Claims

Polychronicity moderates the relationships between effective AI use, AI over-reliance, and learning outcomes

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i? Multitasking tendencies (polychronicity) shape both AI dependency and learning effectiveness, moderating key model relationships. → Gao L 2026

Evidence

Gao L 2026

Gao L, Sun Y and Khan SU (2026) From enhancement to over-reliance: a mixed-method study of generative AI and sustainable learning performance. Front. Psychol. 17:1847369. https://doi.org/10.3389/fpsyg.2026.1847369

q3 · i? · associational · r2

Moderation effects were estimated in the PLS-SEM model via interaction terms; the study reports that "polychronicity moderates key relationships". The hypothesized directions were weaker benefits and stronger over-reliance for high-polychronicity students.

"Furthermore, polychronicity moderates key relationships, indicating that multitasking tendencies shape both AI dependency and learning effectiveness."

Discussion

Page metadata
Field Value
type claim
id polychronicity-moderates-ai-use-learning-relationships
title Polychronicity moderates the relationships between effective AI use, AI over-reliance, and learning outcomes
description Polychronicity moderates the relationships between effective AI use, AI over-reliance, and learning outcomes
status review
evidence_strength moderate
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — gao-l-2026

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