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7 Kemampuan AI untuk Workflow Riset: Tutorial Lengkap Claude & ChatGPT

Tutorial lengkap untuk menerapkan 7 kemampuan AI dalam workflow riset: literature review, novelty check, claim verification, experiment planning, reviewer simulation, reviewer response/rebuttal, dan research workflow dengan Claude dan ChatGPT.

25 menit·8 Oktober 2026
Daftar Isi

7 Kemampuan AI untuk Workflow Riset

<callout icon="🔬" color="blue_bg">

Bukan 7 prompt terpisah. Ini satu research workflow. Tutorial ini menunjukkan bagaimana AI dapat membantu dari research question sampai submission: mencari dan mensintesis literatur, menguji novelty, memverifikasi claims, merancang eksperimen, mensimulasikan reviewer, menyiapkan rebuttal, lalu menghubungkan semuanya menjadi satu evidence-traced workflow. </callout>

Prinsip utama

Masalahnya bukan AI dipakai dalam riset. Masalahnya ketika AI mengambil alih proses berpikir researcher. Gunakan mental model:

Researcher → AI-assisted workflow → Evidence → Researcher judgment → Decision

AI membantu memperluas pencarian, membuat struktur, menemukan kelemahan, membandingkan prior work, dan melakukan pemeriksaan berulang. Researcher tetap memegang research question, scope, keputusan metodologis, interpretasi evidence, dan final claims.

7 kemampuan yang akan dibangun

#CapabilityFungsiOutput utama
1Literature ReviewMencari, menyaring, menginspeksi, dan mensintesis prior work.Source analysis matrix
2Novelty CheckMembandingkan ide dengan closest prior work dan mencari defensible delta.Novelty gate
3Claim VerificationMenelusuri setiap klaim penting kembali ke evidence.Claim ledger
4Experiment PlanningMengubah hypothesis menjadi pilot dan eksperimen yang dapat diuji.Experiment / pilot protocol
5Reviewer SimulationMencari objection sebelum submission.Reviewer risk report
6Reviewer Response / RebuttalMen-triage komentar dan menyusun respons berbasis evidence.Response matrix
7Research WorkflowMenghubungkan semua capability menjadi lifecycle yang konsisten.Evidence-traced research system

Diagram 1 — Gambaran besar workflow

mermaid
flowchart LR
    H["Researcher<br>question, scope, judgment"] --> I["Idea + Scope"]
    I --> L["Literature Review"]
    L --> N["Novelty Check"]
    N --> E["Experiment Planning"]
    E --> V["Claim Verification"]
    V --> R["Reviewer Simulation"]
    R --> W["Writing / Revision"]
    W --> B["Reviewer Response"]
    B --> F["Final Verification"]
    F --> S["Submission"]
    V -. evidence .-> L
    R -. objections .-> E
    B -. revision .-> W
    H -. approval gates .-> N
    H -. final judgment .-> F

Penting: urutan di atas bukan berarti setiap riset harus linear. Reviewer simulation dapat dilakukan lebih awal, claim verification dilakukan berulang, dan novelty dapat memaksa scope kembali ke tahap awal.


0. Repository yang digunakan sebagai fondasi

Tujuh capability di atas bukan tujuh repository yang harus di-install semuanya. Repository utama yang paling dekat dengan keseluruhan workflow adalah Academic Research Agent Skill oleh ngtiendong. README-nya secara eksplisit mencakup literature review, novelty checks, research reality/feasibility gates, experiment planning, reviewer simulation, dan claim verification. Repository tersebut juga menyediakan SKILL.md, CLAUDE.md, command prompts, agent roles, references, examples, dan Mermaid visualizations. citeturn0search0turn0search3 Untuk manuscript review dan rebuttal, paper-lifecycle menyediakan dua skill terpisah: review-revision dan rebuttal-response. citeturn0search2 Untuk academic writing/revision end-to-end, academic-writing-skills menggunakan open Agent Skills format dan dokumentasinya menyebut Claude Code, ChatGPT, Codex, OpenCode, Hermes Agent, serta agent yang kompatibel dengan format tersebut. citeturn0search1

Repository map

RepositoryDipakai untukPosisi dalam tutorial
Academic Research Agent SkillScope, literature, novelty, feasibility, experiment, reviewer simulation, claim verificationFondasi utama
paper-lifecycleReview/revision dan rebuttal responseCapability 5–6
academic-writing-skillsManuscript architecture, drafting, review, revision, submissionWriting layer

Jangan install repository hanya karena capability-nya ada. Pilih skill berdasarkan tahap riset yang sedang dikerjakan.


1. Setup — Claude Code

Step 1 — Clone repository utama

bash
git clone https://github.com/ngtiendong/Academic-Research-Agent-Skill.git
cd Academic-Research-Agent-Skill
cp config/language.example.yaml config/language.yaml

Repository saat ini menyediakan SKILL.md, CLAUDE.md, .claude/commands/, _agents/, agents/, references/, examples/, dan config/. citeturn0search0

Step 2 — Baca kontrak skill

Jangan langsung meminta AI membuat paper. Mulai dengan:

plain
Read SKILL.md and CLAUDE.md.

Do not write a paper yet.

First explain:
1. the current research workflow,
2. the evidence gates,
3. what evidence is required at each gate,
4. what decisions must remain with the researcher,
5. what information you need from me before proceeding.

SKILL.md sendiri menetapkan bahwa researcher memegang direction, constraints, approvals, dan final claims; evidence harus dipisahkan dari agent inference dan researcher hypothesis. citeturn0search3

Step 3 — Masukkan research idea

Contoh:

plain
My research idea is:

"Can method X improve outcome Y under condition Z?"

Do not assume this idea is novel.

First:
1. turn it into a precise research question,
2. define the scope,
3. list non-goals,
4. identify the candidate contribution,
5. identify the evidence required,
6. identify the closest competing approaches,
7. tell me what decision I need to make next.

Do not write the paper yet.

2. Setup — ChatGPT

Ada dua cara memakai capability ini.

A. Environment yang mendukung Agent Skills

Jika environment ChatGPT yang digunakan menyediakan Skills/Agent Skills yang kompatibel, gunakan skill yang tersedia dan berikan source material yang memang diperlukan. Repository academic-writing-skills mendokumentasikan penggunaan open Agent Skills format pada ChatGPT dan Codex. citeturn0search1

B. ChatGPT sebagai research assistant

Jika tidak memakai repository skill loader, gunakan workflow secara portable. Berikan prompt:

plain
Act as a human-guided research assistant.

My role:
- I own the research question.
- I approve scope and major decisions.
- I make the final scientific judgment.
- I own the final claims.

Your role:
- organize and compare evidence,
- identify weak assumptions,
- help inspect prior work,
- test novelty,
- plan feasible experiments,
- verify important claims,
- simulate reviewers,
- help revise the manuscript.

Rules:
1. Never invent citations, data, results, datasets, or experimental outcomes.
2. Separate inspected evidence, inference, and hypothesis.
3. Do not call an idea novel just because you did not find an identical paper.
4. Do not strengthen a claim beyond its evidence.
5. Mark missing information instead of filling gaps.
6. Ask for approval at major decision gates.
7. Do not write the full paper before scope, evidence, and claims are sufficiently established.

My research idea:
[INSERT IDEA]

Start with scope and evidence requirements.

Catatan: portable workflow tidak sama dengan native installation. Jangan mengklaim bahwa setiap repository GitHub dapat di-install identik di ChatGPT chat biasa.


3. Capability 1 — Literature Review

Tujuan

Literature review yang baik bukan:

"Carikan 20 jurnal tentang topik X." Gunakan pipeline:

mermaid
flowchart LR
    Q["Research Question"] --> S["Search Strategy"]
    S --> C["Collect Sources"]
    C --> F["Filter / Screen"]
    F --> I["Inspect Evidence"]
    I --> X["Extract"]
    X --> M["Compare"]
    M --> Y["Synthesize"]

Academic Research Agent Skill menekankan source inspection, evidence extraction, source analysis matrix, dan grounding terhadap closest prior work. citeturn0search0

Step 1 — Pecah research question

plain
I am researching:

[TOPIC / QUESTION]

Help me convert this into:

1. precise research question,
2. key concepts,
3. synonyms,
4. methodological terms,
5. inclusion criteria,
6. exclusion criteria,
7. evidence I need from each source.

Separate:
- facts already supported by my sources,
- assumptions,
- search hypotheses.

Do not invent papers or citations.

Step 2 — Buat search strategy

plain
Create a literature search strategy for this research question:

[QUESTION]

Return:
1. primary keywords,
2. synonyms,
3. related concepts,
4. methodological terms,
5. likely competing approaches,
6. terms that may produce false-positive results.

Do not treat your proposed search terms as evidence.

Step 3 — Screening paper

Berikan paper/notes yang benar-benar tersedia.

plain
For each paper I provide, extract:

1. research problem,
2. method,
3. dataset or context,
4. evaluation metric,
5. main result,
6. limitation,
7. relevance to my research question,
8. closest relationship to my proposed contribution.

For every important statement, identify the source location where possible.

If information is missing, write "not found".
Do not infer missing facts.

Output: Source Analysis Matrix

PaperProblemMethodData/ContextMetricResultLimitationRelevance
Paper A………………High
Paper B………………Medium
Paper C………………Low

Jangan berhenti pada ringkasan

Prompt lanjutan:

plain
Using only the inspected sources:

1. identify areas of agreement,
2. identify conflicting findings,
3. identify methodological differences,
4. identify unresolved gaps,
5. identify which gap is directly relevant to my research question.

Do not label a gap as "novel" yet.

4. Capability 2 — Novelty Check

Pertanyaan yang benar

Bukan:

"Apakah ide saya novel?" Tetapi: "Apa delta yang dapat dipertahankan setelah dibandingkan dengan closest prior work?"

Diagram novelty gate

mermaid
flowchart TD
    I["Proposed Contribution"] --> B["Break into<br>problem / method / context / data / metric"]
    B --> C["Find Closest Prior Work"]
    C --> D["Compare Contribution Delta"]
    D --> G{"Novelty Gate"}
    G -->|Weak / same contribution| R["Revise Scope"]
    R --> I
    G -->|Defensible delta| P["Proceed"]

Prompt 1 — Pecah kontribusi

plain
My proposed contribution is:

[CONTRIBUTION]

Do not assume it is novel.

Break it into:
1. problem,
2. method/intervention,
3. context,
4. dataset,
5. metric,
6. claimed improvement.

For each component, explain what would need to be different for it to constitute a meaningful contribution.

Prompt 2 — Cari closest prior work

plain
Using only sources that can actually be inspected:

Identify the closest prior works to my proposed contribution.

For each work compare:
- problem,
- method,
- context,
- dataset,
- metric,
- contribution,
- limitation.

Then identify the smallest defensible difference between my proposal and each prior work.

Do not call my proposal novel yet.

Prompt 3 — Novelty gate

plain
Run a novelty gate.

Classify the proposal as:

PASS
- evidence shows a defensible contribution delta.

REVISE
- the idea may be useful, but the contribution or scope is not sufficiently differentiated.

FAIL
- closest prior work already covers substantially the same contribution.

For every classification:
1. cite the comparison evidence,
2. explain the reasoning,
3. state what would change the decision.

Do not equate "not found" with "never existed".

Red flag: AI tidak menemukan paper yang identik ≠ bukti bahwa penelitian tersebut belum pernah dilakukan.


5. Capability 3 — Claim Verification

Model kerja

mermaid
flowchart LR
    C["Claim"] --> S["Source"]
    S --> E["Exact Evidence"]
    E --> I["Interpretation"]
    I --> V{"Verification"}
    V -->|Supported| A["Authorized Claim"]
    V -->|Partial| P["Narrow Claim"]
    V -->|Contradicted| X["Remove / Resolve"]
    V -->|Unverified| U["Do Not Use Yet"]

Prompt utama

plain
Verify this claim:

"[CLAIM]"

For every relevant source:

1. identify the exact evidence,
2. explain what the evidence actually establishes,
3. identify conditions or limitations,
4. determine whether the source supports the claim,
5. flag any overstatement.

Classify the claim as:
- supported,
- partially supported,
- contradicted,
- unverified.

Do not strengthen the claim beyond the evidence.
If the source cannot be inspected, say so.

Contoh

Claim awal:

"Method X consistently improves performance." AI harus menguji apakah evidence benar-benar mendukung kata consistently, bukan hanya menemukan satu eksperimen yang hasilnya positif. Jika evidence hanya berlaku pada satu dataset: "Method X improved performance on dataset Y under the reported experimental setting." Itu lebih defensible daripada mengubah satu hasil menjadi general claim.

Claim Ledger

ClaimEvidenceStrengthLimitationAction
X improves YPaper A, Table 2SupportedDataset-specificNarrow wording
X always improves YTidak cukupUnverifiedClaim terlalu luasRemove
X is better than ZA/BPartialSetting berbedaQualify

Prompt untuk audit seluruh draft

plain
Audit the following draft for evidence-to-claim alignment.

For each major claim:
1. extract the claim,
2. identify the supporting source or result,
3. state whether the evidence is direct or indirect,
4. identify unsupported scope,
5. propose the narrowest defensible wording.

Do not rewrite the entire paper yet.
Return a claim ledger first.

6. Capability 4 — Experiment Planning

Academic Research Agent Skill membedakan technical smoke test, feasibility pilot, dan full run, serta menggunakan reality/feasibility gates sebelum pekerjaan eksperimen diperbesar. citeturn0search0turn0search4

Diagram

mermaid
flowchart TD
    H["Hypothesis"] --> S["Technical Smoke Test"]
    S -->|Fails| D["Debug / Stop"]
    S -->|Pass| P["Feasibility Pilot"]
    P -->|Not feasible| R["Revise Scope"]
    P -->|Feasible| G{"Evidence sufficient?"}
    G -->|No| P
    G -->|Yes| F["Full Run"]
    F --> V["Claim Verification"]

Step 1 — Formalize hypothesis

plain
My hypothesis is:

[HYPOTHESIS]

Help me define:
1. independent variable,
2. dependent variable,
3. baseline/control,
4. intervention,
5. evaluation metric,
6. confounders,
7. expected failure condition,
8. minimum evidence needed to test the hypothesis.

Do not invent results.

Step 2 — Rancang feasibility pilot

plain
Design the cheapest decisive feasibility pilot for this hypothesis.

Include:
- objective,
- unit of analysis,
- inputs,
- intervention,
- baseline,
- metric,
- success criterion,
- failure criterion,
- stop condition,
- data/provenance that must be recorded,
- assumptions that could invalidate the pilot.

Do not design the full experiment yet.

Step 3 — Audit eksperimen

plain
Act as a skeptical experimental-methods reviewer.

Audit this experiment plan for:

1. missing baseline,
2. confounders,
3. invalid metric,
4. data leakage,
5. insufficient sample or unit definition,
6. mismatch between hypothesis and measurement,
7. reproducibility risks,
8. hidden assumptions,
9. claims that the experiment cannot actually support.

Rank each issue:
- blocking,
- major,
- minor.

For every blocking or major issue, explain what evidence or design change would resolve it.

7. Capability 5 — Reviewer Simulation

Jangan menunggu paper selesai. Lakukan reviewer simulation saat masih mungkin mengubah scope, experiment, baseline, atau claim.

Prompt reviewer umum

plain
Act as a skeptical peer reviewer.

Review this research proposal/manuscript:

[PASTE MATERIAL]

Evaluate:
1. significance,
2. novelty,
3. methodological validity,
4. evidence quality,
5. experimental design,
6. claim strength,
7. reproducibility,
8. missing baselines,
9. likely reasons for rejection.

For every criticism:
- quote or identify the exact part being criticized,
- explain why it matters,
- classify severity as major or minor,
- state what evidence or change would resolve it.

Do not invent flaws that are not supported by the material.

Tiga reviewer lens

Reviewer A — Novelty

plain
Focus only on novelty and contribution.

Try to find the closest prior work or argument that could weaken the contribution.

Do not invent citations.
Separate:
- demonstrated overlap,
- plausible concern,
- unknown due to missing evidence.

Reviewer B — Methodology

plain
Focus only on:
- controls,
- baselines,
- metrics,
- confounders,
- experimental unit,
- reproducibility,
- data leakage,
- statistical or evaluation weaknesses.

Rank issues by scientific severity.

Reviewer C — Claims

plain
Audit whether each major claim is supported by the proposed evidence.

For each claim:
- evidence available,
- evidence missing,
- overclaim risk,
- minimum change needed.

Merge reviewer reports

plain
Merge these reviewer reports.

Identify:
1. objections appearing across multiple reviewers,
2. objections that are mainly stylistic,
3. critical issues that must be fixed,
4. issues that can be resolved with clarification,
5. issues requiring new evidence.

Create a prioritized revision plan.

Do not optimize for "sounding convincing".
Optimize for scientific defensibility.

8. Capability 6 — Reviewer Response / Rebuttal

Untuk tahap ini, paper-lifecycle menyediakan skill rebuttal-response yang memang ditujukan untuk triage review, menentukan evidence yang diperlukan, dan menyusun response per reviewer. citeturn0search2

Diagram

mermaid
flowchart LR
    C["Reviewer Comment"] --> T["Triage"]
    T --> E["What evidence do we have?"]
    E --> D{"Response type"}
    D --> A["Agree + Change"]
    D --> B["Clarify"]
    D --> C2["Add Evidence"]
    D --> D2["Disagree + Evidence"]
    D --> E2["Cannot Address"]
    A --> R["Draft Response"]
    B --> R
    C2 --> R
    D2 --> R
    E2 --> R
    R --> M["Manuscript Change"]
    M --> V["Final Verification"]

Step 1 — Triage

plain
For each reviewer comment:

1. classify the concern,
2. identify what the reviewer is actually asking,
3. determine whether we have evidence,
4. determine whether a manuscript change is required,
5. identify what we cannot honestly claim.

Do not draft the rebuttal yet.

Return a response matrix.

Response matrix

Reviewer commentConcernEvidenceResponse typeManuscript changeStatus
Comment 1Baseline unclearTable 2Clarify + ChangeMethodsOpen
Comment 2Novelty concernPrior-work comparisonDisagree + EvidenceRelated WorkOpen
Comment 3Missing experimentNoneCannot addressScope clarificationOpen

Step 2 — Pilih response type

Gunakan salah satu:

  • Agree + change
  • Clarify
  • Provide additional evidence
  • Disagree with evidence
  • Cannot address within current scope

Step 3 — Draft response

plain
Draft an evidence-based response to this reviewer comment:

[COMMENT]

Use this structure:
1. acknowledge the concern,
2. answer directly,
3. provide evidence,
4. describe the manuscript change,
5. identify the exact section/table/figure changed.

Rules:
- do not invent experiments,
- do not invent citations,
- do not invent results,
- do not claim a manuscript change that was not actually made,
- do not become defensive,
- disagreement must be supported by evidence.

Jangan gunakan AI untuk "memenangkan argumen"

Tujuan rebuttal adalah membuat reviewer/AC dapat melihat apa yang berubah, evidence apa yang tersedia, dan batas claim yang sebenarnya.


9. Capability 7 — Full Research Workflow

Sekarang semua capability digabungkan. Academic Research Agent Skill mendokumentasikan lifecycle yang jauh lebih rinci: idea → scope → source ingestion → literature grounding → mathematical formalization → novelty gate → reality gate → feasibility pilot → execution planning → experiment → reviewer simulation → claim verification → writing/revision. citeturn0search0

Diagram lifecycle

mermaid
flowchart TD
    I["1. Idea"] --> S["2. Scope"]
    S --> G1{"Human Gate"}
    G1 -->|Revise| S
    G1 -->|Approve| L["3. Source Ingestion"]
    L --> LG["4. Literature Grounding"]
    LG --> M["5. Formalization"]
    M --> N{"6. Novelty Gate"}
    N -->|Fail| S
    N -->|Revise| N2["Fix Contribution / Scope"]
    N2 --> N
    N -->|Pass| RG{"7. Reality / Feasibility Gate"}
    RG -->|Block| EB["Bounded Evidence Recovery"]
    EB --> RG
    RG -->|Pilot Only| P["8. Feasibility Pilot"]
    P --> RG
    RG -->|Execution Ready| X["9. Risk + Experiment Plan"]
    X --> R["10. Approved Run"]
    R --> RS["11. Reviewer Simulation"]
    RS --> CV["12. Claim Verification"]
    CV --> W["13. Writing / Revision"]
    W --> RR["14. Reviewer Response"]
    RR --> FV["15. Final Verification"]
    FV --> SUB["16. Submission"]

Master prompt

Gunakan prompt ini sebagai system-of-work untuk satu project riset.

plain
You are assisting me as a human-guided research assistant.

RESEARCHER OWNS:
- research question,
- scope,
- methodological decisions,
- interpretation,
- final claims,
- final submission decision.

YOU ASSIST WITH:
- source organization,
- literature comparison,
- novelty analysis,
- evidence tracing,
- experiment planning,
- reviewer simulation,
- claim verification,
- revision planning.

EVIDENCE RULES:
1. Never invent citations, DOI, datasets, data, results, or experiments.
2. Separate inspected evidence from inference and hypothesis.
3. Do not call a contribution novel without comparison to relevant prior work.
4. Do not strengthen a claim beyond its evidence.
5. If information is missing, say it is missing.
6. Prefer the cheapest decisive test before a large experiment.
7. Preserve traceability from claim to source or experiment artifact.
8. Stop and ask for researcher approval at major decision gates.

DO NOT:
- generate the full paper immediately,
- fabricate plausible references,
- turn one positive result into a universal claim,
- treat a polished artifact as scientific evidence.

MY RESEARCH IDEA:
[INSERT IDEA]

START:
1. research question,
2. scope,
3. non-goals,
4. candidate contribution,
5. evidence required,
6. closest prior work to investigate,
7. next decisive research action.

Wait for my approval before moving to the next major gate.

10. Contoh end-to-end

Misalkan ide awal:

"Saya ingin meneliti apakah metode X meningkatkan akurasi Y." Jangan langsung: "Buatkan paper 8 halaman." Gunakan:

Step 1 — Scope

plain
Turn this rough idea into a precise research question.

Identify:
- population/context,
- intervention,
- outcome,
- baseline,
- non-goals,
- assumptions.

Do not write the paper.

Step 2 — Literature

plain
Build a source analysis matrix for the closest prior work.

Columns:
problem | method | data/context | metric | result | limitation | relevance

Use only inspected sources.

Step 3 — Novelty

plain
Compare my proposed contribution against the five closest prior works.

Find the smallest defensible contribution delta.

If no meaningful delta remains, recommend narrowing or changing the research question.

Step 4 — Experiment

plain
Design the cheapest decisive feasibility pilot that could falsify my hypothesis.

Include:
baseline, intervention, metric, success criterion, failure criterion, stop condition, provenance.

Step 5 — Reviewer simulation

plain
Review the pilot and proposed contribution as three reviewers:
1. novelty,
2. methodology,
3. claims.

Rank blocking issues first.

Step 6 — Run and verify

Setelah eksperimen benar-benar dilakukan:

plain
Here are the actual experiment artifacts:

[ATTACH RESULTS]

Verify each major claim against the actual results.

Do not infer results that are not present.
Flag every claim that exceeds the evidence.

Step 7 — Writing

Baru masuk ke manuscript architecture dan drafting.

Step 8 — Rebuttal

Jika mendapat review:

plain
Here are the reviewer comments and the revised manuscript.

Triage every comment.
For each:
- concern,
- evidence,
- response type,
- required manuscript change,
- unresolved risk.

Do not draft persuasive language until the response matrix is approved.

11. Struktur folder project yang disarankan

Jangan membuat file hanya supaya project terlihat lengkap. Buat artifact ketika memang diperlukan untuk keputusan berikutnya. Contoh struktur sederhana:

plain
research-project/
├── 01_scope.md
├── 02_literature/
│   ├── source-analysis-matrix.md
│   └── notes/
├── 03_novelty/
│   └── novelty-gate.md
├── 04_experiment/
│   ├── pilot-protocol.md
│   └── results/
├── 05_claims/
│   └── claim-ledger.md
├── 06_review/
│   └── reviewer-simulation.md
├── 07_manuscript/
│   └── manuscript.md
└── 08_rebuttal/
    └── response-matrix.md

Catatan: ini contoh organisasi praktis, bukan kewajiban. Academic Research Agent Skill sendiri menekankan bahwa artifact dibuat sesuai evidence stage dan decision yang sedang dihadapi, bukan sebagai checklist file yang harus selalu lengkap. citeturn0search0


12. Checklist verifikasi sebelum mempercayai output AI

  • Source benar-benar ada.

  • Source yang dijadikan evidence benar-benar dapat diinspeksi.

  • Citation mendukung kalimat yang diklaim.

  • Tidak ada citation/DOI yang dibuat-buat.

  • "Tidak ditemukan" tidak diperlakukan sebagai "belum pernah ada".

  • Closest prior work sudah dibandingkan.

  • Novelty claim memiliki delta yang defensible.

  • Baseline/control masuk akal.

  • Metric benar-benar mengukur research question.

  • Tidak ada data leakage yang terlewat.

  • Hasil eksperimen berasal dari run nyata, bukan AI.

  • Major claims memiliki evidence yang dapat ditrace.

  • Reviewer objections sudah diprioritaskan.

  • Rebuttal tidak mengklaim perubahan yang belum dilakukan.

  • Researcher tetap mengambil keputusan akhir. <callout icon="⚠️" color="yellow_bg">

    Red flag: jika AI memberikan citation, angka, DOI, dataset, hasil eksperimen, atau kesimpulan yang tidak dapat kamu trace kembali ke source/evidence/artifact yang benar-benar ada, jangan masukkan ke penelitian sebelum diverifikasi. </callout>


13. Urutan belajar yang paling masuk akal

Jangan mencoba menjalankan seluruh lifecycle sekaligus.

Level 1 — Evidence

  1. Literature Review
  2. Claim Verification

Level 2 — Contribution

  1. Novelty Check
  2. Experiment Planning

Level 3 — Critique

  1. Reviewer Simulation
  2. Reviewer Response / Rebuttal

Level 4 — System

  1. Full Research Workflow

Latihan pertama

Ambil satu research question nyata. Lalu hasilkan hanya tiga artifact:

  1. Source Analysis Matrix
  2. Novelty Gate
  3. Claim Ledger Kalau tiga artifact ini belum solid, jangan buru-buru meminta AI menulis paper.

14. Ringkasan: apa yang sebenarnya berubah?

Tanpa workflow:

Idea → Prompt AI → Draft → Citation → Submit

Dengan workflow:

Question → Scope → Evidence → Prior Work → Novelty Gate → Feasibility → Experiment → Claim Verification → Reviewer Simulation → Revision → Rebuttal → Final Verification → Submission

Perbedaannya bukan sekadar prompt yang lebih panjang. Perbedaannya adalah AI diberi tempat di dalam proses pengambilan keputusan riset, bukan dijadikan pengganti proses berpikir.

Repository referensi

K
KayadigitalPenulis

Educator Claude AI · Kreator Indonesia

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