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ChatGPT hints help adults learn algebra and statistics online

In a UC Berkeley experiment with 274 Mechanical Turk workers in 2023, ChatGPT-written math hints gave a 17% pre-to-post gain, the only gain significant against a no-hint control.

2
Benefit status
Reported
Evidence
Independently studied
Observed
Sources
1 publication

Benefits

Benefit

The ChatGPT group showed a 17.00 percent learning gain from pre-test to post-test.

Awaiting review

Outcome identifiers

Unresolved

Not reviewedUnresolved

Category

Improved Learning

Awaiting review

Status

Reported benefit

Awaiting review

Beneficiary groups

People.

Awaiting review

Beneficiaries

Unresolved

Not reviewedUnresolved

Reach

Unresolved

Not reviewedUnresolved

Distribution and access

Unresolved

Not reviewedUnresolved

Duration

Unresolved

Not reviewedUnresolved

Expected?

Yes

Awaiting review

Costs and harms

Unresolved

Not reviewedUnresolved

AI system

System

ChatGPT 3.5 ("Dec 15 Version"), prompted to write worked-solution hints shown in the OATutor tutoring system

Awaiting review

Model

This is an explicit unresolved assignment: the sources on record do not settle this question.

Not reviewedUnresolved

Reported configuration

ChatGPT 3.5, "Dec 15 Version"

Awaiting review

Provider

Unresolved

Not reviewedUnresolved

Role

Unresolved

Not reviewedUnresolved

Configuration

Unresolved

Not reviewedUnresolved

Inputs and outputs

Unresolved

Not reviewedUnresolved

Autonomy

Unresolved

Not reviewedUnresolved

Workflow

ChatGPT wrote worked solutions that served as hints on textbook problems. Learners saw the hints in a tutoring system during short practice sessions.

Awaiting review

Human oversight

Unresolved

Not reviewedUnresolved

Relation to AI

Participants were randomly assigned to ChatGPT hints, human-tutor hints or no hints on the same problems, so the comparison isolates the effect of the ChatGPT hints.

Awaiting review

AI contribution

Participants were randomly assigned to ChatGPT hints, human-tutor hints or no hints on the same problems, so the comparison isolates the effect of the ChatGPT hints.

Awaiting review

Measurement

Studies

Randomized. [1]

Fully reviewed: supporting details for Studies

Review date: 29 October 2026.

Sources: [1]

Study design

Randomized. [1]

Fully reviewed: supporting details for Study design

Review date: 29 October 2026.

Sources: [1]

Measurement

Three-item pre-test and post-test on the assigned skill. [1]

Fully reviewed: supporting details for Measurement

Review date: 29 October 2026.

Sources: [1]

Setting and sample

274 Amazon Mechanical Turk workers with at least a high school degree, 28 February to 10 May 2023. [1]

Fully reviewed: supporting details for Setting and sample

Review date: 29 October 2026.

Sources: [1]

Measures

Percentage-point learning gain from pre-test to post-test. [1]

Fully reviewed: supporting details for Measures

Review date: 29 October 2026.

Sources: [1]

Comparison

No-hint control and human-tutor hints. [1]

Fully reviewed: supporting details for Comparison

Review date: 29 October 2026.

Sources: [1]

Results

17.00 percent gain with ChatGPT hints against 1.85 percent with no hints and 11.62 percent with human-tutor hints. [1]

Fully reviewed: supporting details for Results

Review date: 29 October 2026.

Sources: [1]

Additional value from AI

ChatGPT hints produced a gain significant against no hints and similar to human-tutor hints. [1]

Fully reviewed: supporting details for Additional value from AI

Review date: 29 October 2026.

Sources: [1]

Limitations

Adult crowd workers; short sessions; three-item tests; 32 percent of ChatGPT hints failed quality checks before mitigation. [1]

Fully reviewed: supporting details for Limitations

Review date: 29 October 2026.

Sources: [1]

Denominators

One session of about 10 to 20 minutes. [1]

Fully reviewed: supporting details for Denominators

Review date: 29 October 2026.

Sources: [1]

Materials for replication

Participant data at doi:10.6084/m9.figshare.23935269. [1]

Fully reviewed: supporting details for Materials for replication

Review date: 29 October 2026.

Sources: [1]

Money

Kind of value

The kind of monetary value is unclear.

Not reviewedUnresolved

Amount

The monetary value is not established.

Not reviewedUnresolved

Currency and prices

The currency and price basis are unclear.

Not reviewedUnresolved

Period and unit

The period and unit of the monetary estimate are unclear.

Not reviewedUnresolved

Realization

Whether the monetary value was realized is unclear.

Not reviewedUnresolved

Recipients and payers

The recipients and payers are unclear.

Not reviewedUnresolved

Gross or net

Whether the monetary figure includes costs is unclear.

Not reviewedUnresolved

Included costs

The costs included in the estimate are unclear.

Not reviewedUnresolved

Comparison

The financial comparison is not established.

Not reviewedUnresolved

Method

The method used to calculate monetary value is unclear.

Not reviewedUnresolved

Return and payback

The return on investment or payback period is not established.

Not reviewedUnresolved

People and places

Participants

Developer: OpenAI. Operator: University of California, Berkeley (Pardos and Bhandari), in an online study on Amazon Mechanical Turk.

Awaiting review

Jurisdictions

Benefit: Online (Amazon Mechanical Turk); study run from Berkeley, California, United States. Developer: United States. Operator: United States.

Awaiting review

Ownership and control

Unresolved

Not reviewedUnresolved

Dependencies

Unresolved

Not reviewedUnresolved

Cross-border connections

Unresolved

Not reviewedUnresolved

Geopolitical implications

The geopolitical implications are unclear.

Not reviewedUnresolved

Follow-up

Conditions for replication

Hints must be checked or generated several times to catch errors. Problems must be text only.

Awaiting review

Replication attempts

1 follow-up entry. Benefit did not recur: 1.

Awaiting review

Later findings

Unresolved

Not reviewedUnresolved

Availability

Unresolved

Not reviewedUnresolved

Institutional decision

A relevant institutional decision has not been established.

Not reviewedUnresolved

Grounds

The grounds for an institutional decision are unclear.

Not reviewedUnresolved

Action and result

The result of an institutional decision is not established.

Not reviewedUnresolved

Success criterion

The criterion for a successful institutional decision is unclear.

Not reviewedUnresolved

Evidence

Source numbers

AIBD-0618-source-0001

Awaiting review

Citations

Numbered references below.

Awaiting review

Language

Unresolved

Not reviewedUnresolved

Source relationships

Independent

Awaiting review

Original evidence

Unresolved

Not reviewedUnresolved

Access and versions

Unresolved

Not reviewedUnresolved

Supported findings

Unresolved

Not reviewedUnresolved

Evidence strength

Independently studied. Sources: one independent peer-reviewed study. The cited evidence includes no finding by a regulator or court. [1]

Fully reviewed: supporting details for Evidence strength

Review date: 29 October 2026.

Sources: [1]

Missing evidence

Unresolved

Not reviewedUnresolved

Grades

Assessed benefits

People.

Awaiting review

Depth

People: 2.

Awaiting review

Reasons

People: Participants given ChatGPT hints showed a 17.00 percent learning gain from pre-test to post-test, significant against a no-hint control.

Awaiting review

Observed or intended

Unresolved

Not reviewedUnresolved

Rubric

Unresolved

Not reviewedUnresolved

Assessor and date

Unresolved

Not reviewedUnresolved

Alternative assessments

Unresolved

Not reviewedUnresolved

Fill

Independently studied

Awaiting review

Entry details

Kind

The case describes an evaluation or a study. The case is a randomized online experiment. [1]

Fully reviewed: supporting details for Kind

Review date: 29 October 2026.

Sources: [1]

Field

Education.

Awaiting review

Purpose

ChatGPT is a general-purpose chatbot. The authors used it to write hints for tutoring software, and learning from those hints was the purpose of the study.

Awaiting review

Dates

Observed: 10 May 2023. Updated: 29 October 2026.

Awaiting review

Stage

The system was tested in a laboratory. The hints were tested in a short online experiment with crowd workers. [1]

Fully reviewed: supporting details for Stage

Review date: 29 October 2026.

Sources: [1]

Version

SHA-256: 187ea83521fb…

Awaiting review: supporting details for Version

SHA-256 of the public case, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.

References

Other identifiers

Unresolved

Not reviewedUnresolved

Category mappings

Unresolved

Not reviewedUnresolved

Citable summary

Unresolved

Not reviewedUnresolved

Revisions

1 revision. Latest: 29 October 2026.

Awaiting review

Sources

  1. Peer-reviewed study Independent author

    Pardos ZA, Bhandari S. ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PLoS One. 2024;19(5):e0304013. doi:10.1371/journal.pone.0304013

Cite this case

BibTeX Download BibTeX

Version 187ea83521fb, archived in the edition of 29 October 2026

Benefit marks

Depth

The number and color show the depth of a benefit.

  1. Depth 1. A convenience, or time or money saved.
  2. Depth 2. A measurable improvement in a result or ability.
  3. Depth 3. A lasting change, an ability restored, or a risk removed.
  4. Depth 4. A serious harm averted or a serious condition resolved in time.
  5. Depth 5. A life or livelihood preserved, or a change documented across a population.

Beneficiary

  • Circle: a person
  • Square: an organization
  • Triangle: society

Evidence

  • Solid. A court or regulator found a benefit, or researchers independently studied it.

  • Hatched. Independent reporting.

  • Outline. Claimed by the parties. Only the people or organizations involved, or sources whose independence has not been assessed, support the benefit.

The fill changes with the evidence. The depth color stays the same.

How benefits are assessed