, and evaluation over simply increasing task complexity.
Perhaps the most useful question, then, is not whether technology makes academic work easier. It is what we choose to do with the capacity it gives back.
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.
