Aurelia Butler

Experienced exam markers may struggle to spot answers generated by Artificial Intelligence (AI), researchers have found. 
The study was conducted at the University of Reading, UK, where university leaders are working to identify potential risks and opportunities of AI for research, teaching, learning, and assessment, with updated advice already issued to staff and students as a result of their findings. 
The researchers are calling for the global education sector to follow the example of Reading, and others who are also forming new policies and guidance and do more to address this emerging issue. 
In a rigorous blind test of a real-life university examinations system, published today (26 June) in PLOS ONE, ChatGPT generated exam answers, submitted for several undergraduate psychology modules, went undetected in 94% of cases and, on average, attained higher grades than real student submissions.   
This was the largest and most robust blind study of its kind, to date, to challenge human educators to detect AI-generated content.  
Associate Professor Peter Scarfe and Professor Etienne Roesch, who led the study at Reading’s School of Psychology and Clinical Language Sciences, said their findings should provide a “wakeup call” for educators across the world. A recent UNESCO survey of 450 schools and universities found that less than 10% had policies or guidance on the use of generative AI. 
Dr Scarfe said: “Many institutions have moved away from traditional exams to make assessment more inclusive. Our research shows it is of international importance to understand how AI will affect the integrity of educational assessments. 
“We won’t necessarily go back fully to hand-written exams, but global education sector will need to evolve in the face of AI.  

“It is testament to the candid academic rigour and commitment to research integrity at Reading that we have turned the microscope on ourselves to lead in this.” 
Professor Roesch said: “As a sector, we need to agree how we expect students to use and acknowledge the role of AI in their work. The same is true of the wider use of AI in other areas of life to prevent a crisis of trust across society. 
“Our study highlights the responsibility we have as producers and consumers of information. We need to double down on our commitment to academic and research integrity.” 
Professor Elizabeth McCrum, Pro-Vice-Chancellor for Education and Student Experience at the University of Reading, said: “It is clear that AI will have a transformative effect in many aspects of our lives, including how we teach students and assess their learning.  
“At Reading, we have undertaken a huge programme of work to consider all aspects of our teaching, including making greater use of technology to enhance student experience and boost graduate employability skills.  
“Solutions include moving away from outmoded ideas of assessment and towards those that are more aligned with the skills that students will need in the workplace, including making use of AI. Sharing alternative approaches that enable students to demonstrate their knowledge and skills, with colleagues across disciplines, is vitally important.  More

North Carolina State University researchers have developed a kirigami-inspired mechanical computer that uses a complex structure of rigid, interconnected polymer cubes to store, retrieve and erase data without relying on electronic components. The system also includes a reversible feature that allows users to control when data editing is permitted and when data should be locked in place.
Mechanical computers are computers that operate using mechanical components rather than electronic ones. Historically, these mechanical components have been things like levers or gears. However, mechanical computers can also be made using structures that are multistable, meaning they have more than one stable state — think of anything that can be folded into more than one stable position.
“We were interested in doing a couple things here,” says Jie Yin, co-corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at NC State. “First, we were interested in developing a stable, mechanical system for storing data.
“Second, this proof-of-concept work focused on binary computing functions with a cube being either pushed up or pushed down — it’s either a 1 or a 0. But we think there is potential here for more complex computing, with data being conveyed by how high a given cube has been pushed up. We’ve shown within this proof-of-concept system that cubes can have five or more different states. Theoretically, that means a given cube can convey not only a 1 or a 0, but also a 2, 3 or 4.”
The fundamental units of the new mechanical computer are 1-centimeter plastic cubes, grouped into functional units consisting of 64 interconnected cubes. The design of these units was inspired by kirigami, which is the art of cutting and folding paper. Yin and his collaborators have applied the principles of kirigami to three-dimensional materials that are cut into connected cubes.
When any of the cubes are pushed up or down, this changes the geometry — or architecture — of all of the connected cubes. This can be done by physically pushing up or down on one of the cubes, or by attaching a magnetic plate to the top of the functional unit and applying a magnetic field to remotely push it up or down. These 64-cube functional units can be grouped together into increasingly complex metastructures that allow for storing more data or for conducting more complex computations.
The cubes are connected by thin strips of elastic tape. To edit data, you have to change the configuration of functional units. That requires users to pull on the edges of the metastructure, which stretches the elastic tape and allows you to push cubes up or down. When you release the metastructure, the tape contracts, locking the cubes — and the data — in place.

“One potential application for this is that it allows for users to create three-dimensional, mechanical encryption or decryption,” says Yanbin Li, first author of the paper and a postdoctoral researcher at NC State. “For example, a specific configuration of functional units could serve as a 3D password.
“And the information density is quite good,” Li says. “Using a binary framework — where cubes are either up or down — a simple metastructure of 9 functional units has more than 362,000 possible configurations.”
“But we’re not necessarily limited to a binary context,” says Yin. “Each functional unit of 64 cubes can be configured into a wide variety of architectures, with cubes stacked up to five cubes high. This allows for the development of computing that goes well beyond binary code. Our proof-of-concept work here demonstrates the potential range of these architectures, but we have not developed code that capitalizes on those architectures. We’d be interested in collaborating with other researchers to explore the coding potential of these metastructures.”
“We’re also interested in exploring the potential utility of these metastructures to create haptic systems that display information in a three-dimensional context, rather than as pixels on a screen,” says Li.
The work was done with support from the National Science Foundation under grants 2005374, 2126072 and 2231419. More

A new joint study uncovers the remarkable potential of ultrafast lasers that could provide innovative solutions in 2D materials processing for many technology developers such as high-speed photodetectors, flexible electronics, biohybrids, and next-generation solar cells.
The manipulation of 2D materials, such as graphene and transition metal dichalcogenides (TMDs), is crucial for the advancement of next-generation electronic, photonic, quantum, and sensor technologies. These materials exhibit unique properties, including high electrical conductivity, mechanical flexibility, and tunable optical characteristics. Traditional processing methods, however, often lack the necessary precision and can introduce thermal damage. This is where ultrafast laser processing comes into play, offering unprecedented control over the material properties at the nanoscale.
Ultrafast lasers for modifying materials
Recent advancements in the field of light-matter interactions have paved the way for the transformative use of ultrafast laser processing in 2D materials. A new study by Aleksei Emelianov, Mika Pettersson from the University of Jyväskylä (Finland), and Ivan Bobrinetskiy from Biosense Institute (Serbia) explores the remarkable potential of ultrafast laser techniques in manipulating 2D layered materials and van der Waals (vdW) heterostructures toward novel applications.
“Ultrafast laser processing has emerged as a versatile technique for modifying materials and introducing novel functionalities. Unlike continuous-wave and long-pulsed optical methods, ultrafast lasers offer a solution for thermal heating issues. The nonlinear interactions between ultrafast laser pulses and the atomic lattice of 2D materials substantially influence their chemical and physical properties,” tells Postdoctoral Researcher Aleksei Emelianov from University of Jyväskylä.
A new tool for manipulating the properties of 2D materials
The researchers describe progress made over the past decade and primarily focus on the transformative role of ultrafast laser pulses in maskless green technology, enabling subtractive and additive processes that unveil ways for advanced devices. Utilizing the synergetic effect between the energy states within the atomic layers and ultrafast laser irradiation, it is feasible to achieve resolution down to several nanometers.

“Ultrafast light-matter interactions are being actively probed to study the unique optical properties of low-dimensional materials, says Aleksei Emelianov. In our research, we discovered that ultrafast laser processing has the potential to become a new technological tool for manipulating the properties of 2D materials,” he continues.
Reliable tools for advanced materials processing
Key advancements are discussed in functionalization, doping, atomic reconstruction, phase transformation, and 2D and 3D micro- and nanopatterning. The ability to manipulate 2D materials at such a fine scale opens up numerous possibilities for the development of novel photonic, electronic, and sensor applications. Potential applications include high-speed photodetectors, flexible electronics, biohybrids, and next-generation solar cells. The precision of ultrafast laser processing enables the creation of intricate micro- and nanoscale structures with potential utilization in telecommunications, medical diagnostics, and environmental monitoring.
“It is surprising how versatile ultrafast lasers are in tuning and modifying 2D materials. It is highly likely that lasers could provide innovative solutions in 2D materials processing for many technology developers,” adds Mika Pettersson.
This review represents a significant step forward in realizing the full potential of 2D and vdW materials, promising to drive new advancements in technology and industry.
“Still, there is a need for research on the physical basics of ultrafast interactions between lasers and 2D materials, says Ivan Bobrinetskiy. These should include not only interactions between the 2D material lattice and light but also involve the environment and substrates, which makes the physics of these processes more complicated but exciting at the same time,” he continues. More

A discovery by researchers at the University of British Columbia promises to improve care for patients with endometrial cancer, the most common gynecologic malignancy.
Using artificial intelligence (AI) to spot patterns across thousands of cancer cell images, the researchers have pinpointed a distinct subset of endometrial cancer that puts patients at much greater risk of recurrence and death, but would otherwise go unrecognized by traditional pathology and molecular diagnostics.
The findings, published today in Nature Communications, will help doctors identify patients with high-risk disease who could benefit from more comprehensive treatment.
“Endometrial cancer is a diverse disease, with some patients much more likely to see their cancer return than others,” said Dr. Jessica McAlpine, professor and Dr. Chew Wei Chair in Gynaecologic Oncology at UBC, and surgeon-scientist at BC Cancer and Vancouver General Hospital. “It’s so important that patients with high-risk disease are identified so we can intervene and hopefully prevent recurrence. This AI-based approach will help ensure no patient misses an opportunity for potentially lifesaving interventions.”
AI-powered precision medicine
The discovery builds on work by Dr. McAlpine and colleagues at B.C.’s Gynecologic Cancer Initiative — a multi-institutional collaboration between UBC, BC Cancer, Vancouver Coastal Health and BC Women’s Hospital — who in 2013 helped show that endometrial cancer can be classified into four subtypes based on the molecular characteristics of cancerous cells, with each posing a different level of risk to patients.
Dr. McAlpine and team then went on to develop an innovative molecular diagnostic tool, called ProMiSE, that can accurately discern between the subtypes. The tool is now used across B.C., parts of Canada and internationally to guide treatment decisions.

Yet, challenges remain. The most prevalent molecular subtype, encompassing approximately 50 per cent of all cases, is largely a catch-all category for endometrial cancers lacking discernable molecular features.
“There are patients in this very large category who have extremely good outcomes, and others whose cancer outcomes are highly unfavourable. But until now, we have lacked the tools to identify those at-risk so that we can offer them appropriate treatment,” said Dr. McAlpine.
Dr. McAlpine turned to long-time collaborator and machine learning expert Dr. Ali Bashashati, an assistant professor of biomedical engineering and pathology and laboratory medicine at UBC, to try and further segment the category using advanced AI methods.
Dr. Bashashati and his team developed a deep learning AI model that analyzes images of tissue samples collected from patients. The AI was trained to differentiate between different subtypes, and after analyzing over 2,300 cancer tissue images, pinpointed the new subgroup that exhibited markedly inferior survival rates.
“The power of AI is that it can objectively look at large sets of images and identify patterns that elude human pathologists,” said Dr. Bashashati. “It’s finding the needle in the haystack. It tells us this group of cancers with these characteristics are the worst offenders and represent a higher risk for patients.”
Bringing the discovery to patients
The team is now exploring how the AI tool could be integrated into clinical practice alongside traditional molecular and pathology diagnostics, thanks to a grant from the Terry Fox Research Institute.

“The two work hand-in-hand, with AI providing an additional layer on top of the testing we’re already doing,” said Dr. McAlpine.
One benefit of the AI-based approach is that it’s cost-efficient and easy to deploy across geographies. The AI analyzes images that are routinely gathered by pathologists and healthcare providers, even at smaller hospital sites in rural and remote communities, and shared when seeking second opinions on a diagnosis.
The combined use of molecular and AI-based analysis could allow many patients to remain in their home communities for less intensive surgery, while ensuring those who need treatment at a larger cancer centre can do so.
“What is really compelling to us is the opportunity for greater equity and access,” said Dr. Bashashati. “The AI doesn’t care if you’re in a large urban centre or rural community, it would just be available, so our hope is that this could really transform how we diagnose and treat endometrial cancer for patients everywhere.” More

Balance can be impacted by various factors, including diseases such as Parkinson’s disease, acute and chronic injuries to the nervous system, and the natural aging process. Accurately assessing balance in patients is important to identify and manage conditions that affect coordination and stability. Balance assessments also play a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across age groups and medical conditions.
However, traditional methods used to assess balance often suffer from subjectivity, are not comprehensive enough and cannot be administered remotely. Moreover, these assessments rely on expensive, specialized equipment which may not be readily accessible in all clinical settings and depend on the clinician’s expertise, which can lead to variability in results. More objective and comprehensive assessment tools in balance evaluation are greatly needed.
Using wearable sensors and advanced machine learning algorithms, researchers from Florida Atlantic University’s College of Engineering and Computer Science have developed a novel approach that addresses a crucial gap in balance assessment and sets a new benchmark in the application of wearable technology and machine learning in health care. The approach is a significant advance in objective balance assessment, especially for remote monitoring in home-based or nursing care settings, potentially transforming balance disorder management.
For the study, researchers used the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB), widely used in health care to assess a person’s ability to maintain balance under different sensory conditions. Wearable sensors were placed on study participants’ ankle, lumbar (lower back), sternum, wrist and arm.
Researchers collected comprehensive motion data from the participants under four different sensory conditions of m-CTSIB: balance performance with eyes open and closed on a stable surface; and eyes open and closed on a foam surface. Each test condition lasted about 11 seconds without breaks to simulate continuous balance challenges and streamline the assessment process. Researchers used inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for their analysis.
The data was then preprocessed and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, researchers applied Multiple Linear Regression, Support Vector Regression and XGBOOST algorithms. The wearable sensor data served as the input for their machine-learning models, and the corresponding m-CTSIB scores from Falltrak II, one of the leading tools in fall prevention, acted as the ground truth labels for model training and validation. Multiple machine-learning models were then developed to estimate m-CTSIB scores from the wearable sensor data. Researchers also explored the most effective sensor placements to optimize balance analysis.
Results of the study, published in the journal Frontiers in Digital Health, underscore this approach’s high accuracy and strong correlation with ground truth balance scores, suggesting the method is effective and reliable in estimating balance. Data from lumbar and dominant ankle sensors demonstrated the highest performance in balance score estimation, highlighting the importance of strategic sensor placement for capturing relevant balance adjustments and movements.

“Wearable sensors offer a practical and cost-effective solution for capturing detailed movement data, which is essential for balance analysis,” said Behnaz Ghoraani, Ph.D., senior author, an associate professor, FAU Department of Electrical Engineering and Computer Science, co-director of the FAU Center for SMART Health, and a fellow, FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). “Positioned on areas like the lower back and lower limbs, these sensors provide insights into 3D movement dynamics, essential for applications such as fall risk assessment in diverse populations. Coupled with the evolution of machine learning, these sensor-derived datasets transform into objective, quantifiable balance metrics, using an array of machine learning techniques.”
Results provide important insights into the significance of specific movements, feature selection and sensor placement in estimating balance. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both cross-validation methods and demonstrated a high correlation and a low mean absolute error, indicating consistent performance.
“Findings from this important research suggest that this novel method has the potential to revolutionize balance assessment practices, especially in situations where traditional methods are impractical or inaccessible,” said Stella Batalama, Ph.D., dean, FAU College of Engineering and Computer Science. “This approach is more accessible, cost-effective and capable of remote administration, which could have significant implications for health care, rehabilitation, sports science or other fields where balance assessment is important.”
The objectives of this study emerged from recognizing the need for advanced tools to capture the nuanced effects of different sensory inputs on balance.
“Traditional balance assessments often lack the granularity to dissect these influences comprehensively, leading to a gap in our understanding and management of balance impairments,” said Ghoraani. “Moreover, wearables support remote monitoring, enabling health care professionals to evaluate patients’ balance remotely, which is particularly useful in diverse health care scenarios.” More

An Oregon State University doctoral student and researchers at Adobe have created a new, cost-effective training technique for artificial intelligence systems that aims to make them less socially biased.
Eric Slyman of the OSU College of Engineering and the Adobe researchers call the novel method FairDeDup, an abbreviation for fair deduplication. Deduplication means removing redundant information from the data used to train AI systems, which lowers the high computing costs of the training.
Datasets gleaned from the internet often contain biases present in society, the researchers said. When those biases are codified in trained AI models, they can serve to perpetuate unfair ideas and behavior.
By understanding how deduplication affects bias prevalence, it’s possible to mitigate negative effects — such as an AI system automatically serving up only photos of white men if asked to show a picture of a CEO, doctor, etc. when the intended use case is to show diverse representations of people.
“We named it FairDeDup as a play on words for an earlier cost-effective method, SemDeDup, which we improved upon by incorporating fairness considerations,” Slyman said. “While prior work has shown that removing this redundant data can enable accurate AI training with fewer resources, we find that this process can also exacerbate the harmful social biases AI often learns.”
Slyman presented the FairDeDup algorithm last week in Seattle at the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
FairDeDup works by thinning the datasets of image captions collected from the web through a process known as pruning. Pruning refers to choosing a subset of the data that’s representative of the whole dataset, and if done in a content-aware manner, pruning allows for informed decisions about which parts of the data stay and which go.

“FairDeDup removes redundant data while incorporating controllable, human-defined dimensions of diversity to mitigate biases,” Slyman said. “Our approach enables AI training that is not only cost-effective and accurate but also more fair.”
In addition to occupation, race and gender, other biases perpetuated during training can include those related to age, geography and culture.
“By addressing biases during dataset pruning, we can create AI systems that are more socially just,” Slyman said. “Our work doesn’t force AI into following our own prescribed notion of fairness but rather creates a pathway to nudge AI to act fairly when contextualized within some settings and user bases in which it’s deployed. We let people define what is fair in their setting instead of the internet or other large-scale datasets deciding that.”
Collaborating with Slyman were Stefan Lee, an assistant professor in the OSU College of Engineering, and Scott Cohen and Kushal Kafle of Adobe. More

In a study published in Nature Communications June 19, a team of scientists led by Rice University’s Qimiao Si predicts the existence of flat electronic bands at the Fermi level, a finding that could enable new forms of quantum computing and electronic devices.
Quantum materials are governed by the rules of quantum mechanics, where electrons occupy unique energy states. These states form a ladder with the highest rung called the Fermi energy.
Electrons, being charged, repel each other and move in correlated ways. Si’s team found that electron interactions can create new flat bands at the Fermi level, enhancing their importance.
“Most flat bands are located far from the Fermi energy, which limits their impact on the material’s properties,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy at Rice.
Typically, a particle’s energy changes with its momentum. But in quantum mechanics, electrons can exhibit quantum interference, where their energy remains flat even when their momentum changes. These are known as flat bands.
“Flat electronic bands can enhance electron interactions, potentially creating new quantum phases and unusual low-energy behaviors,” Si said.
These bands are especially sought after in transition metal ions called d-electron materials with specific crystal lattices, where they often show unique properties, Si said.

The team’s findings suggest new ways to design these, which could inspire new applications for these materials in quantum bits, qubits and spintronics. Their research shows that electron interactions can link immobile and mobile electron states.
Using a theoretical model, the researchers demonstrated that these interactions can create a new type of Kondo effect, where immobile particles gain mobility by interacting with mobile electrons at the Fermi energy. The Kondo effect describes the scattering of conduction electrons in a metal due to magnetic impurities, resulting in a characteristic change in electrical resistivity with temperature.
“Quantum interference can enable the Kondo effect, allowing us to make significant progress,” said Lei Chen, a Ph.D. student at Rice.
A key attribute of the flat bands is their topology, Chen said. “The flat bands pinned to the Fermi energy provide a means to realize new quantum states of matter,” he said.
The team’s research reveals that this includes anyons and Weyl fermions, or massless quasiparticles and fermions that carry an electric charge. The researchers found that anyons are promising agents for qubits, and materials that host Weyl fermions may find applications in spin-based electronics.
The study also highlights the potential for these materials to be very responsive to external signals and capable of advanced quantum control. The results indicate that the flat bands could lead to strongly correlated topological semimetals at relatively low temperatures potentially operating at high temperatures or even room temperature.
“Our work provides the theoretical foundation for utilizing flat bands in strongly interacting settings to design and control novel quantum materials that operate beyond the realm of low temperatures,” Si said.
Contributors to this research include Fang Xie and Shouvik Sur, Rice postdoctoral associates of physics and astronomy; Haoyu Hu, Rice alumnus and postdoctoral fellow at Donostia International Physics Center; Silke Paschen, physicist at the Vienna University of Technology; and Jennifer Cano, theoretical physicist at Stony Brook University and the Flatiron Institute. More