AI · Design study and user evaluation.

“Why is Chicago deceptive?” Towards building model-driven tutorials for humans

CHI. · 2020

Lai, V., Liu, H., Tan, C.

Original paper ↗

Summary

Model-driven tutorials improved understanding of model behavior and decision support.

For your marketing

Possible founder application

Make onboarding interactive and boundary-case based.

Reported result

What the study found

Model-driven tutorials improved understanding of model behavior and decision support.

Research question

What the authors investigated

Can model-driven tutorials improve understanding of AI?

Design

Design study and user evaluation.

Sample and setting

Users learning a deceptive-review classifier.

Scope and transfer

Limitations to keep in view

Narrow task and short-term learning.

Research sources

Publisher abstract/full text supports the reported design, sample, and findings.

Additional research source ↗

Study strength

4/5 Research record provides a clear design and context; apply the finding within its stated limits.

How this was assessed

The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.

Design transparency · 2/2

Based on the reported study design in the retrieved source.

Sample and setting · 1/1

The sample context is stated; a numerical count is preserved only when reported.

Outcome clarity · 1/1

The finding is summarized from publisher or article metadata.

Access for checking · 1/1

Full text was identified.

Double-blind? The retrieved publisher record does not establish whether participants or outcome assessors were blinded.

Read the paper