Gains:
- Ability to establish a personalized and repeatable research workflow that integrates artificial intelligence at all stages from idea to publication
- Ability to link the verification, confidentiality and transparency checkpoints to be applied at each stage to a written protocol
- Being able to evaluate the limits of the use of artificial intelligence, that the responsibility always lies with the researcher, and the ethical basis as a whole
In the previous ten units, we learned how to use AI and where to stop at each stage of research. This final unit ties it all into one coherent workflow: a repeatable and ethical process that safely integrates AI from idea to publication. The core idea of this unit is that AI is not the driver at any stage of research; At each stage there is an assistant, and at each stage the same three gates are passed—verification, confidentiality, transparency—ultimate responsibility always lies with the researcher. The good news is that these three gates are the same in all stages; once you bind them to a protocol it applies to the entire process.
End-to-end flow: step by step
Research typically progresses in seven phases, with AI taking on a different assistant role in each.
1. Idea and question. AI narrows your focus to focused, measurable questions; offers alternative frameworks. The decision is yours.
2. Literature. AI generates search strategy (keyword, Boolean string); You find and verify sources from real databases.
3. Reading and synthesis. AI summarizes full texts, builds literature matrix; You confirm each finding from the original text.
4. Design. AI offers hypothesis, variable, design and sample options; You and the ethics committee make the methodological and ethical decision.
5. Analysis. AI produces code draft and output comment; The software does the math, the raw data remains private, you verify every number.
6. Spelling. The AI IMRaD outline provides flow control and language polish; Original ideas, real data and your voice come from you.
7. Shipping. AI produces journal benchmarks, cover letters and reviewer response drafts; You ensure journal authenticity and scientific accuracy.
A pattern is seen in this flow: AI always speed and draft, you always decision and verification. This symmetry is the backbone of the workflow.
Three doors: verification, privacy, transparency
At each stage, pass the AI output through three gates before using it.
Verification gate: Is every fact, number, attribution, and claim in this output linked to a real source? Has anything that could be fabricated (especially references and statistics) been independently verified? Any unverified output will not proceed.
Privacy gate: Does the data I upload to this tool contain personally identifiable information, unpublished data, participant statements or confidential referee reports? If so, have I anonymized it or never uploaded it? Do I know the vehicle's data retention policy?
Transparency gate: Do I need to declare this usage? Does it comply with the institution and journal policy? Did I keep a record of what I did so that I could explain it in the future?
Tip: Write these three doors on the edge of your desk. Don't proceed until you've said "yes" to all three before using each AI output. Over time, it becomes a reflex and stops most errors before they occur.
Writing your personal protocol
The most valuable outcome is a written AI usage protocol of your own: what tools you use, what you do and don't have the AI do at each stage, your verification steps, and your statement statement. This protocol keeps you consistent, aligns new team members, and proves your integrity in an audit. Keep the protocol alive; Update as tools and policies change.
If you work in a team, this protocol becomes a shared contract: everyone using the same tools with the same limits, a record of who is leveraging AI at what stage, and a common declaration language. Thus, while one writer receives language support, another does not unknowingly fabricate data; discipline becomes institutional rather than individual. This transparency is also critical in the advisor-student relationship: the advisor knowing where the student is using AI preserves trust and ensures that the learning truly stays with the student. The protocol transforms the AI from a “hidden assistant” into an openly managed tool.
three mini cases
Case 1 — Thesis saved by the protocol. A PhD student wrote a usage protocol from the start: “attributions only from Zotero, AI only language and outline, every issue from software.” When the jury asked about the use of AI in his thesis defense, he showed his recording and statement. Transparency created trust; The defense went smoothly. Without the protocol, the same questions could have turned into doubt.
Case 2 — Error caught by three gates. A researcher would add a “supporting study” suggested by AI to the discussion section. Verification stopped at the door: DOI unresolved, source fabricated. He confirmed that he did not upload the raw data at the privacy gate. He added language support for the transparency gate to his statement. All three doors worked one by one; The erroneous attribution stopped before it could go to air.
Case 3 — End-to-end speed. A team started a new project with a protocol; each member practiced the same three gates. They used AI consistently, from literature to writing, and the overall time was significantly shorter, but there was not a single fabricated attribution or breach of confidentiality. The difference was not that they used AI, but that they used it with a common discipline.
Copiable templates
1) Personal AI usage protocol framework:
Produce me a "protocol for use of AI in research" template. Sections: (1) the tools I use, (2) the allowed and prohibited actions of AI at each research stage, (3) my verification steps, (4) privacy rules, (5) my transparency/declaration statement. Provide an empty fillable frame.
2) Three-door control per stage:
Currently [stage: e.g. I'm in the [analysis] phase and I'm considering using the following AI output: [output summary]. Generate me control questions for three doors: authentication, privacy, transparency. What should I check at each door?
3) End-to-end verification log:
Produce a validation log table for a research project: [phase, what was the AI used for, output, validation method, was it validated, is declaration required]. Let it be empty and fillable.
4) Final inspection before publication:
Produce a final AI-ethics checklist before submission: are all citations verified with DOI, no data has been fabricated, raw data has not been leaked, has use been declared, institution+journal policy has been met, is the original contribution mine. Make it bookmarkable.
Weak prompt / Strong prompt
Weak prompt:
How do I use artificial intelligence in my research?
Very general; It does not include stages, constraints and verification disciplines.
Powerful prompt:
Your role: research integrity consultant. My project: [short description].Produce me an end-to-end workflow table for 7 phases from idea to release, including the AI's permitted role, prohibited work, and verification/privacy/transparency controls at each phase. Maintain in every line that ultimate responsibility lies with me.
Stage
Role of AI
immutable door
idea/question
Collapse, alternative
It's your decision
literature
Creating strategies
verify source
synthesis
summary, matrix
Confirm the finding
Design
Don't offer options
Ethics + the decision is yours
Analysis
Code, comment
Validate number, protect data
spelling
draft, flow
Originality is yours
shipping
criterion, outline
Magazine + accuracy is yours
Common mistakes
- Relaxing discipline according to stage. The three gates are the same at each stage; Attention should not be lost towards the end.
- Not writing a protocol. Without written rules, consistency and auditability are lost.
- Making AI a driver. Must have speed assistant; The decision and validation is always yours.
- Skip recording. You can't prove transparency if you don't document what you're doing.
- Neglecting a single door. Authentication, privacy, and transparency work together; If one is missing, the chain breaks.
Caution: AI tools and institutional/journal policies change rapidly. An application that is valid today may be insufficient tomorrow. Review your protocol and policies regularly; Discipline is constant, tools are variable.
In summary
In the end-to-end research workflow, AI is a pacing and drafting assistant at every stage, not a driver at any stage. From idea to publication, the pattern is the same: AI produces, you decide and verify. Pass each output through three gates—authentication, privacy, transparency—and tie them to a personal protocol. This discipline combines the speed of AI with academic integrity. The shortest rule: assistant AI, driver and you in charge at every stage.
Application task
Write a one-page AI usage protocol for your own current or planned research: the tools you use, the 7 steps of AI allowed/prohibited work, your verification steps, privacy rules, and your declaration statement. Then select a stage, apply the three-gate control on an actual output, and commit the result to your verification log.
checklist
- [ ] Have I clarified the role and boundary of AI for each of the seven stages?
- [ ] Do I pass every output through the gates of verification, confidentiality, and transparency?
- [ ] Do I have a written personal AI usage protocol?
- [ ] Do I record my usage in a verification log?
- [ ] Do I maintain at every stage that I have ultimate scientific responsibility?
Module Exam
1. A doctoral student adds the 10 sources given by artificial intelligence for literature review to his thesis proposal without checking them; His advisor realizes that 3 of the sources do not actually exist. What is the main lesson of this situation?
- A) Using every source suggested by artificial intelligence without verifying it through DOI and database; Ignoring that the responsibility lies with the researcher ✔
- B) It is strictly forbidden to use artificial intelligence in literature review
- C) The number of resources should be 5 instead of 10
- D) The thesis proposal is not presented with a table
Explanation: Artificial intelligence can produce fluent but non-existent attributions (hallucinatory attribution). This is the most devastating risk in academia; Not every source can be used without verification through DOI, imprint and database, and the responsibility lies with the researcher.
2. Which is the most accurate positioning of artificial intelligence in literature review?
- A) As a database that provides a precise and complete list of sources
- B) As an aid to search strategy and keyword generation, but whose sources must be verified in real databases ✔
- C) As a checker that replaces the plagiarism detection tool
- D) As an archive that provides the full text of the articles royalty-free
Explanation: AI is not a reliable source database; The imprints given may be fabricated. The correct role is to generate the search strategy (keyword, Boolean string, scope); The sources themselves must be found and verified in real databases (Scholar, Scopus, WoS, PubMed).
3. A researcher has the AI summarize an article, and the summary includes a 'significant difference found' statement that is not in the article. What is correct behavior?
- A) Accepting the abstract as is, because the AI has read the article
- B) Citing the abstract as a source
- C) Verify the findings and conclusion statements by returning to the original article text ✔
- D) Asking to make the summary shorter
Explanation: When summarizing, AI may distort the finding or add conclusions that do not exist. Every statement containing findings, methods and numerical results should be verified by returning to the original text; The summary is a convenience and does not replace the source.
4. Which statement is most accurate for the role of artificial intelligence in research design?
- A) Artificial intelligence selects the method and replaces ethics committee approval
- B) Artificial intelligence finalizes the sample size and the decision does not remain with the researcher
- C) Artificial intelligence generates options and drafts; Design, ethical and methodological responsibility lies with the researcher ✔
- D) Artificial intelligence cannot be used in any way in research design
Description: Artificial intelligence is a thought partner that generates options for hypotheses, variables and methods; However, design selection, ethics committee (IRB) approval and methodological responsibility belong to the researcher. The artificial intelligence proposal is a draft, not a scientific decision.
5. What is the most critical privacy and authentication principle when having artificial intelligence produce data analysis code drafts?
- A) Loading the raw data as it is and directly reporting the result given by artificial intelligence
- B) Writing the p value given by artificial intelligence without checking it
- C) Leaving the statistical test to be chosen by artificial intelligence and not asking for justification
- D) Draft code and manually verify each digital output without sharing raw data containing ID ✔
Explanation: Raw and identifying data of participants should not be uploaded to publicly available tools; The code draft must be requested and run locally, and each output (test selection, numerical result) must be confirmed manually. Artificial intelligence can also produce fake statistics.
6. What is the correct order of the sections in the IMRaD structure?
- A) Method, Introduction, Discussion, Findings
- B) Findings, Method, Introduction, Discussion
- C) Discussion, Findings, Method, Introduction
- D) Introduction, Method, Findings, Discussion ✔
Description: IMRaD is the standard framework of empirical articles: Introduction, Methods, Results, and Discussion. Specifying which section you are writing for when requesting a draft from artificial intelligence increases the quality of the output.
7. What is the best approach in terms of originality when adding an artificial intelligence-generated text to an academic article?
- A) Using the draft as it is, because the language is fluent
- B) Originalize and validate the draft with your own analysis, data and interpretation ✔
- C) Pasting the draft into the text without reading it
- D) Hiding the source of the draft and presenting the entire draft as its own sentence
Description: The AI output is a draft; It is not the researcher's own opinion, data or interpretation. The manuscript should be originalized by the author's own analysis, contribution and verification; It should not be used as is and presented as a contribution.
8. A non-native English writer uses artificial intelligence for language polishing. Which risk should be specifically audited?
- A) The text should be shorter
- B) Shift of meaning and mistranslation of field terms ✔
- C) Changing the font
- D) Increasing the number of paragraphs
Explanation: When correcting language, AI can shift the meaning, replacing the domain-specific technical term with the wrong equivalent. The author must preserve the original meaning and terminology of each sentence; Content should not be allowed to be distorted for the sake of fluency.
9. What is the most dangerous behavior and correct precaution of artificial intelligence in attribution production?
- A) Making up references that appear real but do not exist; ✔ verify each citation via DOI and database
- B) Alphabetical order of citations; break the order manually
- C) Too many citation suggestions; reduce the number
- D) Ability to change the citation style; lock to one style
Description: AI can create bylines (including author, year, DOI) that appear real but do not exist. Each citation should be independently verified by DOI analysis or database search; The safest way is to manage citations from the actual record with tools like Zotero/Mendeley.
10. What is one of the typical signs of recognizing a predatory magazine?
- A) Promise of fast publication, aggressive email invitation and unrealistic impact factor claim ✔
- B) Transparent referee process and inclusion in recognized directories
- C) Documenting the open access policy
- D) The editorial board is real and verifiable
Explanation: Predatory journals charge fees with the promise of fast publication and do not do real peer review; Aggressive email invitation, unreal impact factor, vague imprint and broad out-of-domain coverage are typical signs. Journals recommended by artificial intelligence should be verified with DOAJ and independent sources.
11. According to the AI policy of most academic journals and institutions, what is appropriate for the use of AI in an article?
- A) If the artificial intelligence contribution is significant, it should be added as a co-author
- B) Artificial intelligence cannot be a writer; its use should be declared transparently and responsibility should remain with the human author ✔
- C) Usage is not declared under any circumstances and is kept confidential
- D) The use of artificial intelligence is plagiarism in all cases and the article will be rejected.
Disclosure: Common editorial principle (e.g. the ICMJE and COPE line) says that the AI cannot be the author, but that its use must be transparently disclosed in the method or acknowledgments/acknowledgment section. Responsibility lies with human authors and use is not concealed.
12. What is self-plagiarism?
- A) Using another author's text without permission
- B) Using the text produced by artificial intelligence
- C) The author re-presents his own previous publication without appropriate attribution ✔
- D) Writing an article with more than one author
Explanation: Self-plagiarism is the author's re-presentation of his own previously published work in a new work without proper attribution. Even if artificial intelligence rewrites the old text, if the same idea/data is repeated without attribution, it is an ethical violation; The obligation to cite sources is not eliminated.
13. What is the best limit for an author using artificial intelligence when preparing responses to referee comments?
- A) Artificial intelligence guarantees the scientific accuracy of the answer, the author may not check it
- B) It is safe to upload the referee report as it is to a public tool.
- C) It is the most efficient way to leave the answer entirely to artificial intelligence.
- D) AI can generate drafts/tones; Responsibility for scientific accuracy and confidentiality lies with the author ✔
Description: AI can generate the tone and outline of the response letter; However, the responsibility for the accuracy of scientific claims, additional analysis and promises lies with the author. Additionally, the referee report is a confidential document; Content should not be shared without anonymization due to the risk of privacy violation.
14. What is the common unchanging principle of using artificial intelligence in the end-to-end research workflow?
- A) Verification, confidentiality and transparency at every stage; ✔ Final responsibility remains with the researcher
- B) Validation should be done only at the last stage to save time
- C) Less verification of the AI output as the stage progresses
- D) Transparency is required only for rejected articles
Description: From literature to publication, AI provides drafting and speed at every stage; However, every output must be validated, data confidentiality must be protected, use must be transparently declared, and final scientific responsibility must always remain with the researcher. This principle is constant at all stages.