Does partial code help users understand logic better than complete solutions?

The excerpt reports that "while participants appreciated proactive coding support, the highly efficient AI generation often did not reserve time for users to develop the necessary understanding of the code logic." Because the generated code "looks very convincing (P13)," participants "were tempted to accept the suggestions and proceed to the next subtask" rather than check them. To counter this, the Codellaborator probe was "constructed to offer hints and partial code rather than the complete solution," and the authors report that "participants from the study appreciated the AI's choice to provide scaffolding (e.g. code skeleton, comments that illustrate the logical steps)."

The design implication drawn from this is to "decompose generated responses to semantic segments and present them gradually to allow time for users to fully interpret and understand the suggestions," rather than always returning a finished answer immediately the way "result-driven" features such as GitHub Copilot's inline autocompletion do. The authors frame this as a deliberate trade against raw efficiency: full-solution generation works for "repetitive processes such as refactoring" but risks maintainability and validation problems "when scaled to larger systems," citing a prior large-scale survey that named "troubles understanding and evaluating code" as a common Copilot usability issue. The excerpt also cautions that generating explanations alongside code is "equally important to not overload the user with information."

This qualifies the sibling finding in Do proactive AI coding agents actually disrupt programmer workflow? by naming one specific lever — output granularity, not just presence or context — for easing the trade-off between proactive efficiency and user understanding. It also extends Where do vibe coding students actually spend their debugging time?: where that study measures how rarely vibe-coding users engage with code once it looks plausible, this excerpt proposes partial, staged output as a way to keep users engaged with the logic rather than only observing the pattern.

The excerpt presents this as one of five design implications drawn from participant feedback on a single technology probe, explicitly stating it is "not a holistic list for designing proactivity." It does not report a controlled comparison isolating hint granularity from the other Codellaborator features, so the claim that partial code preserves understanding better than complete solutions rests on participant appreciation voiced in the study rather than a measured comprehension outcome, and its generalization to other languages, task types, or longer engagements is untested here.