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Step 10 of 11

What the research has found so far

Four findings from the research program, each with its limits stated where the finding is.

These are findings from the research program itself, which sits inside the wider literature scan described on the first step and is refreshed alongside it.

These come from two samples: 359 working adults who reported how they use AI, and 203 students whose actual AI conversations were coded turn by turn. Both are described in the preprint linked at the bottom of this page, which has not been peer reviewed.

The eight modes carry information that usage measures do not

How much someone uses AI, how long they have used it, and how many tools they use are the obvious things to measure. The eight modes explain variance beyond all of them: an additional R² of .23 in the sample, .20 under repeated cross-validation.

What this does not show

This says the modes are not redundant with usage. It does not say the modes cause the outcomes they predict.

Working-adult sample, N = 359 (319 with complete data for the full model). Preprint §3.

Almost every way of using AI feels productive and builds dependency at the same time

Seven of the eight modes correlate positively with perceived productivity (r = .20 to .50) and positively with perceived dependency (r = .08 to .48). The same behaviors that make people feel more effective make them feel more reliant.

One mode breaks the pattern. Verification, checking claims and calculations and sources, sits at r = .04 with productivity and r = -.22 with dependency, and is statistically distinguishable from every other mode on both.

What this does not show

These are correlations measured at one point in time. They do not establish that productivity is being traded against dependency over time, and the proposed explanation for why Verification differs was not supported.

Working-adult sample, pairwise n = 358 to 359. Preprint §3.2.

The tier ordering held up only partly, and not in the direction you might expect

The framework predicts that agency-tier behaviors should look better than passivity-tier ones. What the data show is narrower.

Oracle, asking for a direct answer and accepting it, has the broadest adverse pattern once other modes and usage are controlled for. That half fits. But Verification is the only agency-tier mode whose favorable associations survive correction for multiple comparisons. The other three agency modes do not behave the way a clean ladder predicts.

What this does not show

This is the finding that most constrains what the framework can claim. Passivity, Partnership and Agency are useful teaching groupings; on this evidence they are not a validated hierarchy, and presenting them as one would overstate the result.

Working-adult sample. Benjamini-Hochberg correction across 64 mode-outcome coefficients. Preprint §3.1.

People do not know which modes they use

The students reported how they engage with AI, and their real conversations were then coded turn by turn. The correspondence between the two is close to zero. Not one correlation survives correction for multiple comparisons, and the result is stable across three generations of the coding protocol.

What this does not show

This is a measurement finding, not a claim that people are being dishonest. Self-report and behavioral coding are answering different questions, and a gap between them is exactly why both are needed.

Student sample, N = 203, of whom 171 had classifiable paired profiles. Preprint §4.1.

Quick check

The tier ordering was tested. What actually held up?

Read the work

The typology paper, its methods, and its supplementary material are posted as a preprint. It has not been peer reviewed.