Our tools have a powerful influence on how we recognize what our work involves. Recently, a user researcher shared with me about his experience using a new AI tool, and the experience neatly exemplifies how today’s cognitive tools can hinder the development of understanding gained through ordinary work activities. This hindrance occurs partly because the value of these activities stems not just from the output generated, but even more so from participation in the process.
The researcher transformed raw notes into an affinity map using an AI tool, expecting this to expedite gaining insights from the data. Once the map was complete, though, he realized that this had actually slowed the insight-generating process instead of accelerating it. Much of affinity maps’ utility stems from the the changes in thinking brought about by participating in the mapping process rather than from the existence of a completed map. This change in thinking hadn’t taken place, and he distinctly felt its absence, recognizing that something was missing. This suggests that participation, not just map completion, contributes to developing a rich understanding of the data and gleaning useful insights. It’s deeply challenging to replicate this artificially because the changes in thinking stem from participation rather than from completion, and current AIs are designed to accelerate completion.
Another activity that derives significant value from participation, not just completion, is note-taking. Handwriting notes correlates with greater performance on indicators of conceptual understanding as compared to taking notes on a laptop (2014, 2017). Handwriting notes requires greater cognitive and physical participation, and this correlates with developing a deeper understanding of the material (2017, 2024).
When we use an AI for activities such as affinity mapping or note-taking, we risk losing out on developing this deeper understanding. The AI may be able to achieve an output state that appears satisfactory (and that likely is satisfactory for many scenarios), but it cannot replicate the changes we experience through attentive participation. It’s imperative, then, to be highly selective about what we choose to delegate and when we choose to participate. One strategy for discerning which tasks are most (or least) suited for delegation to an AI is examining the changes in thinking incited by participation in that activity.
If an activity distinctly changes its participant’s thinking, then there is strong incentive to participate, because that’s what fosters the desired deeper understanding. However, if participation doesn’t significantly effect a participant’s thinking, then that task is likely a strong candidate for delegating to an AI. These tasks include things like filling out templates with known variables, copying data, or giving standard updates to an already-familiar audience. The participants in these activities experience few changes in thinking as a result of taking part in them.
Considering the impact of participation can help guide decisions about when to leverage AI and when to pursue deep understanding through active participation in a given process. This selection strategy takes protects the value developed through attentive participation while making space to experiment with AI tools. If you’d like support in making strategic decisions about where and how to start using AI, schedule a free consultation.
This essay was edited with AI assistance.
Mueller, P. A., & Oppenheimer, D. M. (2014). The Pen Is Mightier Than the Keyboard: Advantages of Longhand Over Laptop Note Taking. Psychological Science, 25(6), 1159–1168. https://doi.org/10.1177/0956797614524581
van der Meer, A. L. H., & van der Weel, F. R. (Ruud). (2017). Only Three Fingers Write, but the Whole Brain Works†: A High-Density EEG Study Showing Advantages of Drawing Over Typing for Learning. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00706
Van der Weel, & Van der Meer. (2024). Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1219945