Longer dwell time on AI suggestions is associated with better attention check performance but slightly lower task performance¶
Claim · All claims
Evidence · 1 study · associationalr1·q2·i2medium
Currency · newest study 2026 · checked against its sources 2026-10-10 (automated, 1 of 1 entry) · 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.
q2 i2 Average dwell time negatively correlated with failed attention check rate (rho = -0.26), meaning more time on a suggestion was associated with passing more attention checks. → Jessica Hutchison 2026
Evidence¶
Jessica Hutchison 2026¶
Jessica Hutchison, Ian Tyler Applebaum, Kenneth Angelikas, Kush Rakesh Patel, Phuoc Nguyen, Antonio Lazaro, Nicholas Rucinski, Rahad Arman Nabid, and Stephen MacNeil. (2026). To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks. Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2026). https://doi.org/10.1145/3803400.3809394
q2 · i2 · associational · r1
Spearman correlation of session-level aggregates from the 55-student Clover lab study. Average dwell time (mean 12.2 seconds, SD 8.1) negatively correlated with failed attention check rate with effect size rho = -0.26; the article interprets that students who "spent more time looking at a suggestion passed more attention checks."
"ADTnegatively correlated ( 𝜌=− 0.26) withFCR, which could mean the less time students spend on a suggestion, the more likely they are to have highFCR."
Discussion¶
Learner Variables¶
- Attention — predictor: learners who differ on it differ in outcomes
- Time and Continuity — predictor: learners who differ on it differ in outcomes
Related Claims¶
- Behavioral interaction metrics are weakly or inconsistently correlated with task performance in AI-assisted programming — related
- Higher tab accept rates are strongly associated with failing attention checks among CS1 students using an AI code completion tool — related
- In offline validation, the HMM timing model detected children's attention moments more accurately than fixed-interval and dwell-time triggers
- Log analysis reveals novel post-acceptance behaviors (slow tab accept, slow accept then modify) that show suggestion acceptance is a multi-step process — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
dwell-time-attention-checks-versus-task-performance |
title |
Longer dwell time on AI suggestions is associated with better attention check performance but slightly lower task performance |
description |
Longer dwell time on AI suggestions is associated with better attention check performance but slightly lower task performance |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-10 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — jessica-hutchison-2026 |
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