Why English? | Using GAMER to Disclose Generative AI Across Medical Research, Writing, Verification, Privacy and Conclusions

ORIENTATION

What this article owns

Current fact check

The GAMER Statement was published online in BMJ Evidence-Based Medicine in May 2025. Its final checklist contains nine items for transparent reporting of generative-AI use.

Scope boundary

GAMER owns the intent of disclosing generative AI used during a medical study or manuscript. It differs from a study that evaluates an LLM as the research object.

Worked example

Chapters use a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing. The example is fictional and educational; it is not health advice, a clinical recommendation or a claim that the model should be deployed.

Warm up with eduKate’s guides to explaining scientific evidence and asking better questions in class.

CHAPTER 1 OF 16

1. Ask the First Question: Was Generative AI Used?

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Understand the mechanism

Declare generative-AI use in any study or manuscript step, including ideation, code, analysis, data transformation, drafting and editing.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The study states that AI suggested code, drafted a table narrative and edited language, but did not choose participants or outcomes.

For “Ask the First Question: Was Generative AI Used?”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Follow the evidence trail

Apply this move to “Ask the First Question: Was Generative AI Used?”: Start with the exact sentence in the methods, then locate the appendix, table or repository record that makes it testable. If the detail is absent, write unknown rather than imagining the most favourable condition.

Limit and next action

Disclosure alone does not establish appropriate or accurate use.

Next, rewrite “Ask the First Question: Was Generative AI Used?” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Ask the First Question: Was Generative AI Used?” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 2 OF 16

2. Distinguish Assistance From Authorship and Accountability

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Understand the mechanism

Name what the system generated and what people reviewed, modified, accepted or rejected; humans retain responsibility for the final work.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

An analyst tests three suggested code fragments, corrects a data-type error and writes the final script.

For “Distinguish Assistance From Authorship and Accountability”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Put the wording to work

Apply this move to “Distinguish Assistance From Authorship and Accountability”: Translate the technical statement into an everyday explanation, then translate it back. The two versions should preserve the same actor, action, evidence and limit even when their vocabulary differs.

Limit and next action

A human signature cannot rescue incompetent review.

Next, rewrite “Distinguish Assistance From Authorship and Accountability” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Distinguish Assistance From Authorship and Accountability” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 3 OF 16

3. Create a Workflow Map Before Writing the Disclosure

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Understand the mechanism

Map planning, data handling, analysis, interpretation, drafting and revision, linking each interaction to input, output and reviewer.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The ledger shows that only a synthetic schema entered the tool and that aggregate results were checked before drafting.

For “Create a Workflow Map Before Writing the Disclosure”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Test the claim in context

Apply this move to “Create a Workflow Map Before Writing the Disclosure”: Ask what would change if the setting, user, date, language or access route changed. This counterfactual check reveals whether a broad conclusion rests on a narrow condition.

Limit and next action

A later map cannot recover undocumented use perfectly.

Next, rewrite “Create a Workflow Map Before Writing the Disclosure” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Create a Workflow Map Before Writing the Disclosure” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 4 OF 16

4. Specify the Tool, Version, Release and Use Dates

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Understand the mechanism

Report provider, product, version or release date, interface, account context and dates, while marking unavailable fields.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

Researchers record the named web workspace, displayed model label and dates without exposing credentials.

For “Specify the Tool, Version, Release and Use Dates”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Build a reproducible note

Apply this move to “Specify the Tool, Version, Release and Use Dates”: Record the fact, its source, the date checked and the consequence for interpretation. A short, well-sourced note is more useful than a page of confident paraphrase.

Limit and next action

Version reporting cannot preserve a proprietary service forever.

Next, rewrite “Specify the Tool, Version, Release and Use Dates” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Specify the Tool, Version, Release and Use Dates” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 5 OF 16

5. Record Prompting Techniques and Unedited Responses

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Understand the mechanism

Preserve exact instructions, examples, attachments, message order and iterations, and provide raw responses when lawful and safe.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The repository pairs a synthetic schema and coding request with raw suggestions, tested code and a change note.

For “Record Prompting Techniques and Unedited Responses”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Separate evidence from interpretation

Apply this move to “Record Prompting Techniques and Unedited Responses”: Use two columns. Put directly reported facts on the left and your explanation on the right, then connect them with a cautious verb such as suggests, supports or remains uncertain.

Limit and next action

Confidential prompts may need a safe substitute and an explained restriction.

Next, rewrite “Record Prompting Techniques and Unedited Responses” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Record Prompting Techniques and Unedited Responses” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 6 OF 16

6. Describe What the Tool Did at Each Research Stage

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Understand the mechanism

Use concrete verbs such as suggested, summarised, transformed, drafted or generated code, paired with a stage and human review.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

AI suggests code during analysis, drafts two verified results sentences and shortens prose in discussion.

For “Describe What the Tool Did at Each Research Stage”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Check the decision boundary

Apply this move to “Describe What the Tool Did at Each Research Stage”: Name the decision that the study did and did not examine. This prevents performance language from sliding into claims about safety, benefit, admission, employment or clinical use.

Limit and next action

A precise verb does not prove the role was ethical or sound.

Next, rewrite “Describe What the Tool Did at Each Research Stage” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Describe What the Tool Did at Each Research Stage” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 7 OF 16

7. Disclose New or Fine-Tuned Models and Their Origins

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Understand the mechanism

If a model was developed or fine-tuned, report its base model, version, adaptation method, data provenance and resulting identity.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The fictional team performs no fine-tuning and marks the item not applicable instead of implying novelty.

For “Disclose New or Fine-Tuned Models and Their Origins”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Compare like with like

Apply this move to “Disclose New or Fine-Tuned Models and Their Origins”: Before comparing results, verify that task, population, input, prompt, model, outcome and analysis are aligned. A larger number is not more informative when the conditions differ.

Limit and next action

Naming a base model does not settle licensing, consent or representativeness.

Next, rewrite “Disclose New or Fine-Tuned Models and Their Origins” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Disclose New or Fine-Tuned Models and Their Origins” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 8 OF 16

8. Point to the Exact AI-Assisted Manuscript Sections

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Understand the mechanism

Identify the paragraphs, tables, figures, code listings or supplements that contain AI-assisted work and state the contribution.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The disclosure points to two Results sentences and three clarity edits in Discussion, all checked against outputs.

For “Point to the Exact AI-Assisted Manuscript Sections”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Explain the handover

Apply this move to “Point to the Exact AI-Assisted Manuscript Sections”: Imagine that a new teammate must continue the project tomorrow. Give that person the minimum record needed to reproduce the step and recognise its unresolved risks.

Limit and next action

Word-level annotation can become unreadable while a generic acknowledgement is too vague.

Next, rewrite “Point to the Exact AI-Assisted Manuscript Sections” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Point to the Exact AI-Assisted Manuscript Sections” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 9 OF 16

9. Verify Facts, Calculations, Citations and Code Separately

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Understand the mechanism

Match verification to the artefact: source checks for facts, recomputation for numbers, tests for code and publisher checks for citations.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The analyst runs unit tests, reproduces counts and removes an invented package option before finalising the script.

For “Verify Facts, Calculations, Citations and Code Separately”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Follow the evidence trail

Apply this move to “Verify Facts, Calculations, Citations and Code Separately”: Start with the exact sentence in the methods, then locate the appendix, table or repository record that makes it testable. If the detail is absent, write unknown rather than imagining the most favourable condition.

Limit and next action

Verification reduces discovered errors but cannot promise perfection.

Next, rewrite “Verify Facts, Calculations, Citations and Code Separately” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Verify Facts, Calculations, Citations and Code Separately” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 10 OF 16

10. Protect Privacy Before Sending Any Input

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Understand the mechanism

Classify personal, confidential, proprietary and embargoed material before prompting; report retention, access and approved-environment controls.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

Only a synthetic schema and disclosure-checked aggregate tables enter the generative system; participant records do not.

For “Protect Privacy Before Sending Any Input”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Put the wording to work

Apply this move to “Protect Privacy Before Sending Any Input”: Translate the technical statement into an everyday explanation, then translate it back. The two versions should preserve the same actor, action, evidence and limit even when their vocabulary differs.

Limit and next action

Vendor settings do not replace law, consent or institutional policy.

Next, rewrite “Protect Privacy Before Sending Any Input” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Protect Privacy Before Sending Any Input” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 11 OF 16

11. Separate Language Editing From Translation and Evidence

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Understand the mechanism

Editing can improve form without supplying evidence, while translation introduces additional meaning choices and may follow different guidance.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The tool shortens verified English sentences; a separate human-reviewed translation process is described elsewhere.

For “Separate Language Editing From Translation and Evidence”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Test the claim in context

Apply this move to “Separate Language Editing From Translation and Evidence”: Ask what would change if the setting, user, date, language or access route changed. This counterfactual check reveals whether a broad conclusion rests on a narrow condition.

Limit and next action

Calling a change editing does not make altered certainty harmless.

Next, rewrite “Separate Language Editing From Translation and Evidence” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Separate Language Editing From Translation and Evidence” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 12 OF 16

12. Document Human Revision and Rejected Outputs

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Understand the mechanism

Record material corrections and rejected suggestions that affect facts, analysis, interpretation, privacy or risk.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The team rejects a causal verb, restores the correct denominator and records both decisions in the ledger.

For “Document Human Revision and Rejected Outputs”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Build a reproducible note

Apply this move to “Document Human Revision and Rejected Outputs”: Record the fact, its source, the date checked and the consequence for interpretation. A short, well-sourced note is more useful than a page of confident paraphrase.

Limit and next action

Saving every trivial spelling change can bury important revisions.

Next, rewrite “Document Human Revision and Rejected Outputs” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Document Human Revision and Rejected Outputs” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 13 OF 16

13. Explain Possible Influence on Accuracy and Conclusions

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Understand the mechanism

Assess direct and indirect pathways by which AI could change numbers, emphasis, categories, interpretation or reader perception.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

Authors state that code was independently reproduced but draft prose may have influenced early emphasis, which was reviewed.

For “Explain Possible Influence on Accuracy and Conclusions”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Separate evidence from interpretation

Apply this move to “Explain Possible Influence on Accuracy and Conclusions”: Use two columns. Put directly reported facts on the left and your explanation on the right, then connect them with a cautious verb such as suggests, supports or remains uncertain.

Limit and next action

Self-assessment can miss subtle influence even when artefacts are preserved.

Next, rewrite “Explain Possible Influence on Accuracy and Conclusions” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Explain Possible Influence on Accuracy and Conclusions” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 14 OF 16

14. Pair GAMER With the Study's Design Guideline

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Understand the mechanism

Use GAMER for AI disclosure alongside CONSORT, STROBE, PRISMA, SPIRIT or other design-specific guidance.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

The observational label-reading study uses STROBE for design and GAMER for generative-AI roles.

For “Pair GAMER With the Study's Design Guideline”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Check the decision boundary

Apply this move to “Pair GAMER With the Study's Design Guideline”: Name the decision that the study did and did not examine. This prevents performance language from sliding into claims about safety, benefit, admission, employment or clinical use.

Limit and next action

Several checklists cannot turn weak evidence into strong evidence.

Next, rewrite “Pair GAMER With the Study's Design Guideline” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Pair GAMER With the Study's Design Guideline” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 15 OF 16

15. Use a Safe Student Research Ledger

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Understand the mechanism

Log goal, prompt, output, verification, revision and final use on fictional or public data under assessment rules.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

A student uses AI for headings, rejects an unsupported category and verifies every source independently.

For “Use a Safe Student Research Ledger”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Compare like with like

Apply this move to “Use a Safe Student Research Ledger”: Before comparing results, verify that task, population, input, prompt, model, outcome and analysis are aligned. A larger number is not more informative when the conditions differ.

Limit and next action

Disclosure does not excuse prohibited use or replace learning.

Next, rewrite “Use a Safe Student Research Ledger” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Use a Safe Student Research Ledger” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

CHAPTER 16 OF 16

16. Connect Disclosure Skills to Further Study and Work

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Understand the mechanism

Transfer versioning, privacy review, output testing, limitation writing and decision ownership into research, publishing and quality assurance.

This is an English skill because the reader must connect a noun to a real object, a verb to an observable action and a conclusion to defined evidence. In a fictional observational study of how students read medication labels, using generative AI for code suggestions, a draft table description and language editing, vague wording would hide which condition produced the result. Precise wording gives another person something that can be checked, repeated or responsibly challenged.

See the mechanism in action

A portfolio contains a redacted ledger, verification matrix and comparison of vague versus decision-useful disclosure.

For “Connect Disclosure Skills to Further Study and Work”, the example is deliberately bounded. First identify what the researchers controlled, then identify what remained outside their control. That two-step reading prevents a fluent explanation from quietly becoming a stronger claim than the study earned.

Explain the handover

Apply this move to “Connect Disclosure Skills to Further Study and Work”: Imagine that a new teammate must continue the project tomorrow. Give that person the minimum record needed to reproduce the step and recognise its unresolved risks.

Limit and next action

Documentation supports opportunity but guarantees neither publication nor employment.

Next, rewrite “Connect Disclosure Skills to Further Study and Work” as a question, answer it from the original paper and label the answer reported, inferred or unknown. Then explain the same answer to a parent or classmate without removing its uncertainty. If the simple version sounds more certain, revise it.

Study and career transfer: “Connect Disclosure Skills to Further Study and Work” builds a reusable habit for research integrity, scholarly communication, data stewardship and collaborative authorship. A learner who names conditions, evidence and limits can contribute to handovers, reviews and group decisions without pretending to know more than the record shows. That combination of curiosity and restraint is practical English at work.

PRACTICE LAB

Move from a headline to an evidence trail

Student route

1. Choose one original paper that falls inside the scope of “Build an honest ledger of where generative AI touched a research project”.
2. Underline every task verb, user, dataset, model and outcome in its objective.
3. Build a one-page chain from input to output, evaluation and conclusion.
4. Mark every field reported, inferred or unknown.
5. Rewrite the conclusion so it says only what the design supports.

Parent or teacher route

For gamer, ask for one everyday explanation and one technical explanation. Both should preserve dates, evidence and limitations. Praise a well-stated unknown: recognising missing evidence is disciplined reading, not failure.

Early-career route

Create a compact gamer audit sheet for identity, access, inputs, data separation, prompts, evaluation, uncertainty, limitations and materials. Link each factual entry to its primary source so a colleague can verify it.

Continue with eduKate’s guide to reading course and career information and its practical advice on useful feedback in group projects.

CONNECT THE SERIES

Use one canonical guide for each purpose

From the gamer perspective, read the TRIPOD-LLM guide for broad modular reporting and the MINIMAR guide for population, architecture, evaluation and transparency. For imaging AI, compare CLAIM 2024 reporting with MI-CLAIM study design and reproducibility.

“Build an honest ledger of where generative AI touched a research project” keeps its narrower intent. These links are contextual next reading, not a repetitive link list or an attempt to create another broad English hub.

For research integrity, scholarly communication, data stewardship and collaborative authorship language practice, visit the Singapore English Tuition Centre learning site.