🧭 Academic Integrity & Ethics
Prof. Z. Yao. The "how to think" course — ethical frameworks you'll apply to biotech dilemmas.
T01 Overview of Academic Integrity & Ethics
- Integrity = honesty + strong moral principles you refuse to change
- Ethics = system of accepted beliefs controlling behavior
Three ethical theories
| Theory | Key figure | Core |
|---|---|---|
| Utilitarianism (consequentialism) | Bentham, Mill | Greatest good for greatest number (judge by outcome) |
| Deontology (Kantianism) | Kant | Duty/rules; treat people as ends, never mere means |
| Virtue ethics | Aristotle | What a virtuous person would do (character) |
Four principles of bioethics (Beauchamp & Childress)
- Respect for autonomy 自主 — respect self-rule / decisions
- Beneficence 行善 — do good
- Nonmaleficence 不傷害 — first, do no harm
- Justice 正義 — fair distribution of benefits/risks
Biotech ethics cases
- Trolley problem — utilitarianism (save 5) vs deontology (don't kill 1)
- CRISPR/gene editing — edit embryos? designer babies? somatic vs germline
- End-of-life — DNAR, withholding treatment, euthanasia (active vs passive), double effect
- COVID — ICU triage, compulsory quarantine, vaccine equity
What is "Academic Integrity and Ethics"?
Per the lecture's GPT-4o definition, academic integrity and ethics is the moral code and ethical policy of academia, built on the values honesty, trust, fairness, respect, and responsibility in scholarly work. Maintaining it is crucial for the credibility and reliability of scholarly work and fosters a culture of trust and respect.
Case studies on moral values
Trolley Problem (I): a trolley will kill five tied to a track; you can pull a lever to redirect it onto one person. Do nothing (5 die) or pull (1 dies, 5 live)?
Trolley Problem (II): same five, but you stand on a bridge; you can push a large man down to stop the trolley (he dies, 5 live). Tests the same utilitarian math against a direct personal act — deontology resists killing the one directly.
Clinical scenario (Mrs. X): a burn patient in A&E, two children dead, one of unknown status, herself critical. If she asks how her children are, do you tell the truth or withhold? This is the truth-telling dilemma — "don't tell her."
A brief history of ethical philosophy
- Socrates (469–399 BC): virtue is knowledge; virtue is both necessary and sufficient for happiness (eudaimonia).
- Plato (427–347 BC): four primary virtues — wisdom, courage, justice, moderation — for a truly happy life.
- Aristotle (384–322 BC): happiness achieved when a person is morally virtuous, acting on reason and truth.
The four principles — detail
- Respect for autonomy (自主原則): Latin "self-rule"; obligation to respect others' decisions about their own lives. Also called the principle of human dignity.
- Beneficence (行善原則): the principle of "doing good"; obligation to bring about good in all actions.
- Nonmaleficence (不傷害原則): obligation not to harm — "first, do no harm"; don't increase risk; where harm can't be avoided, minimize it. Combined with beneficence: each action must produce more good than harm.
- Justice (正義原則): respect for human rights and social justice; fair distribution of benefits and risk; in public life, treat all equally, fairly, impartially.
CRISPR / gene editing — the specific questions
CRISPR = Clustered Regularly Interspaced Short Palindromic Repeats. The lecture poses the live questions:
- Edit human genomes to eliminate genetic diseases via CRISPR / Cas9?
- Edit human embryos? Prevent severe disease (e.g. AIDS induced by HIV)?
- Edit our children's genomes? Design the baby before birth?
- Legalize gene therapy for inherited diseases among embryos in HK?
End-of-life care — the full checklist
- Withholding / withdrawing of medical interventions
- Breaking bad news / truth-telling
- DNAR (do not attempt resuscitation)
- MOLST (medical order for life-sustaining treatment)
- Symptomatic treatment that may shorten life
- Hastening death — principle of double effect
- Terminal / palliative sedation
- Physician-assisted dying?
Euthanasia = intentionally ending a life to relieve pain and suffering. Active = lethal intervention; "passive" = withdrawal of care (e.g. artificial nutrition and hydration).
COVID-19 & academic ethical issues
COVID-19 ethics: prioritize some patients for ventilation / ICU beds? compulsory quarantine? prevent discrimination against specific groups? decisions on vaccination?
Academic-specific issues the lecture flags:
- Use of clinical samples, animals or humans for experiments
- Use of AI (e.g. generative AI)
- Plagiarism, citation and copyright — including the HKU case: AI-generated non-existent references
Week 3 · Moral and Ethical Issues in Science and Technology (Prof. Xunjin Zhu)
Delivered 14/9/2026. This session moves from abstract ethical theory to documented misconduct cases — the material most likely to appear in the video-presentation assessment.
Why "ownership & inventorship" matters to you
The lecture frames this session as "the rules of the game": knowing them protects you, ensures you get credit, and keeps you out of serious trouble. Two learning goals sit at the centre — distinguishing inventorship from ownership, and identifying the ethical responsibilities of researchers, supervisors, and institutions.
Intellectual Property (IP) splits into two asset classes:
- Physical assets — a physical object (land, car) with limited availability (only a limited number of people can use it at a time).
- Intangible assets (IP) — monetary value but no physical substance (software, brand recognition / goodwill); intellectual assets are creations of the mind (idea, song, writing). They have unlimited availability — one person can use them without preventing others from using them.
IP rights in Hong Kong: types & ordinances
Hong Kong offers several types of IP protection, each tailored to a different asset:
| Protection | What it covers |
|---|---|
| Patents | Innovations; gives the innovator exclusive rights for a set period. |
| Trademarks | A brand's unique identifiers — logos and names. |
| Copyright | Creative/artistic works: music, literature, software, and more. |
| Registered designs | The visual appearance of products. |
Hong Kong's IP laws are governed by key ordinances: the Patents Ordinance, the Copyright Ordinance, and the Trade Marks Ordinance. These define what can be protected, the length of protection, enforcement mechanisms, penalties for infringement, and dispute-resolution procedure.
Patents, inventorship & ownership
A patent protects inventions that are products, substances, or processes which are new and inventive, giving the owner the right to exclude others from using the invention in a jurisdiction for a limited period.
Hong Kong law does not clearly state who owns IP an employee creates during employment; the general principle is that an employer owns the IP created by its employees in the course of their employment.
The inventor is the "actual deviser" of the invention. A person qualifies as an inventor when they:
- conceived the core idea;
- used their initiative to go beyond a brief, leading to an inventive concept;
- identified the significance of a result that led to the invention.
Activities that would not, on their own, make someone an inventor include: carrying out routine work; offering advice; supervising a project (but not devising the inventive concept); testing the invention; and carrying out follow-up work.
Universities and institutes normally own IP made, designed, discovered, or created by staff/students in the course of employment or that makes significant use of institutional resources — institution-administered funds, or institution-funded time, facilities, or equipment.
Personal contribution to an invention can be recognised by: (1) designation as a legal inventor on the patent; (2) sharing in the final return (if any) from commercialisation; (3) recognition through publications and presentations.
Case: Stern v. The Trustees of Columbia University
Background. László Z. Bitó published papers on prostaglandins' effect on intraocular pressure (IOP) in animals (rabbits, owl monkeys) and believed rhesus monkeys would be good models. Fredric A. Stern, a Columbia medical student (1980), ran an ophthalmology elective in Bito's lab and experimented on rhesus monkeys with topical prostaglandins to reduce IOP — confirming a previously known effect but not proving whether tachyphylaxis (acute development of tolerance to a drug after repeated doses, giving a diminished clinical response) would develop in primates.
Bito continued the work, leading to Columbia's US Patent No. 4,599,353 (Filed 1982; Issued 1986) with Bito as sole inventor. After learning of the patent, Stern sought co-inventorship in the District Court and later the Federal Circuit.
Core inventive concept (per the court): using prostaglandins to treat glaucoma in primates by maintaining reduced IOP without tachyphylaxis. The patent's Claim 1 is "a method for treating hypertension or glaucoma in a primate subject's eye comprising periodically contacting the surface of the eye with an amount of an eicosanoid or an eicosanoid derivative effective to reduce intraocular pressure… and to maintain reduced intraocular pressure" — interpreted as maintenance throughout treatment without tachyphylaxis.
Federal Circuit ruling. Stern did not understand the claimed invention, did not discover that prostaglandins affect IOP, did not discover that repetitive application maintains reduced IOP, and did not conceive the idea of using prostaglandins to reduce IOP in primates. There was no collaboration with Bito; Stern simply repeated an experiment Bito had already done on animals Bito had chosen as models. His contribution is insufficient for co-inventorship.
Case: Chou v. University of Chicago
Background. Dr. Joany Chou and her supervisor Bernard Roizman worked on a herpes virus vaccine. Roizman filed a patent application with himself as sole inventor and received substantial royalty licensing; he told Chou her discovery was not patentable and, in 1996, indicated she would be fired if she did not resign. A University policy stated every patentable invention "shall be the property of the University" and assigned to it.
Chou sued Roizman, the University of Chicago, ARCH Development Corporation (the University's wholly-owned subsidiary), and Aviron (the exclusive licensee), alleging she was the rightful inventor and should be added as co-inventor, and charging:
- fraudulent concealment (deliberate hiding);
- breach of fiduciary duty (duty to look after Chou);
- unjust enrichment (getting rich at her expense);
- breach of contract (policy to compensate the inventor).
District Court: Chou had no ownership interest and lacked standing to challenge inventorship; defendants owed no duty to inform her about patentability.
Federal Circuit: Chou was allowed to challenge inventorship. Under 35 U.S.C. §256, "if a rightful inventor was not listed as an inventor on the patent, that inventor can sue in federal court to have his or her name added." The court found Roizman had failed in his duty to advise Chou of applications on which she may have been an inventor; Roizman and the University breached fiduciary duty under an agency theory; Roizman had unjustly retained the benefit of Chou's research; and the University, violating its express policies, had failed to compensate her.
Supervisor, institutional & personal responsibilities
From both cases, the lecture extracts duties:
- Clarity of inventorship & authorship rights — clearly define researchers' roles and contributions; institutions need well-defined ownership/attribution policies.
- Responsibilities of supervisors & mentors — an ethical duty to guide mentees on authorship, inventorship, and recognition; a duty of care to acknowledge contributions and act in mentees' best interests (career advancement, recognition).
- Honesty & transparency — a duty of disclosure; the system relies on honesty and transparency.
Proactive steps a young researcher can take: read institutional policies; document contributions; have clear conversations with supervisors; and understand the difference between authorship and inventorship.
Research misconduct I: Obokata & STAP cells (RIKEN, 2014)
Haruko Obokata (RIKEN) published two Nature articles in January 2014 claiming that, triggered by a simple stimulus, mature somatic cells could be reprogrammed into pluripotent STAP cells ("Stimulus-Triggered Acquisition of Pluripotent" cells) — an easier method than conventional protocols.
Many experts failed to reproduce the experiment; serious doubts arose. On 1 April 2014 RIKEN concluded it was research misconduct: multiple falsifications — manipulating image data, reusing data from her own doctoral thesis for a different project, and copying from others' publications without permission. Obokata agreed to retract on 4 June 2014; in July she failed to reproduce her own findings under third-party monitoring.
Human costs & aftermath: on 5 August 2014 her mentor and co-author Yoshiki Sasai committed suicide (criticised for inadequate supervision, though not accused of misconduct); Obokata resigned from RIKEN on 21 December 2014; RIKEN's second investigation found all STAP cells resulted from contamination by 3 different types of ES cells, unlikely to be accidental; in 2015 her doctorate was revoked. RIKEN then revised internal rules to require all staff to receive research-ethics education and to stipulate data-recording procedures.
Research misconduct II: Han / NgAgo non-replication
Chunyu Han (Hebei University of Science and Technology) reported NgAgo — a single-stranded DNA (ssDNA)-guided Argonaute endonuclease, an acronym for Natronobacterium gregoryi Argonaute. NgAgo binds 5′-phosphorylated ssDNA of ~24 nucleotides to guide it to the target and makes double-strand breaks, like the CRISPR/Cas system but without requiring a protospacer adjacent motif (PAM). It was reported suitable for genome editing, but this has not been replicated; some reported inability to replicate and others claimed success, leaving ongoing debate with no definitive conclusion.
Research misconduct III: the "Geng" exposé in China (2026)
In April 2026, 耿同学 (Geng) began posting videos exposing alleged academic misconduct in papers by prominent Chinese-university professors. The investigations targeted recipients of the National Science Fund for Distinguished Young Scholars; alleged misconduct included fabrication and falsification of research data, with some datasets showing unnatural patterns — identical values differing by exactly 0.3 or ending uniformly in 5.
Institutional & official responses: Tongji University confirmed misconduct within weeks, removing Prof. Wang from his deanship and demoting him two professional grades; the first author was dismissed. The National Natural Science Foundation of China (NSFC) issued a formal notice with penalties including: revocation of funded projects and recovery of allocated funds; bans on applying for NSFC grants ranging from 3 to 7 years; and official criticism recorded in the research-integrity database.
Significance for academic-integrity education: data integrity; accountability at all levels (both senior authors/deans and junior researchers); the role of external oversight; and that academic integrity is a "social responsibility" for a healthy research ecosystem. Misconduct "wastes resources, misdirects entire fields, and, in medical research, can endanger lives."
Case: Autism vs. Vaccines (Wakefield, Lancet 1998)
In 1998, The Lancet published Andrew Wakefield's paper claiming autism symptoms appeared soon after the Measles-Mumps-Rubella (MMR) vaccine. Methodological problems: small sample size (12 children), no control group, and vague conclusions.
Subsequent, more rigorous studies found no connection:
| Year | Study | Sample | Link to autism? |
|---|---|---|---|
| 1999 | — | 500 children | None |
| 2001 | — | 10,000 children | None |
| 2002 | Denmark | 537,000 children | None |
| 2002 | Finland | 535,000 children | None |
| 2005 | Review of 31 studies | >10,000,000 children | None |
| 2012 | 27 cohort + 17 case-control + 6 self-controlled case series + 5 time-series + 2 ecological + 1 case cross-over | >14,700,000 children | None (any case) |
The Lancet initially exonerated Wakefield et al. of ethical violations/misconduct, but after the UK General Medical Council's 2010 judgment it completely retracted the paper. Still, 1/4 of U.S. parents believe some vaccines cause autism, and there have been 0 credible studies linking them.
Real-world harm: France reported a massive measles outbreak with nearly 15,000 cases in 2011; the U.K. reported more than 2,000 measles cases in 2012; vaccine refusals were largely to blame for a whooping-cough outbreak in California. Wakefield later directed the anti-vaccination film Vaxxed: From Cover-Up to Catastrophe (2016) and received The Skeptic's "Rusty Razor" award (2018) for pseudoscience. In 2019, Trump said "they have to get the shots," and Hviid et al. (Ann Intern Med 2019;170(8):513–520) studied 657,461 Denmark children — again no link.
Case: the CRISPR-baby experiment (He Jiankui)
He Jiankui, associate professor at Southern University of Science & Technology, announced on 25 November 2018 the birth of two gene-edited babies, Lulu and Nana, whose father is HIV-positive. He used CRISPR/Cas9 to delete the CCR5 gene (which produces an immune-cell surface protein HIV uses to establish infection), claiming this gives natural HIV immunity. CRISPR = Clustered Regularly Interspaced Short Palindromic Repeats.
Conducted in secrecy, bypassing norms: successful birth (mid-Oct 2018), a YouTube interview (19 Nov), AP announcement (25 Nov), and the International Human Genome Editing Summit (29 Nov) — with no peer review, no independent confirmation, and no journal publication. He claimed ethical approval from the Medical Ethics Committee of Shenzhen Harmonicare Hospital, but the hospital said the committee never met to discuss the project.
Not medically necessary: neither infant was proved HIV-positive; established drugs (e.g. maraviroc) already block CCR5; the rationale did not address an unmet need; different HIV strains enter via proteins other than CCR5, so deletion cannot confer complete immunity; and there is no way to determine any benefit.
Safety issues: no animal test before humans; people with natural CCR5 deficiency are more vulnerable to West Nile virus and more likely to die of influenza; off-target effects may unexpectedly edit other genes; long-term effects are unknown.
Poorly executed: in Lulu only half the CCR5 genes were edited (heterozygous, or mosaic — she will likely not resist HIV by He's theory); Nana also has normal copies (unlikely to confer resistance); He planned to mimic the natural delta32 mutation (found in ~10% of Europeans) but created a different, non-target edit of CCR5; off-target effects were claimed "harmless" without strong supporting evidence.
No proper informed consent: the form falsely described the experiment as an "Aids-Vaccine Development Project," used jargon participants (some of whom had never heard "gene editing") would not understand, omitted off-target risks and the fact that gene editing was prohibited/controversial, and resembled a business subcontracting form to absolve the team of liability.
Consequences: China declared He "seriously violated" state regulations and likely faced criminal charges; he forged ethics documents; and he faced possible corruption/bribery charges over how he assembled the team (IVF clinic, other scientists, obstetricians).
Gene-edited monkeys (BMAL1) & animal-research ethics
On 24 January 2019, The National Science Review published a study from the Chinese Academy of Sciences' Institute of Neuroscience: researchers cloned five monkey embryos edited to remove the BMAL1 gene, causing anxiety, depression, and schizophrenia-like symptoms, using CRISPR/Cas9; findings could help treat sleep disorders, diabetes, cancer, and neurodegenerative diseases.
Unlike He's experiment, this one was authorised and funded by the Chinese Academy of Sciences and the Shanghai city government. China is the only country with the technology to clone captive primates (still widely used globally); America is the world's largest user of lab chimpanzees. Bioethics experts stressed that ethical issues around animal use are similar to those for humans and must be closely regulated — experiments like these carry an "ethical price."
Global regulation & law on human germline editing
Push for standards. The WHO is assembling a panel to develop global standards. Leaders of the U.S. National Academy of Medicine and National Academy of Sciences, with the Chinese Academy of Sciences, proposed a commission with other academies to set criteria so scientists cannot "seek out convenient locales for conducting dangerous and unethical experimentation," including an international mechanism for raising concerns.
Chinese law & regulations:
- 2003 — Ethical Guiding Principles for Human Embryonic Stem Cell Research: blastocysts used for research cannot be implanted into the reproductive system of humans or other animals.
- 2012 — Regulations on the Management of Human Genetic Resources: major principles include consent to collect, preserve, and utilise human genetic resources.
- 2017 — Biotechnology Research and Development Safety Management Measures: lists R&D involving genetic engineering such as human genetic editing with significant risks as a high-risk level, requiring strict management.
U.S. law & regulations: no federal laws regulate human reproductive cloning or inheritable genetic modification, but the FDA asserts authority over human reproductive cloning, "three-person IVF," and inheritable genetic modification, and vets gene-therapy experiments/products. A 2017 National Academies report says edited embryos should be used in human pregnancies only to prevent or treat "serious diseases or disabilities" with no "reasonable alternative" treatment.
Summary: a scientist's duties
The lecture closes with five duties for scientists:
- Clarify roles — know inventorship vs ownership; get everything in writing.
- Uphold integrity — your reputation for honesty is everything; never falsify data.
- Consider consequences — your work affects public health and society; think beyond the lab.
- Embrace oversight — ethical review and peer scrutiny are protections, not obstacles.
- Combat misinformation — control and suppress scientific misinformation; prevent erroneous findings from misleading others; protect the public's health and welfare from false results.
T01 Course Map — schedule, format & assessment
T01 is not lecture-based — it is hybrid/integrated: lectures and seminars, case studies, presentations, and discussion. Attendance at every class matters; no pre-class preparation is required, but reading, thinking and discussion after each class is. The organising lecturer is Prof. Zhongping Yao (zhongping.yao@polyu.edu.hk), Department of Life Sciences.
| Date | Theme / Topic | Lecturer | Platform |
|---|---|---|---|
| 7/9/2026 Mon, Week 2 18:30–21:20 | Overview of Academic Integrity and Ethics | Prof. Zhongping Yao | MS Teams |
| 14/9/2026 Mon, Week 3 18:30–21:20 | Moral and Ethical Issues in Science and Technology | Prof. Xunjin Zhu | MS Teams |
| 21/9/2026 Mon, Week 4 18:30–21:20 | Ethical Use of AI in Academic Research | Prof. Quanhua Mu | MS Teams |
| 28/9/2026 Mon, Week 5 18:30–19:30 | Ethical Use of Information in Thesis Writing & Journal Publication — understanding plagiarism, proper citation and copyright | Mr. Kin Fu Michael Yip | MS Teams |
| 28/9/2026 Mon, Week 5 19:30–21:20 | Self-study — watch the Canvas video and respond to the test questions | — | Canvas |
| 5/10/2026 Mon, Week 6 18:30–21:20 | Presentations and Discussion | Prof. Xunjin Zhu Prof. Quanhua Mu | MS Teams |
(1) Background · (2) Integrity and ethics issues and outcomes · (3) Your decision and justification
Submit on Canvas by Wednesday of Week 5 — 23:59, 30 September 2026. Selected videos are shown and discussed in Week 6.
Assisted Reproductive Technology (ART) — an ethics example
The overview lecture's "Ethics examples" section flags Assisted Reproductive Technology (ART) and the world's first test-tube baby as a concrete ethics topic alongside CRISPR and embryo editing. The English card covers CRISPR and embryos but omits this anchor example that frames the broader reproduction-and-ethics debate.
Kant's formulation of the moral law
The overview's deontology slide quotes Kant directly (not carried into the English card):
Root causes of research misconduct
The Week 3 discussion asks why researchers fabricate or falsify. The lecture answers with systemic drivers the English card never states:
- Hypercompetitive pressure — funding, promotion, and tenure depend heavily on publication metrics (and, per the Obokata discussion, "intense pressure to publish in high-impact journals").
- Reactive oversight — institutional oversight is "often reactive rather than proactive," so problems surface only after publication.
Strengthening institutional self-regulation
The same discussion asks how institutions can detect misconduct earlier. The source's answer:
How funding-agency sanctions deter misconduct
On how penalties actually deter, the source gives the funding-lever rationale the English card's NSFC list implies but does not state:
- Loss of funding eligibility — a career-ending sanction for researchers whose work depends on grants.
- Return of misappropriated funds — investigators must repay allocated money, raising the personal cost of fabrication.
Practice MCQs — Stern & Chou (from the source slides)
These literal multiple-choice items appear in the Week 3 slides but were only summarised in the English card's exam-focus callouts. Reproduced for drill:
| Question (verbatim stem) | Options | Correct |
|---|---|---|
| Stern v. Columbia — "Which of the following statements regarding Stern v. The Trustees of Columbia University is correct?" A. Stern contributed to the concept of treating hypertension or glaucoma in a primate subject's eye. B. Bito had a collaborative relationship with Stern during this research project. C. Stern's record should be strong evidence of his contribution to co-inventorship. D. Stern's contribution is sufficient to support a claim of co-inventorship. | [ ] A, B, C, D [ ] A, B, C [ ] A and B only [ ] None of the above | None of the above — all four statements are false per the Federal Circuit. |
| Chou v. University of Chicago — "Based on the case of Chou v. University of Chicago, which of the following statements are correct?" A. Roizman owed no duty to inform Chou about patentability of her research. B. Chou qualifies to challenge under 35 U.S.C. §256 due to her significant contribution. C. Chou should be awarded full ownership of the patent. D. Roizman had unjustly retained the benefit of Chou's research. | [ ] A, B, C, D [ ] A, B, C [ ] A and C only [ ] B and D only | B and D only — B (§256 challenge) and D (unjust enrichment) are true; A and C are false. |
STAP aftermath (2016–2018)
The English card stops at Obokata's 2015 doctorate revocation. The source continues the timeline:
- 2016 — Obokata published the book 'Ano hi' (あの日, "That Day"); in it she still maintains the STAP experiment was real and directs suspicions at Teruhiko Wakayama, a former RIKEN researcher.
- 2018 — her last public note was a magazine photo spread, underscoring how far the fallout extended beyond retraction.
Wakefield — the MMR uptake drop
Missing from the English card's vaccine section: the direct behavioural consequence the source records — MMR vaccination rates began to drop after the 1998 paper, feeding the later outbreaks (France ~15,000 measles cases 2011; UK >2,000 measles 2012; California whooping-cough outbreak).
T01 · W4 Ethical Use of AI in Academic Research
Week 4 · Ethical Use of AI in Academic Research (Prof. Quanhua Mu)
The fourth session of ABCT5T01, taught by Prof. Quanhua Mu. The lecture has four parts: (1) what AI and generative AI are, (2) the risks and the ethical principles that answer them, (3) PolyU's own guidelines, and (4) the rules for AI in academic publication. Everything below is from the Week 4 slides.
What is generative AI?
| Term | Definition / example |
|---|---|
| Generative AI | "A subfield of artificial intelligence that focuses on developing systems capable of autonomously generating new and creative content." (Ramdurai & Adhithya, 2023, International Journal of Computer Science and Engineering, 10(6), 1–8) |
| Named tools on the slides | ChatGPT, DeepSeek |
The framing slide is blunt about the context: Google search-trend data for the word "AI" over the past ten years, titled "We are in the AI age."
Prompt craft — "the fifth literacy"
Using GenAI well is treated as a literacy in its own right — the fifth literacy, alongside oral, written, visual and digital literacy (a framing the lecture credits to an HKU press release). The slide lists six concrete tactics:
- Include details in your query to get more relevant answers
- Ask the model to adopt a persona
- Use delimiters to clearly indicate distinct parts of the input
- Specify the steps required to complete a task
- Provide examples
- Specify the desired length of the output
Are you allowed to use it? Yes — PolyU's stance
The slide answers the obvious question directly: Yes. PolyU takes an open and forward-looking stance on GenAI tools. The lecture states that generative AI usage has become a normal part of learning, teaching and assessment since 2023/24 Semester One. A dedicated resource hub exists at genai.polyu.edu.hk/explore.
The university's student-facing guide (Academic Registry, revised 21 May 2026) reduces to three verbs:
| Use GenAI … | Means |
|---|---|
| critically | Check it. Verify claims, references and logic yourself. |
| ethically | Disclose it. Don't fabricate. Don't upload what you shouldn't. |
| wisely | Know when it helps your learning and when it replaces it. |
Risk 1 — Hallucination
Hallucination = the model produces confident output that is simply wrong. The lecture gives three causes:
- Insufficient training data
- Incorrect assumptions made by the model
- Biases in the data used to train the model
And one uncomfortable conclusion: it is very difficult to get rid of AI hallucinations. This is not a bug awaiting a patch — it is a property of how these systems work.
The practical consequence the lecture highlights: the references in GenAI-generated text often contain errors or simply do not exist. The illustrative prompt used in class was: "What is osteosarcoma, and what are the most recent treatment options for this disease? Please write a paragraph and add the citations."
Risk 2 — Copyright
Three distinct problems, not one:
| Problem | Why it matters |
|---|---|
| Training on copyrighted content | The action of using copyrighted content to train GenAI models may constitute copyright infringement. Case referred to on the slide: The New York Times Company v. Microsoft Corporation (with OpenAI). |
| Output can reproduce others' content | GenAI-generated text may contain the same content as web pages and websites owned by others — you can be the one who publishes it. |
| Copyright in AI-generated media | Whose copyright is it? The slide uses Thaler v. Perlmutter and the AI-generated image titled "A Recent Entry to Paradise" as the worked example. |
Risk 3 — Privacy
The lecture's concern is simple and concrete: what you type in may be retained and reused. The slides point to OpenAI's own help documentation on data usage for consumer services and on how your data is used to improve model performance.
The proposed solution on the slide is to run GenAI locally:
- Download and install ollama
- Pull down and run open-source GenAI models
- Enjoy
The worked example given in class was running DeepSeek R1 locally. The point generalises: if the model never talks to a server you don't control, the data-upload problem disappears.
Risk 4 — Data fabrication (the most serious)
This is the one the lecture spends the most slides on, and the one that ends careers.
Data fabrication used to require effort in a lab. GenAI removed that constraint. The lecture's central example is a paper in Nature Nanotechnology (s41565-025-02009-9) presenting purely AI-generated images from text prompts through ChatGPT, labelled as real experimental micrographs:
| Figure | What it was presented as |
|---|---|
| (a) Nano-puffs | Four fabricated nanomaterial images. The authors generated each from four initial sentences describing the nanomaterial (contrast, shape, dispersity), the background material (e.g. a TEM grid), and any other material (e.g. dirt). Subsequent minor interactions with ChatGPT produced the final images. |
| (b) Nano-Cheetos | |
| (c) Nano-Dragon's teeth | |
| (d) Nano D12s |
Note the method: the fabricated figures were not crude. They were tuned — sentence by sentence, image by image — until they looked like real microscopy.
What can we do? The slide's three countermeasures:
- Access the raw data
- AI detection methods
- Repeat the experiments
A second, closer-to-home case appears on the slides: a student fabricated interview data using ChatGPT — i.e. qualitative research data that no one could have detected from the numbers alone.
Risk 5 — Interfering with learning and research
The lecture assembles a cluster of concerns under this heading, drawing on Nature news coverage (d41586-026-02447-y and d41586-026-02632-z) and the mathandai.org multilingual project page. The core worry is that GenAI changes the process rather than only the output — the struggle that is the learning gets outsourced, and research habits form around whatever is easiest to generate.
Risk 6 — Digital inequality
Digital inequality is listed as a risk in its own right: access to the best tools, and the skill to use them, is unevenly distributed. A technology framed as an equaliser may widen the gap between those who already have resources and those who do not.
The lecture's own balance sheet — benefits vs risks
| Benefits (5) | Risks (6) |
|---|---|
| Personalised and immediate response | Accuracy and transparency |
| Extremely wide knowledge | Copyright issues |
| High language skills | Privacy issues |
| Brainstorming support | Lack of real understanding |
| Ability to organise information in a logical way for writing and presentation | Deepfake concerns |
| — | Digital inequality |
Read the columns against each other and the shape of the lecture appears: the benefits are all about speed and form; the risks are all about truth and ownership. That is why the answer is "use it" and not "don't".
The four ethical principles (European Commission framework)
Four principles govern ethical use of GenAI in academic research. This framing comes from the European Commission's Living Guidelines on the Responsible Use of Generative AI in Research, which is listed among the lecture's key references.
| Principle | What it demands |
|---|---|
| Reliability | Ensure the quality of research, reflected in design, methodology, analysis and use of resources. Verify and reproduce information produced by the AI. Be aware of equality and non-discrimination issues relating to bias and inaccuracies. |
| Honesty | Be honest in developing, carrying out, reviewing, reporting and communicating research — transparently, fairly, thoroughly and impartially. Disclose that generative AI has been used. |
| Respect | Respect colleagues, research participants, research subjects, society, ecosystems, cultural heritage and the environment. Responsible use must account for the limitations of the technology, its environmental impact, and societal effects (bias, diversity, non-discrimination, fairness, prevention of harm). Manage information properly; respect privacy, confidentiality, intellectual property and proper citation. |
| Accountability | Be accountable for research from idea to publication; for its management and organisation; for training, supervision and mentoring; and for its wider societal impacts. Be responsible for all output a researcher produces, underpinned by human agency and oversight. |
Six recommendations for researchers
The same guidelines then spell out what researchers should actually do. These six are examinable as a list.
- Remain ultimately responsible for scientific output. Researchers are accountable for the integrity of content generated by or with AI tools. Maintain a critical approach and be aware of the tools' limitations — bias, hallucinations, inaccuracies. Do not use fabricated material created by generative AI in the scientific process, e.g. falsifying, altering or manipulating original research data.
- Use generative AI transparently. Detail which tools have been used substantially, including name, version and date, how it was used, and how it affected the research process. Where relevant, make the input (prompts) and output available, in line with open-science principles. Account for the stochastic (random) nature of these tools — the same input can produce different output — and aim for reproducibility and robustness. Disclose or discuss limitations, including possible biases and mitigation measures.
- Pay particular attention to privacy, confidentiality and IP when sharing sensitive or protected information. Generated or uploaded input (text, data, prompts, images) could be used for other purposes, such as training AI models. Protect unpublished or sensitive work — yours or others' — by not uploading it unless there are assurances the data will not be reused. Do not provide third parties' personal data unless the data subject has consented and there is a clear purpose, so that data-protection rules are met. Understand the technical and ethical implications: check the tool's privacy options, who manages the tool (public or private institutions, companies), and where the tool runs — from closed environments and third-party infrastructure with guaranteed privacy, all the way to open internet-accessible platforms.
- Respect applicable local and international legislation, as in normal research activity. Watch for the potential for plagiarism (text, code, images) when using AI outputs. Respect others' authorship and cite appropriately — the output of a large language model may be based on someone else's results and require proper recognition and citation. AI output can also contain personal data; if this becomes apparent, you are responsible for handling it properly and following data-protection rules.
- Continuously learn how to use these tools properly, including through training. The tools evolve quickly and new uses are regularly discovered — stay up to date on best practices and share them with colleagues and stakeholders.
- Refrain from using generative AI substantially in sensitive activities that could impact other researchers or organisations — for example peer review or the evaluation of research proposals. Avoiding the tools eliminates the risk of unfair treatment or assessment arising from their limitations (hallucinations, bias), and safeguards fellow researchers' original unpublished work from exposure or inclusion in an AI model.
Authorship — can GenAI be an author?
The slide poses the question and then answers it flatly: GenAI tools should not be listed as an author in your paper. This is the position of the bodies named in the lecture's references:
| Body | Position |
|---|---|
| COPE (Committee on Publication Ethics) | Authorship and AI tools — position statement |
| ICMJE | Recommendations for the Conduct, Reporting, Editing and Publication of Scholarly Work in Medical Journals |
| Science editorial | "ChatGPT is fun, but not an author" — and the later policy change on generative AI and large language models |
The reasoning is the same everywhere: authorship requires accountability, and a tool cannot be held accountable. Responsibility stays with the human authors.
Using GenAI in writing
The permitted boundary is narrow and precise: GenAI can only be used to improve the readability and language of the work — not the key authoring tasks such as producing scientific insights and drawing scientific conclusions.
AI-giarism. The general rule stated on the slide is that the authors are responsible for ensuring that their work is free of plagiarism. The lecture points to the PolyU Library's academic-integrity guide on GenAI and plagiarism. Note the implication: you cannot plead that the tool did it.
Using GenAI in figures, images and artwork
The lecture treats this as its own category because it is where the rules are strictest.
- A worked example from Cell (S0092-8674(24)00642-1) is shown as "an interesting example".
- A second example: authors made a video with help from ChatGPT and Suno — the ChromoGlass Destruction Derby rap for the 3D mammoth project hosted at aidenlab.org.
- The requirement in all cases is proper declaration.
And then the hard rule:
Accuracy, transparency and reproducibility
| Requirement | What you must do |
|---|---|
| Accuracy | The authors are accountable for the accuracy of the work. They must ensure all sources are appropriately cited, and should carefully review the work to guard against bias that may be introduced by AI. |
| Transparency and reproducibility | Authors should disclose detailed information about their use of GenAI: the full prompt used in producing the work, as well as the AI tool and its version. |
Reviewing manuscripts with GenAI
On peer review the guidance is negative and consistent across the major journals: it is generally not permitted to review manuscripts with GenAI. The slide names Nature, Science and Cell, and cites several Nature pieces on the subject (d41586-024-03588-8, d41586-025-00894-7, d41586-026-02631-0). This is the publication-facing twin of research recommendation 6.
Regulations are still moving
The lecture closes its publication section with a warning rather than a rule: GenAI is fast evolving, so do the regulations. Journals change policy as the technology changes. The instruction is to keep learning and keep an eye on regulation policies.
Take-home messages — the slide's own DO / DON'T
| DO | DON'T |
|---|---|
| Be informed of university and journal guidelines and uphold responsible use of GenAI. | Don't upload personal, proprietary or confidential information that could violate relevant policies when using GenAI. |
| Ensure the accuracy and reliability of the research output. | Do not use or publish anything created with GenAI without reviewing it for accuracy and plagiarism. |
| Keep sufficient records of the use of GenAI to ensure transparency and reproducibility of the results. | Don't rely on GenAI to write a substantial proportion of your essay or manuscript. |
| Follow local and international regulations about GenAI use. | Don't use GenAI to generate or manipulate figures for publication. |
| Keep learning. | — |
The ethics of AI itself
The lecture steps back from academic use to the technology as a whole.
Two further points close the argument:
- "Make AI safe" vs "make safe AI" — the discussion prompt, drawn from a UNESCO piece framing the two positions. The distinction matters: one is about constraining what exists, the other about building differently from the start.
- DeepSeek is highlighted as the first major LLM to undergo the peer-review process — a marker of how the boundary between AI systems and scientific publishing is itself moving.
Key references cited on the slide
| Source | Title |
|---|---|
| UNESCO | Guidance for generative AI in education and research |
| European Commission | Living guidelines on the responsible use of generative AI in research |
| PolyU | University Stance on the Use of Generative Artificial Intelligence in Learning, Teaching and Assessment |
| COPE | Authorship and AI tools: position statement |
| ICMJE | Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals |
| Science | "ChatGPT is fun, but not an author" · Change to policy on the use of generative AI and large language models |
Note on completeness: this session runs to 87 slides, many of which are figure, screenshot or diagram slides with no extractable text (e.g. the "How does GenAI work?", "Ethical AI Principles" and several case-study slides). The URLs and citations above are exactly those printed on the slides, so the sources can be retrieved directly if you want the missing visuals.