In a randomized experiment with 52 Python programmers learning an unfamiliar library, those given an AI assistant scored 17% lower on a later quiz of concepts, code reading and debugging, with no significant speed gain; participants who delegated code to the AI scored lowest.

SocioLens project,

Claim LD04-SL-C12 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: preprint
Verification state: Checked against the primary source; the source is a preprint. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Long-term skill loss; effects on experienced workers in their own domain
Limitations: Small sample; one library and one session; the quiz has no code-writing items; conflict of interest: the authors worked through the Anthropic Fellows programme
Source (as cited): Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026
Source type: Preprint (recorded as “Preprint”)
Institutional position: AI developer-affiliated research programme
Maturity of the evidence: Emerging evidence
Recorded date: 14 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026 (Preprint)
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Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (PREPRINT)
Source type (coded): PREPRINT
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD04-SL-C12	—
2	PUBLIC-FACING CLAIM	In a randomized experiment with 52 Python programmers learning an unfamiliar library, those given an AI assistant scored 17% lower on a later quiz of concepts, code reading and debugging, with no significant speed gain; participants who delegated code to the AI scored lowest.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	B — Skill after the tool is removed	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026	source_ids: SRC-SL-SHEN-TAMKIN-ARXIV-2601-20245
7	SOURCE TYPE	Preprint	classes: PREPRINT
8	INSTITUTIONAL POSITION	AI developer-affiliated research programme	—
9	SCOPE	Crowd-recruited programmers with over a year of Python and no prior use of the Trio library; one session	—
10	DESIGN	Randomized experiment (26 per arm) followed by a 27-point quiz without AI	—
11	SAMPLE / DATASET	52 participants	—
12	MODEL / TOOL / VERSION	AI chat assistant	—
13	COMPARATOR	Participants learning without AI	—
14	MAIN RESULT	AI group 4.15 points (17%) lower on the quiz (Cohen’s d = 0.738, p = 0.010); largest gap on debugging; no significant speed gain; high scorers used AI for concepts, low scorers delegated	—
15	INFERENTIAL TARGET	Immediate learning of a new programming skill with and without AI help	—
16	DOES NOT LICENSE	Long-term skill loss; effects on experienced workers in their own domain	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Small sample; one library and one session; the quiz has no code-writing items; conflict of interest: the authors worked through the Anthropic Fellows programme	—
19	CONTRADICTORY / SUPPORTING SOURCES	Consistent with school-mathematics losses without guardrails and with the substitution–complement split in classroom experiments	—
20	GEOGRAPHY / LANGUAGE	Not checked / English	—
21	DISTRIBUTIONAL LEDGER	How AI is used (concepts vs delegation) separates learners more than access does	—
22	R01 / FIELD-MAP LINKS	LD-04, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	Larger replications, or delayed tests that show no lasting difference	—
24	PUBLICATION STATUS	ELIGIBLE WITH PREPRINT QUALIFIER	state: ELIGIBLE · qualifiers: PREPRINT
25	RECORDED DATE	2026-10-14	—

Field 22 references resolved against Field Map v0.2: LD-04 (LIVING_DEBATE); D2 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026” → Shen & Tamkin (2026), “How AI Impacts Skill Formation” (SRC-SL-SHEN-TAMKIN-ARXIV-2601-20245--v1.0) — Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026; class PREPRINT (explicit public provenance pin)

Cited by Living Debate versions

Cite this page

SocioLens project. (2026, October 14). In a randomized experiment with 52 Python programmers learning an unfamiliar library, those given an AI assistant scored 17% lower on a later quiz of concepts, code reading and debugging, with no significant speed gain; participants who delegated code to the AI scored lowest [Claim LD04-SL-C12, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld04-sl-c12/v1.0/

@misc{sociolens_ld04_sl_c12_v1_0,
  author = {{SocioLens project}},
  title = {In a randomized experiment with 52 Python programmers learning an unfamiliar library, those given an AI assistant scored 17\% lower on a later quiz of concepts, code reading and debugging, with no significant speed gain; participants who delegated code to the AI scored lowest.},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld04-sl-c12/v1.0/}},
  note = {Claim LD04-SL-C12, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 7c898117811129320a61947d5a4eb0cc9572a89e6bce28a433c08af2b31ab816.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD04-SL-C12 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-14; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts. Block “ENTRY 12 — LD04-SL-C12” SHA-256 7b03c212aadc5d3eb8e30e2da435aa64f3e2a883457e0f5992c0daa84a85c152; fixture fixtures/authored/claims/LD04-SL-C12--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD04-SL-C12 v1.0); the first version of a new Claim of the LD-04 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD04-SL-C12 v1.0" (Project Lead (Claude, AI), 2026-10-14); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 14 October 2026 to build the LD-04 debate; a new Claim, first version, written from the project's LD-04 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-SHEN-TAMKIN-ARXIV-2601-20245--v1.0 — basis: field 6 of ENTRY 12 — LD04-SL-C12 cites "Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-SHEN-TAMKIN-ARXIV-2601-20245--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Shen & Tamkin, “How AI Impacts Skill Formation,” arXiv:2601.20245 v2, February 2026".