HKU professor apologizes after PhD student’s AI-assisted paper cites fabricated sources.
Updated
January 8, 2026 6:33 PM
.jpg)
The University of Hong Kong in Pok Fu Lam, Hong Kong Island. PHOTO: ADOBE STOCK
It’s no surprise that artificial intelligence, while remarkably capable, can also go astray—spinning convincing but entirely fabricated narratives. From politics to academia, AI’s “hallucinations” have repeatedly shown how powerful technology can go off-script when left unchecked.
Take Grok-2, for instance. In July 2024, the chatbot misled users about ballot deadlines in several U.S. states, just days after President Joe Biden dropped his re-election bid against former President Donald Trump. A year earlier, a U.S. lawyer found himself in court for relying on ChatGPT to draft a legal brief—only to discover that the AI tool had invented entire cases, citations and judicial opinions. And now, the academic world has its own cautionary tale.
Recently, a journal paper from the Department of Social Work and Social Administration at the University of Hong Kong was found to contain fabricated citations—sources apparently created by AI. The paper, titled “Forty Years of Fertility Transition in Hong Kong,” analyzed the decline in Hong Kong’s fertility rate over the past four decades. Authored by doctoral student Yiming Bai, along with Yip Siu-fai, Vice Dean of the Faculty of Social Sciences and other university officials, the study identified falling marriage rates as a key driver behind the city’s shrinking birth rate. The authors recommended structural reforms to make Hong Kong’s social and work environment more family-friendly.
But the credibility of the paper came into question when inconsistencies surfaced among its references. Out of 61 cited works, some included DOI (Digital Object Identifier) links that led to dead ends, displaying “DOI Not Found.” Others claimed to originate from academic journals, yet searches yielded no such publications.
Speaking to HK01, Yip acknowledged that his student had used AI tools to organize the citations but failed to verify the accuracy of the generated references. “As the corresponding author, I bear responsibility”, Yip said, apologizing for the damage caused to the University of Hong Kong and the journal’s reputation. He clarified that the paper itself had undergone two rounds of verification and that its content was not fabricated—only the citations had been mishandled.
Yip has since contacted the journal’s editor, who accepted his explanation and agreed to re-upload a corrected version in the coming days. A formal notice addressing the issue will also be released. Yip said he would personally review each citation “piece by piece” to ensure no errors remain.
As for the student involved, Yip described her as a diligent and high-performing researcher who made an honest mistake in her first attempt at using AI for academic assistance. Rather than penalize her, Yip chose a more constructive approach, urging her to take a course on how to use AI tools responsibly in academic research.
Ultimately, in an age where generative AI can produce everything from essays to legal arguments, there are two lessons to take away from this episode. First, AI is a powerful assistant, but only that. The final judgment must always rest with us. No matter how seamless the output seems, cross-checking and verifying information remain essential. Second, as AI becomes integral to academic and professional life, institutions must equip students and employees with the skills to use it responsibly. Training and mentorship are no longer optional; they’re the foundation for using AI to enhance, not undermine, human work.
Because in this age of intelligent machines, staying relevant isn’t about replacing human judgment with AI, it’s about learning how to work alongside it.
Keep Reading
METiS TechBio’s blockbuster IPO signals rising investor interest in AI startups focused on how drugs are delivered inside the body
Updated
May 14, 2026 3:02 PM
HIV-1 virus particles, coloured red. PHOTO: UNSPLASH
Investors are beginning to place bigger bets on AI startups focused on drug delivery infrastructure rather than drug discovery alone. That shift was on display this week after METiS TechBio, a Hong Kong tech-bio startup focused on AI-powered drug delivery systems, debuted on the Hong Kong Stock Exchange.
The listing made METiS TechBio the world’s first publicly traded AI-powered drug delivery startup and the first AI-powered large-molecule biopharmaceutical startup listed in Hong Kong. The startup raised more than HKD 2.1 billion through its IPO, making it the largest healthcare listing in Hong Kong so far in 2026.
Investor demand was unusually strong. The Hong Kong public offering was oversubscribed by more than 6,900 times while the international tranche recorded 82 times oversubscription. More than 280 institutional investors participated in the international placing.
The strong demand reflects a wider shift in AI biotech. Over the past few years, much of the sector’s attention has focused on using AI to discover new drugs or molecules. METiS is taking a different approach. The startup focuses on how medicines are delivered inside the body after they are developed.
That challenge is becoming harder to ignore in biotech. Designing a therapy is only one part of the process. Delivering it precisely to specific organs, tissues or cells remains a major hurdle, especially for newer therapies involving RNA, proteins and large-molecule drugs.
METiS is trying to solve that problem through its proprietary NanoForge platform. The system uses AI to design and test nanodelivery systems that help medicines reach targeted areas inside the body more efficiently. The platform combines AI models, simulation systems and high-throughput screening tools to speed up formulation development and improve delivery precision.
The startup says it has already achieved targeted delivery across eight organs and tissue systems including the liver, lungs, heart, muscles and central nervous system.
One of its lead programs, MTS-004, became China’s first AI-enabled formulation drug to complete a Phase III clinical trial. The drug is being developed for pseudobulbar affect, a neurological condition that affects emotional expression. According to the startup, AI tools helped reduce preclinical formulation development time from up to two years to less than three months.
Investor interest in the IPO also came from some of the world’s largest asset managers and healthcare funds. BlackRock led the cornerstone investments with a USD 50 million subscription. Other participating investors included UBS Asset Management Singapore, Mirae Asset, ORIX Corporation, Deerfield, RTW, Hillhouse Capital and IDG Capital.
METiS is also building what it describes as a “platform collaboration + product partnership” business model. The startup currently works with more than 30 pharmaceutical and biotechnology partners globally, including large pharmaceutical companies and medical research institutions.
The company reported RMB 105 million in revenue in 2025, largely tied to upfront payments connected to its MTS-004 partnership agreements. It also said some platform collaboration contracts could reach milestone values of up to USD 109 million.
Chris Lai said: "The future of biomedicine will no longer be simply about 'taking medicine when one falls ill.' METiS TechBio's ambition is to harness AI to build nano-rockets that can navigate with precision through the inner space of the human body's 30 trillion cells, write the code of nucleic acids and proteins into cells, and reprogram diseased and aging cells into healthy cells. This was our founding aspiration, and it is the mission to which we will dedicate our lives. The IPO marks a new starting point for us to accelerate forward, and we will strive to live up to the support and trust we have received from all sectors."
The IPO also highlights how Hong Kong is increasingly positioning itself as a hub for next-generation biotech and AI healthcare startups. While investor excitement around AI drug discovery has cooled in parts of the market, startups focused on delivery systems and biotech infrastructure are beginning to attract stronger institutional backing.
For METiS, the challenge now will be turning that investor confidence into commercially viable therapies and long-term partnerships. But the listing suggests that AI-driven drug delivery is starting to emerge as a category investors are willing to treat as core biotech infrastructure rather than a niche research experiment.