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Friday, July 17, 2026

 

Are you listening to me? Well, kinda… New Trinity research shows people can track more than one conversation at once





Trinity College Dublin

The research team in Trinity College Dublin. 

image: 

The research team in Trinity College Dublin, from left to right: Prof. Alejandro López Valdés, Dr Sara Carta, and Prof. Giovanni Di Liberto, from Trinity’s School of Computer Science and Statistics, the Trinity College Institute of Neuroscience (TCIN), and the ADAPT Research Ireland Centre for AI-Driven Digital Content Technology hosted by Trinity.

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Credit: Prof. Giovanni Di Liberto.






Ever wondered how some people seem able to keep up with the conversation they’re having while also noticing what’s being said across the room? New research suggests this ability isn’t simply good hearing but that it may reflect the brain’s remarkable capacity to briefly process more than one conversation at once.

Scientists at Trinity College Dublin have discovered that, for a short period of around one to two seconds, the brain can begin following a new conversation before it has fully let go of the previous one. The findings, published in leading international journal PLOS Biology, challenge the long-held view that we can only focus on one speaker at a time.

The discovery may help explain why some people are particularly good at navigating busy social situations, whether that’s discreetly picking up useful information, keeping an ear on an important announcement, or deciding whether another conversation is worth joining without completely losing track of the one they’re already in.

The researchers measured participants’ brain activity using electroencephalography (EEG) while they listened to two people speaking at the same time against a background of crowd noise. Participants were asked to switch their attention between the speakers while the researchers tracked how their brains responded.

They found that the brain starts engaging with the new speaker before it has fully disengaged from the first, creating a brief overlap in which both conversations are represented simultaneously. And this is visible on the EEG via a unique neural signature that pops up as the process occurs.

Professor Giovanni Di Liberto, from Trinity’s School of Computer Science and Statistics, the Trinity College Institute of Neuroscience (TCIN), and the ADAPT Research Ireland Centre for AI-Driven Digital Content Technology hosted by Trinity, is one of the senior authors of the research.  

He said: “Our findings suggest that some people may naturally be better multitaskers than others, allowing them to better explore what’s happening around them without immediately losing focus on their current conversation. This could help explain why some people seem especially good at navigating busy social environments.”

“Because this brief ‘dual tracking’ ability seems to differ from person to person, it potentially gives some individuals an advantage in situations where rapidly shifting attention is valuable.”

What is the potential impact of this research?

The work also has important practical implications because understanding how the brain naturally switches between competing voices could help scientists develop better hearing technologies, including smarter hearing aids that support not only focusing on one speaker but also exploring the wider sound environment more naturally. 

It could also improve understanding of why some people, including older adults and those with hearing difficulties, find busy places such as restaurants, workplaces and family gatherings particularly exhausting.

Ultimately, the work offers fresh insight into one of the brain’s most impressive everyday skills: helping us stay engaged in one conversation while remaining ready to respond when something more important catches our ear.

This work brought together scientists from Trinity, TCIN and ADAPT (Dr Sara Carta and supervisors Prof. Giovanni Di Liberto and Prof. Alejandro López Valdés), and the Eriksholm Research Centre (part of Oticon; co-supervisors Emina Aličković and Johannes Zaar). It was supported by funding from Research Ireland and the Demant foundation, and was organised via the Research Ireland Centre for Training in AI (CRT-AI).  

The experimetnal setup, showing a subject with brain signal cap, listening to more than one conversation at once.

Credit

Prof. Alejandro López Valdés.

Sunday, June 21, 2026

Interview

Trump’s Attacks on Black Power and Freedom Show How Far We Still Have to Go


Thinking on the Black freedom struggle from June 19, 1865, to now, where do we collectively want to see ourselves next?
June 18, 2026


LaTosha Brown, co-founder of the voting rights group Black Voters Matter, leads people in a chant as they walk across Edmund Pettus Bridge as they commemorate the 60th anniversary of "Bloody Sunday" on March 9, 2025, in Selma, Alabama.Michael M. Santiago / Getty Images


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How should Juneteenth be understood, be celebrated, be remembered, given this moment of deep anti-democratic machinations and authoritarian/fascistic ambitions cultivated at the “highest” offices within this land? The tension between the sense of renewed freedom that Juneteenth engenders and the profound unfreedom that this country perpetuates is not lost on me. Indeed, as the 250th anniversary of this nation’s independence is on the horizon, on July 4, 2026, the tension screams of a biting contradiction that forces the observation that the Fourth of July was never meant for Black people. It is within the context of this tension and contradiction that I conducted this exclusive interview with Jeanelle K. Hope, who is an independent scholar and a lecturer at the University of California–Washington Center, and co-author of The Black Antifascist Tradition.

George Yancy: It is such a pleasure to be in conversation with you again, Jeanelle. I am particularly excited to talk with you about what Juneteenth means to you. But first I want to return to our previous discussion and connect it to the recent attacks on the Voting Rights Act. In our last exchange, you laid bare the historical dimensions, political commitments, and courageous spirit of the Black anti-fascist tradition. In that exchange, you argued that we must remember that “fascism attacks on all fronts, so we must develop a strategy that recognizes this.” As I reread your instructive and powerful words, I was reminded of the Supreme Court’s conservative majority decision, on April 29, 2026, in Louisiana v. Callais, which has been characterized as an evisceration of — or, at minimum, a significant weakening of — “Section 2 of the Voting Rights Act (VRA) and opened the door for states to enact discriminatory voting maps and laws.”

Justice Samuel Alito wrote in the majority opinion in Louisiana v. Callais that Section 2 of the Voting Rights Act imposes liability “only when circumstances give rise to a strong inference that intentional discrimination occurred.” This standard requires Black plaintiffs to demonstrate intentional racial discrimination. In her book Where Is Your Body?, critical race theorist Mari J. Matsuda critiques the concept of a narrow “linear, intent-based notion of causation” when it comes to racism, arguing that “if the effects of racism exist, that is cause for action.” My colleague, historian Carol Anderson, states: “Jim Crow was a political project designed to preserve racial hierarchy through law and the power structure that depended on it. When we see coordinated efforts to purge voters, centralize election control, dismantle the Voting Rights Act, and dilute Black political power, we should call it what it is.” Within the context of your important historical and political work, let’s call it what it is: the unabashed continuation of anti-Black fascism.




U.S. philosopher George Santayana warned, “Those who cannot remember the past are condemned to repeat it.” I would argue that the problem for Black people is not a failure to remember the past; rather, it is that the past is not past. We are witnessing the continuation of systems and practices that remind us, repeatedly, that we have no rights that the white state is bound to respect. In short, Black people continue to have their rights violated, their freedoms politically constrained, and their ontology, their being, rendered abject. Speak to how you see this latest attack on Black people’s voting power and freedom — which amounts to a form of civil death — as a manifestation of anti-Black fascism. For those who may not view it as such, what are they missing?

Jeanelle K. Hope: During this election season, we are seeing an all-out conspiracy to effectively roll back voting rights, which Black people have steadily fought for, with redistricting being the main strategy that has effectively sanctioned mass disenfranchisement. The efforts, largely taken up by Southern states, elucidate two major pillars of anti-Black fascism: anti-democracy and dual application of the law. The recent Louisiana v. Callais decision is another example of how the law is being weaponized to cement major objectives within the fascist project. It is evident that the primary goal of Southern state redistricting efforts, and gerrymandering, is to starkly dilute Black voting power, smashing any remaining illusion of democracy for Black people in Texas, Louisiana, Tennessee, Alabama…


The Black Anti-Fascist Tradition Recognized Fascism Didn’t Begin in Europe
Black anti-fascists have long warned about creeping fascism, from slavery to mass incarceration to ICE terror. 
By George Yancy , Truthout February 21, 2026

As I shared in our last interview, the law is such an integral player in shaping fascism. Despite the liberal belief that the law will save us from fascism, what we are seeing unfold within the courts is the strategy of autocratic legalism — or the creeping dismantling of legal frameworks that have long aided democracy to bolster the consolidation of power for authoritarian and fascist regimes.


“What we are seeing unfold within the courts is the strategy of autocratic legalism — or the creeping dismantling of legal frameworks that have long aided democracy to bolster the consolidation of power for authoritarian and fascist regimes.”

The legal analyses of Matsuda, Derrick Bell, and Kimberlé Crenshaw have long served as sharp critiques of the law’s failure to recognize intersecting forms of oppression, systemic racism, and the upholding of a dual application of the law along racial lines. Moreover, they have been most vocal about the high, and shifting, burden of proof required by the courts when victims name racist and discriminatory actions as stymieing their life, liberty, and freedom. The core arguments of critical race scholars and lawyers can similarly be applied to the dismantling of the Voting Rights Act. The law has once again proven to not be a tool of justice, but a chisel to decide those that should and should not benefit from democracy.

For the last four years, critical race theory (CRT) has been vehemently attacked by far right and conservative-led school boards and think tanks, with history, English, and ethnic studies K-12 teachers and librarians also caught in the crosshairs. Perhaps it is CRT’s keen explication of U.S. law’s fascist and anti-democratic tendencies that was most threatening, not the thousands of books by Black authors that were banned in the name of CRT.

Living in the Washington, D.C., metro area and having been born and raised in California, it’s not lost on me that redistricting has also been adopted by Democratic-led states as a means of “fighting fire with fire” to “settle the score.” While it looks like California’s redistricting efforts are largely being upheld by the courts, the strategy, though blessed by a majority of voters, is dead in the water in Virginia. In a state where Black people comprise nearly 20 percent of the population and Northern Virginia is nearing “minority majority” status, you can’t unsee the glaring contradictions of the law that once again favors anti-Black fascism over justice.

In response to Virginia’s redistricting referendum, a member of Congress introduced the “Make DC Square Again Act” that would restore the District’s original boundaries for the sole purpose of disenfranchising Northern Virginians by leveraging Washington, D.C.’s lack of statehood. This bill and far right commentary (including the growing position of conservative women willing to give up their right to vote for a more conservative future) regarding redistricting lay bare that disenfranchisement is a priority for the modern American fascist project.

Just last year, Black historical associations and legal groups celebrated 60 years since the passage of the Voting Rights Act, a commemorative moment that felt quite hollow, considering how the legislation was being gutted. In this moment, we must wrestle with what to do over the next 60 years to regain ground lost and to transform what democracy looks like. Redistricting (from the Democrats and Republicans) is a race to the bottom, toward fascism. In the words of Audre Lorde, “the master’s tools will never dismantle the master’s house.” We must stop trying to meet the rise of the far right with the same fascist strategies dressed up in progressive and liberal language. The recent release of “Project 2029” (which leaves much to be desired), the Democrats’ supposed response to Project 2025, is another example of this. Fascism must be met with radical imagination and ambitious world-remaking — not with a reactionary policy framework that is always five steps behind. We must radically reimagine voting, elections (Election Day should be a national holiday), political parties (no more duopoly and get rid of dark money in elections), the Electoral College (how about just the popular vote), and much more.

I began with that recent major attack on the Voting Rights Act as a way of highlighting the deep sense of the tragicomic reality of Black life. As Black people, we constantly strive for freedom, empowerment, and joy. Yet, the viciousness of anti-Blackness forces us into various states of unfreedom, disempowerment, despair. When I think of Juneteenth, I think of the tragicomic. There was the brutality of American slavery, and yet there was that sense of celebration and elation “on June 19, 1865, when some 2,000 Union troops arrived in Galveston Bay, Texas. The army announced that the more than 250,000 enslaved black people in the state, were free by executive decree.” That joyous reality would only be followed by involuntary servitude through the criminalization of the Black body, the creation of Black Codes, convict leasing, and sharecropping. Add to this the reality of mass incarceration of Black people; disproportionate vulnerability to police and state violence; inequitable access to health care, housing, and education; disproportionately high poverty rates; and limited access to economic opportunities and growth. Given these realities, we find ourselves in what Saidiya Hartman terms “the afterlife of slavery” and what Christina Sharpe calls being in “the wake” — that is, that we are still mourning the effects of the transatlantic slave trade. How do you think about Juneteenth within the context of so much anti-Blackness; indeed, the continuation of anti-Black fascism?

There’s a bittersweetness to Juneteenth. On the one hand, there remains a level of excitement as the holiday moves into its sixth year of national recognition following decades of advocacy by people like Ms. Opal Lee. Yet, Juneteenth is probably the single most consequential American holiday as it forces us to truly grapple with the meaning of freedom in a more critical way than the Fourth of July. To borrow from Angela Davis, Juneteenth is a reminder that “freedom is a constant struggle,” and that the old seeds of slavery continue to germinate, taking root in many systems, institutions, and facets of modern Black life — from state-sanctioned violence and mass incarceration to varying structural inequalities.



“To borrow from Angela Davis, Juneteenth is a reminder that ‘freedom is a constant struggle,’ and that the old seeds of slavery continue to germinate, taking root in many systems.”


Juneteenth urges us to consider how we can work in the spirit of abolitionists to root out every seed of slavery. And as anti-Black fascism continues to evolve from its colonial and chattel slavery foundations to more sophisticated outcomes, the Juneteenth holiday demands that we reimagine what freedom fighting looks like. With our nation’s current march toward fascism, so many of our supposed freedoms are on the line — from voting rights and citizenship, freedom of speech, academic freedom, the freedom to protest, and beyond. Let the Juneteenth holiday serve as a reminder of the ever-shifting grounds of freedom.

Thus, celebrating Juneteenth must entail study, organizing, and dreaming. I hope that those celebrating Juneteenth engage in much-needed consciousness raising. Read. Not only about the history of slavery, abolition, and Juneteenth, but also more contemporary works that help elucidate slavery’s afterlives. Juneteenth is a communal event and holiday. So before firing up the barbeque, breaking out the seafood boil, or busting open a box of crabs, I hope folks sit with one another in critical reflection and discussion. We must all consider what freedom fighting looks like within our communities and strategize how to work together in those efforts. As Robin D.G. Kelley’s early work reminds us, for the enslaved and so many Black activists, freedom was only a dream. We must use the Juneteenth holiday to also dream of what new iterations of freedom look like — a freedom beyond anti-Black fascism.


“For the enslaved and so many Black activists, freedom was only a dream. We must use the Juneteenth holiday to also dream of what new iterations of freedom look like.”

Despite the bitterness of Juneteenth, it is Black joy that encompasses the sweetness of the holiday. It is a sweetness that extends to glasses of red drinks, plates of red velvet cake, and slices of watermelon. There is something especially sacred in commemorating Juneteenth through gathering, partaking in Black foodways, dancing, and laughing. I hope that we all revel in joy this Juneteenth.

Juneteenth will precede the 250th anniversary of this nation’s independence on July 4, 2026. I am reminded of the scathing critique by Frederick Douglass, where he writes, “What, to the American slave, is your 4th of July?I answer: a day that reveals to him, more than all other days in the year, the gross injustice and cruelty to which he is the constant victim. To him, your celebration is a sham.” As I celebrate Juneteenth, I’m simultaneously aware of the sham of this country’s “greatness” in relationship to the continued violent and dehumanizing logics of anti-Blackness. As this nation celebrates its 250th anniversary, Douglass’s truth-telling to this nation continues to hold: “Your boasted liberty, an unholy license;your national greatness, swelling vanity; your sounds of rejoicing are empty and heartless; your denunciations of tyrants, brass fronted impudence; your shouts of liberty and equality, hollow mockery.” Reading your work militates against perpetuating shams. There are times when living in bad faith — lying to ourselves — can feel easier than facing social and civil death. But willfully remaining ignorant will not stop anti-Black fascistic violence, just as remaining critically conscious will not, on its own, stop it either. Yet we still need to remain critically conscious. How do we do so, especially in the context of the upcoming 250th anniversary of this nation’s independence, a day which will be filled with praise, celebration, and, for me, deep hypocrisy? Talk about how necessary it is that we maintain a critical consciousness of this nation’s bloody history, pretense, and anti-Blackness.



















The 250th anniversary of the nation’s independence has presented a unique narrative and branding (dare I say grifting) opportunity that has largely been used to support America’s march toward fascism. Living in the Washington, D.C., metro area, the thick veneer of patriotism and nationalism expressed via “America 250” and “Freedom 250” programming and branding is inescapable. Plans to celebrate this moment involve completely reshaping the cultural atmosphere and ethos of Washington, D.C. A few highlights include: the construction of a 250-foot arch (a Western architectural symbol of power and expansion) near Arlington National Cemetery, a mixed martial arts UFC fight recently held on the White House lawn (the event somehow ended with a racist and misogynist jab at former first lady Michelle Obama), the National Mall is currently being transformed into a “State Fair” (it should be noted that this “State Fair” is being held in a District long denied statehood), and a Grand Prix auto race is slated to take place around major monuments. Ironically, much of the National Mall is surrounded by tall fencing, making the space look more like a police state rather than the projected freedom playground. Furthermore, institutions and artists are being called to “promote American and Western values” in a manner that conveys “greatness,” “grandeur,” and “abundance,” while limiting discussions that underscore the very roots of the nation — colonialism, slavery, and Native American genocide. America 250 has effectively served as a narrative tool to reshape U.S. culture through far right politics, Christian nationalism, hypermasculinity, and fascism. The 250th anniversary of U.S. independence, especially under the current political climate, must be understood as a paradoxical moment to interrogate U.S. democracy and freedom, not a moment to lean into the cultural spectacle of “America 250” and “Freedom 250” branding.

With the residents of Washington, D.C., being forced to host all these events across their communities, I challenge folks to shift their gaze from America 250 to the renewed Free DC movement. Since the late 1700s, Washingtonians (who are predominantly Black) have been denied statehood, self-determination, and full participation in democracy — an enduring punishment for the District’s predominantly Black population that traces back to debates around slavery in the region. If we are to celebrate independence and freedom in the nation’s capital, why is it that the people who live year-round in the capital aren’t afforded the fundamental freedom of statehood?

I want to underscore that what UFC fighter Josh Hokit said about Michelle Obama was vile, ignorant, and racist. Given the importance of Juneteenth, I don’t want to end on a pessimistic note, even if pessimism is fully justified. James Baldwin, who was passionately dedicated to radically transforming this country through love — and by demanding that it look at itself in a disagreeable mirror and admit to the lie of its “innocence” — was still skeptical. In The Fire Next Time, he asks, “Do I really want to be integrated into a burning house?” It seems to me that if the house is burning, we have been locked in it — and never fully integrated — since the beginning of our arrival. Despite this, we have found ways of pushing back and talking back. We have been able to create, invent, love ourselves, find joy, and deploy our Black imaginations to think and be otherwise.

In your book The Black Antifascist Tradition, you stress the importance of abolition. In fact, I would argue that if the house has been burning for so long, then perhaps it is no longer habitable until the ashes have been swept away to allow for forms of clarity, insight, and wisdom that will begin with the kind of profound and mature love, as Baldwin understood it, that might function as the foundation for a radically new, unprecedented way of political belonging.

You don’t stop at abolition. You write, “The endpoint of abolition is not destruction but futurity.” You link abolition to Afrofuturism. How do you understand the creative dynamics of Afrofuturism? As you know, not all Black people will experience or celebrate Juneteenth with a sense of political exuberance or reflect on how we have made so much “progress.” I’m interested in how you think about Afrofuturism alongside abolition, because the latter suggests a radical future — something yet to come.

As mentioned earlier, in celebrating Juneteenth we must incorporate dreaming. When we dream, we can imagine futures that go beyond the status quo and reform. We place our future selves somewhere anew, with environments that are defined by our holistic well-being. With all that we know about the Black freedom struggle from June 19, 1865, to the present, where do we collectively (not individually) want to see ourselves next?Art and culture are often great avenues to explore this type of imagining and radical world-building. This is why fascism actively works to co-opt and control art, media, and cultural production.

There is no greater entry point to discussing the intersection of Black anti-fascism and Afrofuturism than the work of pioneering Afrofuturist writer Octavia Butler. Folks have long discussed how Parable of the Sower outlines the rise of fascism in the 21st century, but it is her follow-up work, Parable of the Talents, where Butler engages in this deeply Black anti-fascist world rebuilding. Beyond Butler, there are so many artists that are creating various forms of art and culture that help us dream of a new future. For example, I’m still sitting with Boots Riley’s film I Love Boosters and the vision he lays out for the future of organizing under technofascism. I’m similarly wrestling with Aleshea Harris’s disturbing yet liberating journey through a tale of Black women’s vengeance in Is God Is. Both films offer poignant meditations on Black futures and freedom, while not shying away from the pessimism of it all. I think it’s important to let art and culture — particularly independent, political, and Afrofuturist art — serve as a guiding light as we dream.


This article is licensed under Creative Commons (CC BY-NC-ND 4.0), and you are free to share and republish under the terms of the license.




George Yancy

George Yancy is the Samuel Candler Dobbs professor of philosophy at Emory University and a Montgomery fellow at Dartmouth College. He is also the University of Pennsylvania’s inaugural fellow in the Provost’s Distinguished Faculty Fellowship Program (2019-2020 academic year). He is the author, editor and co-editor of over 25 books, including Black Bodies, White Gazes; Look, A White; Backlash: What Happens When We Talk Honestly about Racism in America; and Across Black Spaces: Essays and Interviews from an American Philosopher published by Rowman & Littlefield in 2020. His most recent books include a collection of critical interviews entitled, Until Our Lungs Give Out: Conversations on Race, Justice, and the Future (Rowman & Littlefield, 2023), and a coedited book (with philosopher Bill Bywater) entitled, In Sheep’s Clothing: The Idolatry of White Christian Nationalism (Roman & Littlefield, 2024).


Friday, June 05, 2026

 

New Novatug Training Centre to Boost Tug Safety and Efficiency

Wärtsilä
Novatug picture

Published Jun 3, 2026 5:18 PM by The Maritime Executive

[By: Wärtsilä]

Technology group Wärtsilä has delivered an advanced simulation suite for Novatug’s newly opened training centre in Terneuzen, the Netherlands, which has been designed to promote safer tug operations and raise the level of specialised master training. Developed in close collaboration with Novatug, the innovation and R&D division of Multraship Towage & Salvage, the simulation suite includes full mission simulators, mixed reality sets, an instructor operating station, and a debriefing room, alongside custom digital models including a Carrousel Rave Tug (CRT) model for specialised tug master training. The order was booked by Wärtsilä in Q4 2025. 

This new simulation suite enables high fidelity modelling of Novatug’s new CRT, providing enhanced control over assisted ships while they are manoeuvring in port. The CRT overcomes growing challenges related to port-calls of very large- and ultra large cargo vessels by improving their steering and braking capabilities in confined spaces, whilst also reducing fuel consumption and emissions. Therefore, the specially configured Wärtsilä simulators, featuring digital models for mandatory, professional development and competency training, as well as applied research, allow tug masters to attain proficiency in operating the CRT and a range of other modern tugs in a safe and controlled environment. 

“We value Wärtsilä’s vast simulation expertise to model the Carrousel Rave Tug and provide the most realistic training environment possible. This strategic training partnership will elevate the safety and efficiency of tug operations for shipping companies and ports worldwide by raising the level of specialised training”, says Leendert Muller, Managing Director of Multraship Towage & Salvage. 

The Novatug CRT uses a patented Carrousel towing system, where the towing point can rotate around the tug. This design keeps towing forces under control and eliminates the risk of capsizing due to a towload, while also improving braking and steering so that assisted ships can be handled more safely and efficiently in port. The inclusion of the CRT in Wärtsilä’s simulator enables tug masters to rehearse specific CRT manoeuvres realistically and repeatedly, thus giving instructors a consistent way to assess competence before the skills are applied in live operations. 

“Collaboration and partnerships between maritime stakeholders are essential for ensuring an efficient future for the shipping industry”, comments Johan Ekvall, Director, Simulation & Training, Wärtsilä Marine. “New vessel innovations need to be backed by training. By providing a realistic and controlled environment for specialised learning, simulation can help close critical skill gaps and better prepare tug masters for current and future operational demands.” 

With more than 30 years of experience and expertise in digital modelling of vessels and their functionalities, Wärtsilä is supporting Novatug in strengthening crew safety, tow operations and the wider port network.

The products and services herein described in this press release are not endorsed by The Maritime Executive.

Tuesday, May 12, 2026

 

Generative artificial intelligence can significantly reduce the number of animal experiments

Between 30 and 50 percent fewer mice for pharmacological research experiments



Goethe University Frankfurt





FRANKFURT. In early phases of drug development, new active substances are tested in animals –alongside numerous other experimental methods. Researchers face a dilemma: on the one hand, for ethical reasons, they aim to keep the number of animals used in an experiment as low as possible. On the other hand, animal experiments must include enough animals to produce reliable and representative results, for example to determine whether a new drug candidate produces a specific effect.

Professor Jörn Lötsch, data scientist and clinical pharmacologist at Goethe University, in cooperation with computer scientist Professor Alfred Ultsch from Philipps University Marburg—neither of whom conducts animal experiments himself—has developed a generative artificial intelligence called genESOM. genESOM is based on a network of thousands of artificial neurons that “learns” the internal structure of a dataset. This allows it to expand the volume of experimentally obtained data and simulate a larger number of animals in the experiment than were actually used.

Integrated Error Monitoring

To train the AI, the scientists used existing data from a previously published mouse study conducted at Fraunhofer ITMP. The research team achieved two key innovations: first, training the AI to generate new data points based on the study data that integrate into the learned data structure as if they had been obtained in real experiments.

The second innovation was integrating error monitoring directly into the process of generating new data points. Generative AI methods generally risk amplifying not only the relevant signal but also noise and random variation. This problem is known as error inflation and can lead to variables that are actually insignificant being incorrectly identified as treatment-relevant (so-called false-positive variables).

By deliberately separating the learning phase from the synthesis phase, it becomes possible to introduce an artificial error signal into the process and precisely measure its propagation. This results in a data-driven stopping criterion that halts data generation before scientific validity is compromised.

AI Training with Published Study Data

genESOM passed a practical test using data from a preclinical study on a multiple sclerosis model. In the original study, 26 mice were divided into three treatment groups to investigate the effects of an experimental drug. Lötsch and Ultsch reduced the dataset to 18 animals (six per group) to simulate a smaller experiment. When they analyzed this reduced dataset, all previously detected treatment effects disappeared completely: statistical tests showed no significance, and machine learning methods could not distinguish between the treatment groups. After augmenting the reduced dataset with additional data points using genESOM, all effects of the full experiment reappeared at the original level of significance – without introducing relevant false-positive findings. Alternative AI methods, including complex deep-learning neural networks tested by the researchers, failed in this case.

Lötsch explains: “We have now tested a number of datasets in a similar way and can say today: with genESOM, the number of animals used in exploratory research can be reduced by 30 to 50 percent while maintaining scientific validity.” However, the data scientist emphasizes that genESOM can only learn from data obtained in real animal experiments. Nor can the number of laboratory animals be reduced arbitrarily: “If too few animals are included in an experiment and the number is then simply supplemented using generative AI, the experiment could quickly become scientifically worthless due to the amplification of random findings.” Nevertheless, Lötsch is convinced: “With genESOM, we can make an important contribution to reducing the number of animal experiments in large areas of preclinical research.”

The project was funded by the German Research Foundation (DFG) under the title “Generative artificial intelligence-based algorithm to increase the predictivity of preclinical studies while keeping sample sizes small.”

Publications:
Jörn Lötsch, Benjamin Mayer, Natasja de Bruin, Alfred Ultsch: Self-organizing neural network-based generative AI with embedded error inflation control enhances effective knowledge extraction from preclinical studies with reduced sample size. Pharmacological Research (2026) https://doi.org/10.1016/j.phrs.2026.108159

Jörn Lötsch, André Himmelspach, Dario Kringel: Dimensionality-modulated generative AI for safe biomedical dataset augmentation. iScience (2026) https://doi.org/10.1016/j.isci.2025.114321 

Alfred Ultsch, Jörn Lötsch: Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks Open Access. Briefings in Bioinformatics (2024) https://doi.org/10.1093/bib/bbae640
 

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Robots, AI to help shipbuilding stay on track


American and Japanese researchers will develop robots and AI to help shipbuilders pivot when the built ship deviates from the planned design



University of Michigan






Key takeaways

  • As ships are built, internal parts—pipes, cables and equipment—can arrive out of order, and scheduling pressure can cause parts to be installed such that the remaining parts no longer fit as expected.

  • Re-installing parts could delay construction, but robotic and AI assistants can help shipbuilders catch problems early and predict issues ahead of time, as well as suggest solutions.

  • University of Michigan Engineering leads a team of American researchers developing the technology, with a $6.2M grant from the Japanese Ministry of Land, Infrastructure, Transport and Tourism.

 

Autonomous robots and AI models could help shipyard workers catch when a ship's built structure differs from design drawings, allowing workers to fix problems or adapt sooner. University of Michigan Engineering is leading the American arm of an international project to develop such a system. 

 

Funded with a $6.2 million grant from the Japanese Ministry of Land, Infrastructure, Transport and Tourism, the collaboration will design and prototype AI and robot teammates to track what was actually built inside the growing ship and compare it to a digital twin of the intended structure. The system will then create reports of mismatches that workers can use to make adjustments.
 

"We want to build a co-pilot system that uses AI and robotics to take some of the detective work off workers' shoulders," said Alan Papalia, U-M assistant professor of naval architecture and marine engineering and the principal investigator of the American research team. "The system should automatically map what's installed, identify where reality is drifting from the design, and suggest workable alternatives when something needs to change."

 

Papalia's team includes researchers from U-M and the Massachusetts Institute of Technology. The project is funded through the first quarter 2027 and overseen by the Monohakobi Technology Institute, an R&D Center within NYK Line, a global shipping and logistics company based in Japan.

 

"It's very complementary to our other research projects led by Japanese universities, in which the main focus is robots for automation of hull construction and steel welding," said Hideyuki Ando, managing director of the Monohakobi Technology Institute.

"We wanted to partner with the University of Michigan because of their unique status as a high-output research university with a dedicated department for naval architecture and marine engineering."

 

Helping construction stay on track


The American team is developing technology to help shipyard workers with outfitting—the installation of pipes, cables, electrical systems and other equipment inside the ship. Hundreds of thousands of individual components have to be placed inside confined, changing spaces, and scheduling pressure often causes the outfitting schedule to be dictated by crew and part availability rather than an ideal build sequence. 

 

In the shifting build schedule, workers can find that parts don't fit as they expected and the original drawings sometimes prove impractical as outfitting progresses. Compartments may have closed earlier than expected, and the shortest route to an electrical box or pipe may be blocked. If issues aren't caught early, some installations may need to be reworked, which could delay delivery of the ship.

 

To help workers pivot, the robots will be designed to roam the growing ship structure and collect LiDAR and camera data that will be fed to an AI model along with other human-made measurements. The AI model will then construct a digital model of the built structure to be compared with the intended design. With the digital model, the AI will look for deviations from the plan and predict problems that may arise based on how equipment has been installed. 

 

When the model finds a problem—such as a pipe that no longer fits as expected or a build sequence that will likely be disrupted—the system will generate a list of potential solutions and the tradeoffs between them. With that information, workers can verify problems and decide how to resolve them. The entire robotic system will be automated to help alleviate some of the burden of verifying that construction is on track, but the AI model will also flag when and where it has insufficient sensor data, so that people can help fill in gaps as needed.

 

Training shipbuilding helpers 

 

To train the AI to understand the robot's images of the ship and identify problems, the researchers will create a synthetic dataset by simulating the shipbuilding process many times. The researchers will also interview tradespeople at shipyards in the U.S. and Japan to ensure that the AI matches how skilled workers reason on the job and provides realistic suggestions.

 

Once trained, the AI could potentially run at an offline workstation, a remote server wirelessly connected to the robot or on the robot itself.

 

The robots and AI models will be tested with a new physical model of a ship section, which the researchers call the Shipbuilding Test Block. The model will be reconfigurable so that it can represent many different stages of outfitting, shipboard systems and shipbuilding issues.

 

The roles of American team members include:

 

  • Development of robotic systems and algorithms for ship outfitting, led by Papalia

  • Establishment of shipyard collaborations, managed by Dave Singer, a professor of naval architecture and marine engineering

  • Interviews with tradespeople and the integration of human knowledge, led by Matt Collette, professor of naval architecture and marine engineering; Leia Stirling, professor of robotics and industrial and operations engineering; and Patricia Alves-Oliveira, assistant professor of robotics 

  • Design and production of the Shipbuilding Test Block, led by Thomas McKenney, associate professor of practice in naval architecture and marine engineering

  • Development of AI models that can process multiple kinds of data to help find optimal solutions, led by Faez Ahmed, associate professor of mechanical engineering at MIT.

 

The complementary Japanese projects are led by Yokohama National University, Osaka University, Osaka Metropolitan University and the National Maritime Research Institute. 

Reasoning like a human: New prompting strategy boosts AI accuracy in healthcare advice



New study finds that mimicking human intuition helps ChatGPT better identify when patients can safely use self-care.




JMIR Publications

Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study 

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Co-author Marvin Kopka from the Division of Ergonomics, Department of Psychology & Ergonomics (IPA) at Technische Universität Berlin.

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Credit: Marvin Kopka




(Toronto, May 11, 2026) Researchers at Technische Universität Berlin have discovered that teaching Large Language Models (LLMs) to mimic human intuition and reasoning significantly improves their ability to provide accurate medical care-seeking advice. The study, published in JMIR Biomedical Engineering from JMIR Publications, suggests a paradigm shift in prompt engineering: moving away from computer-focused instructions toward strategies rooted in applied psychology.

As millions of users turn to tools like ChatGPT for health advice, a persistent issue remains: AI often defaults to emergency or professional care recommendations, even for minor issues, out of extreme caution. This over-triage can lead to unnecessary healthcare costs and patient anxiety.

The Breakthrough: Naturalistic Decision-Making (NDM)

The research team, led by Marvin Kopka and Markus A. Feufel, tested 10 different ChatGPT models (including the newest GPT-4o and GPT-5 series) using prompts inspired by Naturalistic Decision-Making (NDM). Unlike traditional logic, NDM focuses on how human experts make high-stakes decisions under uncertainty.

The study utilized two specific psychological frameworks:

  • Recognition-Primed Decision-Making (RPD): Instructing the AI to match the patient’s symptoms to "ypical cases and mentally simulate the outcome.

  • Data-Frame Theory: Tasking the AI to build a mental frame of the situation and constantly question it as new data emerges.

Key Results

  • Significant Accuracy Boost: NDM-inspired prompts increased overall accuracy across all models. The most notable gains were in self-care advice, which jumped from a meager 13.4% with standard prompts to nearly 30% with NDM reasoning.

  • Activating "Thinking" in Simpler Models: Non-reasoning models (which typically failed to identify self-care cases) began providing accurate, nuanced advice when given a "human reasoning blueprint."

  • Safety Maintained: While the AI became better at identifying when it was safe to stay home, it maintained its high accuracy in identifying true emergencies.

“When testing AI, we too often give it perfect information and then see that it performs extremely well,” said author Marvin Kopka. “But many problems in the real world are ill-defined. We have good models for how experts make decisions in such situations, so using them as prompts seemed like an obvious next step. I hope that applying human decision-making to LLMs will help us develop AI tools that are also useful in real-world decision-making.”

Bridging the Gap to Personalized Medicine

The study suggests that in real-world situations, where medical data is often messy or incomplete, a "reasoning blueprint" based on human cognition can be more effective than standard computational logic. By instructing the AI to simulate outcomes and question its own initial "frames" of a situation, the researchers were able to mitigate the common AI tendency toward over-caution.

While these findings mark a significant step forward in making LLMs more effective partners in clinical decision-making, the team notes that the model is currently best suited for controlled environments. Future research will be essential to determine if these NDM-inspired prompts translate into better decision support for everyday users in non-standardized settings.

Recognition for Excellence

About the Author Team: The research was conducted by Marvin Kopka and Markus A. Feufel at the Division of Ergonomics, Department of Psychology & Ergonomics (IPA) at Technische Universität Berlin. Their work focuses on human factors and the safe integration of AI into human decision-making environments. Marvin was recently recognized as one of the five winners of the 2025 JMIR Publications Early Career Researcher Award, an honor that underscores the caliber and impact of the research presented in this study.

 

Original article: Kopka M, Feufel M. Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study. JMIR Biomed Eng 2026;11:e88053

URL: https://biomedeng.jmir.org/2026/1/e88053

DOI: 10.2196/88053
 

About JMIR Publications

JMIR Publications is a leading open access publisher of digital health research and a champion of open science. With a focus on author advocacy and research amplification, JMIR Publications apartners with researchers to advance their careers and maximize the impact of their work. As a technology organization with publishing at its core, we provide innovative tools and resources that go beyond traditional publishing, supporting researchers at every step of the dissemination process. Our portfolio features a range of peer-reviewed journals, including the renowned Journal of Medical Internet Research. 

To learn more about JMIR Publications, please visit jmirpublications.com or connect with us via XLinkedInYouTubeFacebook, and Instagram.

Head office: 130 Queens Quay East, Unit 1100, Toronto, ON, M5A 0P6 Canada

Media contact: communications@jmir.org

The content of this communication is licensed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, published by JMIR Publications, is properly cited.

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Smart AI gives electric vehicle batteries 23 per cent longer life – without increasing the charging time



Chalmers University of Technology

Smart AI charging can extend electric car battery life 

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Smart AI charging can extend electric car battery life by 23 percent – ​​without increasing charging time. Photo: Ivan Radic | CC BY 2 0

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Credit: Ivan Radic





Fast charging shortens the life of vehicle batteries, but is necessary on longer journeys with electric vehicles. Researchers at Chalmers University of Technology, Sweden, have now developed a new AI method that adapts fast charging to the health of the battery. Their study shows that battery life can be increased by almost 23 per cent without extending the charging time. All that is required is an update of the vehicle’s software.

When individuals or companies consider acquiring electric vehicles, the possibility of fast charging is an important factor.

“For taxis or heavy vehicles in industry, for example, access to fast charging means a lot, but this is also true for passenger cars. Although private motorists usually charge their electric cars at home, the availability of fast charging outside the home is a crucial factor, as it facilitates commuting and driving over longer distances,” says Changfu Zou, professor at the Department of Electrical Engineering at Chalmers.

Electric vehicle batteries currently have a life of approximately 8-15 years*, depending on use and charging. Several studies of the European EV market* show that consumers who are considering buying an EV are concerned about the limited life of batteries.

The requirement for efficient fast charging is also in conflict with battery health, as such charging is stressful for the batteries and shortens their life.

Changfu Zou has taken on this challenge with Meng Yuan, Assistant Professor at Victoria University of Wellington, New Zealand, and a former researcher at Chalmers. In the recently published study, they show that it is possible to increase the life of batteries without significantly increasing the charging speed – with the help of artificial intelligence.

Adapting charging to battery health

In the study, the researchers present an AI-based charging strategy that adapts the current during each fast charge to the battery’s chemistry and ‘state of health’. The adapted charging extends battery life by around 23 per cent compared to the standard method today. At the same time, the charging time is unaffected, give or take a few seconds.

“We show that it is possible to charge more or less as fast as today, but with significantly less long-term degradation of the battery,” says Meng Yuan.

When a battery is charged fast, a large current is forced into the various cells, which causes a greater risk of chemical side reactions, among other things. One of the most problematic is known as lithium plating, in which metallic lithium precipitates on the electrode instead of being stored correctly in the battery’s structure. This can reduce capacity and may also affect safety, as unevenness in the structure of the lithium can, in a worst case scenario, cause a short circuit.

“The risk of lithium plating increases with the age of the battery. However, the standard methods of charging today use the same current and voltage regardless of whether the battery is new or has been used for years,” says Meng Yuan.

Short charging time and less wear and tear

The new, AI-based charging strategy is based on reinforcement learning**, in which the right actions are rewarded and thus reinforced. The training environment consisted of a model of one of the most common electric vehicle batteries on the market and a simulation of the parameters that have an impact on both charging time and battery health.

The AI model was trained to adapt the charging according to how charged or discharged the battery was at the time of charging. It also needed to take into account the overall health of the battery, as this is crucial to both capacity and electrochemistry. The result was a charging strategy that both keeps the charging time short and minimises harmful reactions.

“Our study shows that smart adaptation of the current during charging, taking into account the changing electrochemical state of the battery, can maximise both the performance and the life of the battery,” says Changfu Zou.

Easy to implement – but adaptation required

The new charging strategy is both easy and cost-effective to implement, according to the researchers: in principle, it could be implemented through software updates in the vehicle's battery management systems. However, some adaptation is needed for the method to be used generally.

“There are not so many different battery types today, but the method needs to be calibrated for it to be used by everyone. Using transfer learning, we can take advantage of what our AI model has already learned, and thus adapt the AI model to new batteries more quickly,” says Changfu Zou.

The next step is to test the method directly on physical batteries. The researchers hope that the AI-based charging strategy will make a significant contribution to the electrification of the transport sector.

“To reduce emissions and transition to a fossil-free society, it is important for people to be prepared to switch to electric vehicles. The possibility of fast charging, combined with an increased battery life, are important driving forces,” says Meng Yuan.

“And for the automotive industry, an almost 23 per cent increase in battery life can mean lower warranty costs, better resale value and more efficient use of critical raw materials,” says Changfu Zou.

*Sources:

European Commission/EAFO, 26 March 2026: Consumer Monitor 2025: EU drivers' view on electric cars
PwC, 10 september 2025: eReadiness 2025 – EVs charging ahead in a broadening market
McKinsey, 5 augusti 2024: How European consumers perceive electric vehicles

**Reinforcement learning is a method in machine learning in which an algorithm learns by interacting with an environment, and gradually improving its decisions based on the feedback it receives.

More about the research

The study Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-Ion Batteries was published in IEEE Transactions on Transportation Electrification. The authors are Changfu Zou, Chalmers University of Technology, Sweden, and Meng Yuan, Victoria University of Wellington, New Zealand.

This work was supported by the European Union’s Horizon Europe research and innovation programme through the Marie Skłodowska-Curie Actions Postdoctoral Fellowships, Swedish Research Council, and Swedish Foundation for International Cooperation in Research and Higher Education.

More about fast charging and battery life

An electric vehicle battery today has a life of approximately 8-15 years, depending on use and charging (1). The capacity of the battery gradually decreases with age. Volvo Cars’ electric vehicles, for example, come with a battery warranty of eight years or 160,000 kilometres (2).

In the study, the researchers measured the battery’s life in equivalent full cycles (EFC) – that is, how many full charge and discharge cycles the battery can withstand before the capacity drops to 80 per cent of its original value. At this limit, the battery still works, but is noticeably degraded and has a shorter range and reduced power (3).

Fast charging generally accounts for up to about 10-12 per cent of all charging, according to an analysis of 22,000 electric vehicles in the United States, Canada and Europe (4). Fast charging is more commonly used by long-distance commuters and those without access to home charging. The use of public charging, including fast charging, is also higher in regions where fewer people have the opportunity to charge at home, such as southern Europe and China (5).

1.How long does the battery last in an electric vehicle? Vehicle battery life, charging cycles and cost | go-e
2.https://www.volvocars.com/se/cars/electrification/battery/ (In Swedish)
3.https://www.twaice.com/battery-encyclopedia/end-of-life 
4.EV Battery Health Study: New Data on Fast Charging & Degradation | Geotab
5.Global Trends in Electric Vehicle Charging Demand and Infrastructure Development

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Smarter search for fuel-cell catalysts using machine learning



New computational workflow can efficiently optimize both catalytic activity and stability in platinum-based alloys




Institute of Science Tokyo

Efficiently exploring the material space for alloy catalysts 

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This work showcases how machine learning-based tools can be leveraged to identify promising alloy structures for fuel cells.

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Credit: Institute of Science Tokyo





A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo. Their approach addresses a longstanding challenge in catalyst design and consistently produces high-performing candidates from several material combinations.

Proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology that can generate electricity by combining hydrogen and oxygen into water. However, their performance depends heavily on a chemical step known as the oxygen reduction reaction (ORR), which requires an efficient catalyst to proceed at practical rates. Although platinum (Pt) remains the standard ORR catalyst in PEMFCs due to its remarkable electrochemical properties, its high cost and scarcity are barriers to large-scale adoption. As a result, researchers have turned to platinum-based alloys as less expensive alternatives that still maintain strong catalytic performance.

Designing these alloy-based catalysts, however, is far from straightforward. The number of possible atomic arrangements in alloy materials is enormous, making it impractical to test every candidate through experiments or computational methods like density functional theory. At the same time, catalysts must satisfy more than one requirement; they need to be highly reactive for ORR, but also stable under real operating conditions. Most machine learning-based approaches address these properties separately and thus lack the ability to propose atomic structures that fulfill both criteria simultaneously. How can we search for suitable alloy designs more efficiently?

In a recent study, Associate Professor Atsushi Ishikawa of the School of Environment and Society at Institute of Science Tokyo, Japan, together with graduate student Taishiro Wakamiya, developed a new strategy to address this challenge. Their work, published in the journal npj Computational Materials on April 14, 2026, introduces a method that combines atomistic simulations with generative artificial intelligence to design alloy catalysts for the ORR.

The proposed approach hinges on two key tools. The first is a neural network potential (NNP) model—a machine learning model trained on quantum mechanical calculations that can quickly estimate key material properties. The second is a generative model known as a conditional variational autoencoder (CVAE), which can propose new atomic structures based on desired properties. In this case, the model was trained to target both low overpotential (a measure of catalytic activity) and low alloy formation energy (a measure of stability).

The workflow operates as an iterative loop, with the NNP model evaluating the performance of proposed alloys and the CVAE refining them and feeding them back to the NNP stage. Over multiple iterations, this process gradually shifts the alloys toward better-performing arrangements. When applied to Pt–nickel alloys, the method generated structures that met overpotential and formation energy criteria simultaneously. Notably, the model also rediscovered known design principles by itself, such as how platinum-rich surface layers can enhance ORR activity.

The team further demonstrated the versatility of their workflow by extending it to multiple alloy systems, including Pt–titanium and Pt–yttrium. “The present work demonstrates that the combined use of atomistic calculations and the CVAE provides a general computational screening method that can produce new alloy surface structures satisfying both activity and stability criteria from limited initial data,” explains Ishikawa.

Beyond fuel-cell catalysts, the researchers believe their framework could have wide-ranging applications. “The newly developed workflow may be applicable to a broad range of materials challenges, including water electrolysis for hydrogen production, battery electrode materials, and catalysts for chemical processes,” concludes Ishikawa.

By enabling faster and more targeted exploration of complex material spaces, this work could help accelerate the development of sustainable energy technologies.

***

About Institute of Science Tokyo (Science Tokyo)

Institute of Science Tokyo (Science Tokyo) was established on October 1, 2024, following the merger between Tokyo Medical and Dental University (TMDU) and Tokyo Institute of Technology (Tokyo Tech), with the mission of “Advancing science and human wellbeing to create value for and with society.”

 

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AI matches human teachers: HKUST study finds a brief pre-lecture chat boosts students' brain synchrony and learning outcomes



This study provides the first neuroscientific evidence that scalable, AI-powered interactions can enhance online education



Hong Kong University of Science and Technology

Prof. LI Ping, Dean of the School of Humanities and Social Science and Chair Professor of Psychology and Cognitive Science at HKUST (right) and Dr. PENG Yingying, HKUST Postdoctoral Fellow and the paper’s first author (left). 

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Prof. LI Ping, Dean of the School of Humanities and Social Science and Chair Professor of Psychology and Cognitive Science at HKUST (right) and Dr. PENG Yingying, HKUST Postdoctoral Fellow and the paper’s first author (left). Prof. Li led the research team to find that a brief one-on-one pre-lecture conversation—whether with a human or an AI instructor—improves students’ neural synchrony and learning outcomes.

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Credit: HKUST





Millions of students worldwide have long relied on self-paced learning through pre-recorded video lectures, a model that forms the backbone of massive open online courses (MOOCs) and large-scale online education. Since the COVID-19 pandemic, dependence on video-based online learning has increased significantly, with learner participation rising sharply. However, this expansion has also been accompanied by a widespread decline in student engagement, undermining overall learning outcomes.

A research team at The Hong Kong University of Science and Technology (HKUST), led by Prof. LI Ping, Dean of the School of Humanities and Social Science and Chair Professor of Psychology and Cognitive Science, has found that a brief one-on-one pre-lecture conversation (8–10 minutes) — whether with a human or an AI instructor — improves students’ neural synchrony and learning outcomes.

Human and AI instructors achieve comparable learning outcomes, but through different neural pathways. Human interaction engages both cognitive scaffolding and strong social-emotional processing, mediated by gaze alignment, while AI interaction supports more top-down cognitive processing. The study shows that AI-led and human-led pre-class interactions yield statistically indistinguishable learning outcomes across recall, comprehension, and knowledge transfer.

The study, published in the leading international academic journal Neuron under the title "Scaffolding human and AI instruction: neural alignment and learning gains in online education," provides the first neuroscientific evidence that AI instructors can match their human counterparts in improving online learning quality.

How the Study Was Conducted
The research team recruited 57 university students and randomly assigned them to three groups:

•    Group 1 (No interaction): Watched a 14-minute video lecture with no prior student-teacher conversation.
•    Group 2 (Human instructor interaction): Engaged in a brief structured face-to-face conversation (8–10 minutes) with a human instructor beforehand.
•    Group 3 (AI instructor interaction): Participated in a similarly timed interaction with an AI instructor that closely resembled the human instructor in appearance and voice. The AI instructor, powered by GPT-4, incorporated speech recognition, content generation, text-to-speech synthesis, and real-time talking-head animation. Students were aware they were interacting with an AI.

All participants then watched the same 14-minute video lecture inside an MRI scanner, while their eye movements, brain responses, and learning outcomes were recorded.

The Results
The results were striking. Students who spoke with either the human or the AI instructor showed stronger synchronized neural activity in brain regions responsible for information processing, cognitive resource allocation, and socio-emotional responses during subsequent video learning. No significant differences were found between the two groups across recall, comprehension, and knowledge transfer.

By contrast, students who had no pre-lecture interaction did not exhibit these patterns, and their learning outcomes paled in comparison.

Prof. Li explained: "Both groups—students who interacted with a human instructor and those who interacted with the AI instructor—showed similar brain synchrony patterns during learning, and both outperformed students who had no interaction, especially on challenging comprehension questions. This tells us that social scaffolding, even when brief and AI mediated, fundamentally shapes how the brain prepares us to learn."

Different Pathways, Same Destination
While AI-led interaction produced comparable learning outcomes, the study also identified meaningful differences. Students who interacted with the AI instructor reported lower perceived social closeness and showed lower gaze alignment during the lecture compared with those in the human-interaction group. 

Brain imaging shows synchronized neural activity in information-processing, cognitive control and socio-emotional regions, while eye-tracking data demonstrates gaze alignment. Although students reported feeling less socially close to the AI instructor and showed lower gaze alignment, their learning outcomes were equally strong.

Both methods proved effective. These findings suggest that effective AI educational systems do not need to perfectly replicate human interaction. AI instruction can succeed by generating sufficient social-emotional resonance while leveraging its computational strengths in retrieving knowledge and delivering personalized learning.

A particularly novel contribution of the study is its demonstration of a multi-stage, reciprocal cascade linking eye movements, brain activity, and learning outcomes — which researchers call "eye-brain-behavior correspondence."

Students who had prior interaction with the human instructor showed significantly higher gaze alignment: their eyes moved in more coordinated directions and followed more similar patterns to one another and to the instructor's gaze. Further analyses revealed that this shared visual attention was associated with better learning, mediated by activity in the superior temporal sulcus (STS), a region involved in social perception and language comprehension. At the same time, alignment in the posterior cingulate cortex — a core hub of the default mode network — appeared to guide coordinated gaze behavior in a top-down fashion.

Dr. PENG Yingying, HKUST Postdoctoral Fellow and the paper's first author, said, "We found bidirectional pathways in the workings of the mind and the brain: when students fixate their attention on the same learning material, their brains align, and aligned brains further help keep their attention in sync. Together, these processes reinforce one another and support learning."

What This Means for Education
This research reveals multiple routes to improving students' online learning. Human interaction engages both cognitive scaffolding and strong social-emotional processing mediated by visual alignment, whereas AI interaction supports more top-down cognitive processing while still providing meaningful emotional support.

Prof. Li remarked, "This points toward a looming future for the social fabric of education, where even an AI instructor can pause, notice a student's subtle changes, and respond with care. These subtle aspects of human communication, if successfully realized in AI-empowered systems, may help cultivate what it means to feel seen, heard, and socially connected in a digital classroom." 

As AI continues to evolve at a rapid pace, gaining deeper insight into how AI shapes human cognition and brain function - and how the brain, in turn, adapts to AI - will be critical for the development of scalable and socially enriched learning environments. Such understanding will help ensure that AI enhances, rather than replaces, human‑centered, active learning.


Schematic of the student experiment. 

Schematic of the student experiment. A total of 57 university students were randomly assigned to three groups. During the experiment, the research team simultaneously recorded the students’ eye movements, brain responses, and learning outcomes.

Credit

HKUST