Submitting AI-Generated Writing “As Is” or with Only Light Edits Is RiskySubmitting AI-generated text “as is” is dangerous

Submitting AI-Generated Writing “As Is” or with Only Light Edits Is Risky

Academic paper/report written with ChatGPT raises plagiarism concerns: Saturday, August 23, 2025

Generative AI is highly convenient, but it does not automatically satisfy academic standards. In particular, references and other source materials are protected by copyright law, and AI use does not remove the need to handle quotations and sources appropriately.
iBooks Academic Support has begun offering specialist editing services to help bring AI-assisted drafts to a safer and academically appropriate standard.

■ Why Is Writing Left Entirely to AI Risky?
Uncertain Facts (Hallucinations)
AI may generate nonexistent references, statistics, or terminology in a plausible form. Errors such as fabricated DOIs, fictitious page ranges, or contradictory publication years can easily be introduced.

Breakdown of Logical Development
The chain from claim to evidence to interpretation may be weak, with problems such as confusion of causation and correlation, inconsistent operational definitions, or insufficient positioning relative to prior studies. These weaknesses can be challenged during evaluation.

Noncompliance with Academic Formatting
Formatting styles such as APA, MLA, or Chicago may be inconsistent; reference lists may not match in-text citations; figure/table captions or notes may be missing; and terminology may be inconsistent. Formatting deductions can directly affect grades.

Plagiarism & Ethical Risk
Source attribution may become unclear, and mishandling of secondary citations or insufficient paraphrasing can increase the risk of plagiarism findings. Violating a university’s AI-use rules may also lead to disciplinary action.

Lack of Verifiability and Reproducibility
Methods and data may not be specified precisely enough, leaving no reproducible record of data sources, procedures, or settings. This can create problems during oral examinations or resubmission.

Shallow Field-Specific Knowledge
AI output may not fit field-specific theoretical frameworks or conventions, such as ethical descriptions in nursing, selection of evaluation metrics in computer science, or assessment criteria in education, and may therefore fail to meet readers’ expectations.

Variation in Quality and Responsibility
Output quality varies substantially, and responsibility for errors rests with the submitter. Repairing major problems immediately before the deadline can be difficult.

Loss of Learning Opportunities
If the writer does not acquire skills in literature search, logical organization, and methodology during the writing process, they may be unable to explain the content during a presentation or oral examination.

■ iBooks’ Solution: A Two-Stage Approach Combining AI and Human Specialists
Specialist Editing Team：
　- Structural correction (aligning problem statement → prior studies → methods → results → discussion)
　- Strengthening logical development (clarifying the relationship among claim, evidence, and interpretation)
　- Reference consistency checks (existence verification, replacing with up-to-date sources, and consistent formatting in APA / MLA / Chicago, etc.)
　- Plagiarism-risk reduction support (clear quotation and note handling / optional pre-check of similarity)
Note: This service specializes in editing, proofreading, and methodological advice. Clients may bring AI-assisted drafts, and we provide structural correction, logical strengthening, source-consistency checks, and formatting standardization. The service focuses on final academic-quality refinement.