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Decoding the Future of AI: From Disruption to Democratisation and Beyond
The global AI landscape has become a melting pot for innovation, with diverse thinking pushing the boundaries of what is possible. Its application extends beyond just technology, reshaping traditional business models and redefining how enterprises, governments, and societies operate. Advancements in model architectures, training techniques and the proliferation of open-source tools are lowering barriers to entry, enabling organisations of all sizes to develop competitive AI solutions with significantly fewer resources. As a result, the long-standing notion that AI leadership is reserved for entities with vast computational and financial resources is being challenged. This shift is also redrawing the global AI power balance, with a decentralised approach to AI where competition and collaboration coexist across different regions. As AI development becomes more distributed, investment strategies, enterprise innovation and global technological leadership are being reshaped. However, established AI powerhouses still wield significant leverage, driving an intense competitive cycle of rapid innovation. Amid this acceleration, it is critical to distinguish true technological breakthroughs from over-hyped narratives, adopting a measured, data-driven approach that balances innovation with demonstrable business value and robust ethical AI guardrails. Implications of the Evolving AI Landscape The democratisation of AI advancements, intensifying competitive pressures, the critical need for efficiency and sustainability, evolving geopolitical dynamics and the global race for skilled talent are all fuelling the development of AI worldwide. These dynamics are paving the way for a global balance of technological leadership. Democratisation of AI Potential The ability to develop competitive AI models at lower costs is not only broadening participation but also reshaping how AI is created, deployed and controlled. Open-source AI fosters innovation by enabling startups, researchers, and enterprises to collaborate and iterate rapidly, leading to diverse applications across industries. For example, xAI has made a significant move in the tech world by open sourcing its Grok AI chatbot model, potentially accelerating the democratisation of AI and fostering innovation. However, greater accessibility can also introduce challenges, including risks of misuse, uneven governance, and concerns over intellectual property. Additionally, as companies strategically leverage open-source AI to influence market dynamics, questions arise about the evolving balance between open innovation and proprietary control. Increased Competitive Pressure The AI industry is fuelled by a relentless drive to stay ahead of the competition, a pressure felt equally by Big Tech and startups. This is accelerating the release of new AI services, as companies strive to meet growing consumer demand for intelligent solutions. The risk of market disruption is significant; those who lag, face being eclipsed by more agile players. To survive and thrive, differentiation is paramount. Companies are laser-focused on developing unique AI capabilities and applications, creating a marketplace where constant adaptation and strategic innovation are crucial for success. Resource Optimisation and Sustainability The trend toward accessible AI necessitates resource optimisation, which means developing models with significantly less computational power, energy consumption and training data. This is not just about cost; it is crucial for sustainability. Training large AI models is energy-intensive; for example, training GPT-3, a 175-billion-parameter model, is believed to have consumed 1,287 MWh of electricity, equivalent to an average American household’s use over 120 years1. This drives innovation in model compression, transfer learning, and specialised hardware, like NVIDIA’s TensorRT. Small language models (SLMs) are a key development, offering comparable performance to larger models with drastically reduced resource needs. This makes them ideal for edge devices and resource-constrained environments, furthering both accessibility and sustainability across the AI lifecycle. Multifaceted Global AI Landscape The global AI landscape is increasingly defined by regional strengths and priorities. The US, with its strength in cloud infrastructure and software ecosystem, leads in “short-chain innovation”, rapidly translating AI research into commercial products. Meanwhile, China excels in “long-chain innovation”, deeply integrating AI into its extended manufacturing and industrial processes. Europe prioritises ethical, open and collaborative AI, while the APAC counterparts showcase a diversity of approaches. Underlying these regional variations is a shared trajectory for the evolution of AI, increasingly guided by principles of responsible AI: encompassing ethics, sustainability and open innovation, although the specific implementations and stages of advancement differ across regions. The Critical Talent Factor The evolving AI landscape necessitates a skilled workforce. Demand for professionals with expertise in AI and machine learning, data analysis, and related fields is rapidly increasing. This creates a talent gap that businesses must address through upskilling and reskilling initiatives. For example, Microsoft has launched an AI Skills Initiative, including free coursework and a grant program, to help individuals and organisations globally develop generative AI skills. What does this mean for today’s enterprise? New Business Horizons AI is no longer just an efficiency tool; it is a catalyst for entirely new business models. Enterprises that rethink their value propositions through AI-driven specialisation will unlock niche opportunities and reshape industries. In financial services, for example, AI is fundamentally transforming operations, risk management, customer interactions, and product development, leading to new levels of efficiency, personalisation and innovation. Navigating AI Integration and Adoption Integrating AI is not just about deployment; it is about ensuring enterprises are structurally prepared. Legacy IT architectures, fragmented data ecosystems and rigid workflows can hinder the full potential of AI. Organisations must invest in cloud scalability, intelligent automation and agile operating models to make AI a seamless extension of their business. Equally critical is ensuring workforce readiness, which involves strategically embedding AI literacy across all organisational functions and proactively reskilling talent to collaborate effectively with intelligent systems. Embracing Responsible AI Ethical considerations, data security and privacy are no longer afterthoughts but are becoming key differentiators. Organisations that embed responsible AI principles at the core of their strategy, rather than treating them as compliance check boxes, will build stronger customer trust and long-term resilience. This requires proactive bias mitigation, explainable AI frameworks, robust data governance and continuous monitoring for potential risks. Call to Action: Embracing a Balanced Approach The AI revolution is underway. It demands a balanced and proactive response. Enterprises must invest in their talent and reskilling initiatives to bridge the AI skills gap, modernise their infrastructure to support AI integration and scalability and embed responsible AI principles at the core of their strategy, ensuring fairness, transparency and accountability. Simultaneously, researchers must continue to push the boundaries of AI’s potential while prioritising energy efficiency and minimising environmental impact; policymakers must create frameworks that foster responsible innovation and sustainable growth. This necessitates combining innovative research with practical enterprise applications and a steadfast commitment to ethical and sustainable AI principles. The rapid evolution of AI presents both an imperative and an opportunity. The next chapter of AI will be defined by those who harness its potential responsibly while balancing technological progress with real-world impact. Resources Sudhir Pai: Executive Vice President and Chief Technology & Innovation Officer, Global Financial Services, Capgemini Professor Aleks Subic: Vice-Chancellor and Chief Executive, Aston University, Birmingham, UK Alexeis Garcia Perez: Professor of Digital Business & Society, Aston University, Birmingham, UK Gareth Wilson: Executive Vice President | Global Banking Industry Lead, Capgemini 1 https://www.datacenterdynamics.com/en/news/researchers-claim-they-can-cut-ai-training-energy-demands-by-75/?itm_source=Bibblio&itm_campaign=Bibblio-related&itm_medium=Bibblio-article-related
Virtual reality training tool helps nurses learn patient-centered care
University of Delaware computer science students have developed a digital interface as a two-way system that can help nurse trainees build their communication skills and learn to provide patient-centered care across a variety of situations. This virtual reality training tool would enable users to rehearse their bedside manner with expectant mothers before ever encountering a pregnant patient in person. The digital platform was created by students in Assistant Professor Leila Barmaki’s Human-Computer Interaction Laboratory, including senior Rana Tuncer, a computer science major, and sophomore Gael Lucero-Palacios. Lucero-Palacios said the training helps aspiring nurses practice more difficult and sensitive conversations they might have with patients. "Our tool is targeted to midwifery patients,” Lucero-Palacios said. “Learners can practice these conversations in a safe environment. It’s multilingual, too. We currently offer English or Turkish, and we’re working on a Spanish demo.” This type of judgement-free rehearsal environment has the potential to remove language barriers to care, with the ability to change the language capabilities of an avatar. For instance, the idea is that on one interface the “practitioner” could speak in one language, but it would be heard on the other interface in the patient’s native language. The patient avatar also can be customized to resemble different health stages and populations to provide learners a varied experience. Last December, Tuncer took the project on the road, piloting the virtual reality training program for faculty members in the Department of Midwifery at Ankara University in Ankara, Turkey. With technical support provided by Lucero-Palacios back in the United States, she was able to run a demo with the Ankara team, showcasing the UD-developed system’s interactive rehearsal environment’s capabilities. Last winter, University of Delaware senior Rana Tuncer (left), a computer science major, piloted the virtual reality training program for Neslihan Yilmaz Sezer (right), associate professor in the Department of Midwifery, Ankara University in Ankara, Turkey. Meanwhile, for Tuncer, Lucero-Palacios and the other students involved in the Human-Computer Interaction Laboratory, developing the VR training tool offered the opportunity to enhance their computer science, data science and artificial intelligence skills outside the classroom. “There were lots of interesting hurdles to overcome, like figuring out a lip-sync tool to match the words to the avatar’s mouth movements and figuring out server connections and how to get the languages to switch and translate properly,” Tuncer said. Lucero-Palacios was fascinated with developing text-to-speech capabilities and the ability to use technology to impact patient care. “If a nurse is well-equipped to answer difficult questions, then that helps the patient,” said Lucero-Palacios. The project is an ongoing research effort in the Barmaki lab that has involved many students. Significant developments occurred during the summer of 2024 when undergraduate researchers Tuncer and Lucero-Palacios contributed to the project through funding support from the National Science Foundation (NSF). However, work began before and continued well beyond that summer, involving many students over time. UD senior Gavin Caulfield provided foundational support to developing the program’s virtual environment and contributed to development of the text-to-speech/speech-to-text capabilities. CIS doctoral students Fahim Abrar and Behdokht Kiafar, along with Pinar Kullu, a postdoctoral fellow in the lab, used multimodal data collection and analytics to quantify the participant experience. “Interestingly, we found that participants showed more positive emotions in response to patient vulnerabilities and concerns,” said Kiafar. The work builds on previous research Barmaki, an assistant professor of computer and information sciences and resident faculty member in the Data Science Institute, completed with colleagues at New Jersey Institute of Technology and University of Central Florida in an NSF-funded project focused on empathy training for healthcare professionals using a virtual elderly patient. In the project, Barmaki employed machine learning tools to analyze a nursing trainee’s body language, gaze, verbal and nonverbal interactions to capture micro-expressions (facial expressions), and the presence or absence of empathy. “There is a huge gap in communication when it comes to caregivers working in geriatric care and maternal fetal medicine,” said Barmaki. “Both disciplines have high turnover and challenges with lack of caregiver attention to delicate situations.” UD senior Rana Tuncer (center) met with faculty members Neslihan Yilmaz Sezer (left) and Menekse Nazli Aker (right) of Ankara University in Ankara, Turkey, to educate them about the virtual reality training tool she and her student colleagues have developed to enhance patient-centered care skills for health care professionals. When these human-human interactions go wrong, for whatever reason, it can extend beyond a single patient visit. For instance, a pregnant woman who has a negative health care experience might decide not to continue routine pregnancy care. Beyond the project’s potential to improve health care professional field readiness, Barmaki was keen to note the benefits of real-world workforce development for her students. “Perceptions still exist that computer scientists work in isolation with their computers and rarely interact, but this is not true,” Barmaki said, pointing to the multi-faceted team members involved in this project. “Teamwork is very important. We have a nice culture in our lab where people feel comfortable asking their peers or more established students for help.” Barmaki also pointed to the potential application of these types of training environments, enabled by virtual reality, artificial intelligence and natural language processing, beyond health care. With the framework in place, she said, the idea could be adapted for other types of training involving human-human interaction, say in education, cybersecurity, even in emerging technology such as artificial intelligence (AI). Keeping people at the center of any design or application of this work is critical, particularly as uses for AI continue to expand. “As data scientists, we see things as spreadsheets and numbers in our work, but it’s important to remember that the data is coming from humans,” Barmaki said. While this project leverages computer vision and AI as a teaching tool for nursing assistants, Barmaki explained this type of system can also be used to train AI and to enable more responsible technologies down the road. She gave the example of using AI to study empathic interactions between humans and to recognize empathy. “This is the most important area where I’m trying to close the loop, in terms of responsible AI or more empathy-enabled AI,” Barmaki said. “There is a whole area of research exploring ways to make AI more natural, but we can’t work in a vacuum; we must consider the human interactions to design a good AI system.” Asked whether she has concerns about the future of artificial intelligence, Barmaki was positive. “I believe AI holds great promise for the future, and, right now, its benefits outweigh the risks,” she said.
Measuring how teachers' emotions can impact student learning
University of Delaware professor Leigh McLean has developed a new tool for measuring teachers’ emotional expressions and studying how these expressions affect their students’ attitudes toward learning. McLean uses this tool to gather new data showing emotional transmission between teachers and their students in fourth-grade classrooms. McLean and co-author Nathan Jones of Boston University share the results of their use of the tool in a new article in Contemporary Educational Psychology. They found that teachers displayed far more positive emotions than negative ones. But they also found that some teachers showed high levels of negative emotions. In these cases, teachers’ expressions of negative emotions were associated with reduced student enjoyment of learning and engagement. These findings add to a compelling body of research highlighting the importance of teachers’ and students’ emotional experiences within the context of teaching and learning. “Anyone who has been in a classroom knows that it is an inherently emotional environment, but we still don’t fully understand exactly how emotions, and especially the teachers’ emotions, work to either support or detract from students’ learning,” said McLean, who studies teachers’ emotions and well-being in the College of Education and Human Development’s School of Education (SOE) and Center Research in Education and Social Policy. “This new tool, and these findings, help us understand these processes more precisely and point to how we might provide emotion-centered classroom supports.” Measuring teacher and student emotions McLean and Jones collected survey data and video-recorded classroom observations from 65 fourth-grade teachers and 805 students in a Southwestern U.S. state. The surveys asked participants to report their emotions and emotion-related experiences — like feelings of enjoyment, worry or boredom — as well as their teaching and learning behaviors in mathematics and English language arts (ELA). Using the new observational tool they developed — the Teacher Affect Coding System — McLean and Jones also assessed teachers’ vocal tones, body posturing, body movements and facial expressions during classroom instruction and categorized outward displays of emotion as positive, negative or neutral. For example, higher-pitched or lilting vocal tones were categorized as positive, while noticeably harsh or sad vocal tones were categorized as negative. Overall, McLean and Jones found that teachers spent most of their instructional time displaying outward positive emotions. Interestingly though, they did not find any associations between these positive emotions and students’ content-related emotions or learning attitudes in ELA or math. “This lack of association might be because outward positivity is the relative ‘norm’ for elementary school teachers, and our data seem to support that,” McLean said. “That’s not to say that teachers’ positivity isn’t important, though. Decades of research has shown us that when teachers are warm, responsive and supportive, and when they foster positive relationships with their students, students do better in almost every way. It could be that positivity works best when done in tandem with other important teacher behaviors or routines, or it could be that it is more relevant for different student outcomes.” However, they did find that a small subset of teachers — about 10% — displayed notable amounts of negative emotions, with some showing negativity during as much as 80% of their instructional time. The students of these teachers reported reduced enjoyment and engagement in their ELA classes and reduced engagement in their math classes. “We think that these teachers are struggling with their real-time emotion regulation skills,” McLean said. “Any teacher, even a very positive one, will tell you that managing a classroom of students is challenging, and staying positive through the frustrating times takes a lot of emotional regulation. Emotion regulation is a particularly important skill for teachers because children inherently look to the social cues of adults in their immediate environment to gauge their level of safety and comfort. When a teacher is dysregulated, their students pick up on this in ways that can detract from learning.” Recommendations for supporting teacher well-being Given the findings of their study, McLean and Jones make several recommendations for teacher preparation and professional learning programs. As a first step, they recommend that teacher preparation and professional learning programs share information about how negative emotions and experiences are a normal part of the teaching experience. As McLean said, “It’s okay to be frustrated!” However, it is also important to be aware that repeated outward displays of negative emotion can impact students. McLean and Jones also suggest that these programs provide specific training to teachers on skills such as mindfulness and emotion regulation to help teachers manage negative emotions while they’re teaching. “Logically, these findings and recommendations make complete sense,” said Steve Amendum, professor and director of CEHD’s SOE, which offers a K-8 teacher education program. “After working with many, many teachers, I often see teachers' enthusiasm or dislike for a particular activity or content area transfer to their students.” McLean and Jones, however, emphasize that supporting teacher well-being can’t just be up to the teachers. Assistant principals, principals and other educational leaders should prioritize teacher wellness across the school and district. If teachers’ negative emotions in the classroom result in part from challenging working conditions or insufficient resources, educational leaders and policymakers should consider system-wide changes and supports to foster teacher well-being. To learn more about CEHD research in social and emotional development, visit its research page. To arrange an interview with McLean, connect with her directly by clicking on the contact button found on her ExpertFile profile page.
NASA Grant Funds Research Exploring Methods of Training Vision-Based Autonomous Systems
Conducting research at 5:30 a.m. may not be everybody’s first choice. But for Siddhartha Bhattacharyya and Ph.D. students Mohammed Abdul, Hafeez Khan and Parth Ganeriwala, it’s an essential part of the process for their latest endeavor. Bhattacharyya and his students are developing a more efficient framework for creating and evaluating image-based machine learning classification models for autonomous systems, such as those guiding cars and aircraft. That process involves creating new datasets with taxiway and runway images for vision-based autonomous aircraft. Just as humans need textbooks to fuel their learning, some machines are taught using thousands of photographs and images of the environment where their autonomous pupil will eventually operate. To help ensure their trained models can identify the correct course to take in a hyper-specific environment – with indicators such as centerline markings and side stripes on a runway at dawn – Bhattacharyya and his Ph.D. students chose a December morning to rise with the sun, board one of Florida Tech’s Piper Archer aircraft and photograph the views from above. Bhattacharyya, an associate professor of computer science and software engineering, is exploring the boundaries of operation of efficient and effective machine-learning approaches for vision-based classification in autonomous systems. In this case, these machine learning systems are trained on video or image data collected from environments including runways, taxiways or roadways. With this kind of model, it can take more than 100,000 images to help the algorithm learn and adapt to an environment. Today’s technology demands a pronounced human effort to manually label and classify each image. This can be an overwhelming process. To combat that, Bhattacharyya was awarded funding from NASA Langley Research Center to advance existing machine learning/computer vision-based systems, such as his lab’s “Advanced Line Identification and Notation Algorithm” (ALINA), by exploring automated labeling that would enable the model to learn and classify data itself – with humans intervening only as necessary. This measure would ease the overwhelming human demand, he said. ALINA is an annotation framework that Hafeez and Parth developed under Bhattacharyya’s guidance to detect and label data for algorithms, such as taxiway line markings for autonomous aircraft. Bhattacharyya will use NASA’s funding to explore transfer learning-based approaches, led by Parth, and few-shot learning (FSL) approaches, led by Hafeez. The researchers are collecting images via GoPro of runways and taxiways at airports in Melbourne and Grant-Valkaria with help from Florida Tech’s College of Aeronautics. Bhattacharyya’s students will take the data they collect from the airports and train their models to, in theory, drive an aircraft autonomously. They are working to collect diverse images of the runways – those of different angles and weather and lighting conditions – so that the model learns to identify patterns that determine the most accurate course regardless of environment or conditions. That includes the daybreak images captured on that December flight. “We went at sunrise, where there is glare on the camera. Now we need to see if it’s able to identify the lines at night because that’s when there are lights embedded on the taxiways,” Bhattacharyya said. “We want to collect diverse datasets and see what methods work, what methods fail and what else do we need to do to build that reliable software.” Transfer learning is a machine learning technique in which a model trained to do one task can generalize information and reuse it to complete another task. For example, a model trained to drive autonomous cars could transfer its intelligence to drive autonomous aircraft. This transfer helps explore generalization of knowledge. It also improves efficiency by eliminating the need for new models that complete different but related tasks. For example, a car trained to operate autonomously in California could retain generalized knowledge when learning how to drive in Florida, despite different landscapes. “This model already knows lines and lanes, and we are going to train it on certain other types of lines hoping it generalizes and keeps the previous knowledge,” Bhattacharyya explained. “That model could do both tasks, as humans do.” FSL is a technique that teaches a model to generalize information with just a few data samples instead of the massive datasets used in transfer learning. With this type of training, a model should be able to identify an environment based on just four or five images. “That would help us reduce the time and cost of data collection as well as time spent labeling the data that we typically go through for several thousands of datasets,” Bhattacharyya said. Learning when results may or may not be reliable is a key part of this research. Bhattacharyya said identifying degradation in the autonomous system’s performance will help guide the development of online monitors that can catch errors and alert human operators to take corrective action. Ultimately, he hopes that this research can help create a future where we utilize the benefits of machine learning without fear of it failing before notifying the operator, driver or user. “That’s the end goal,” Bhattacharyya said. “It motivates me to learn how the context relates to assumptions associated with these images, that helps in understanding when the autonomous system is not confident in its decision, thus sending an alert to the user. This could apply to a future generation of autonomous systems where we don’t need to fear the unknown – when the system could fail.” Siddhartha (Sid) Bhattacharyya’s primary area of research expertise/interest is in model based engineering, formal methods, machine learning engineering, and explainable AI applied to intelligent autonomous systems, cyber security, human factors, healthcare, explainable AI, and avionics. His research lab ASSIST (Assured Safety, Security, and Intent with Systematic Tactics) focuses on the research in the design of innovative formal methods to assure performance of intelligent systems, machine learning engineering to characterize intelligent systems for safety and model based engineering to analyze system behavior. Siddhartha Bhattacharyya is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.
For autonomous machines to flourish, scalability is everything
The past decade has seen remarkable advancements in robotics and AI technologies, ushering in the era of autonomous machines. While the rise of these machines promises to revolutionize our economy, the reality has fallen short of expectations. That’s not for a lack of intensive investments in research in development, says Yuhao Zhu, an associate professor of computer science at the University of Rochester. The reason we’re not seeing more service robots, autonomous drones, and self-driving vehicles, Zhu says, is that autonomation development is currently scaling with the size of engineering teams rather than the amount of relevant data and computational resources. This limitation prevents the autonomy industry from fully leveraging economies of scale, Zhu says, particularly the exponentially decreasing cost of computing power and the explosion of available data. Zhu recently co-authored a report on the quest for economies of scale in autonomation in Communications of the ACM and is part of an international team of computer scientists focused on making autonomous machines more reliable and less costly. He can be reached by email at yzhu@rochester.edu.
New true crime podcast Writing Wrongs launches with a chilling case of miscarriage of justice
True crime enthusiasts and forensic linguistics fans have a gripping new podcast to add to their playlists. Writing Wrongs, an original podcast from the Aston Institute for Forensic Linguistics (AIFL) at Aston University, provides a deep dive into how forensic language analysis plays a crucial role in solving crimes and improving the delivery of justice. Hosts Professor Tim Grant and Dr Nicci MacLeod, leading experts in forensic linguistics, explore how police interviews and linguistic evidence played a key role in one of Britain’s most infamous miscarriages of justice. Throughout the series, they’ll explore real-life cases where forensic linguistics has played a pivotal role in solving crimes, joined by expert guests who reveal the fascinating - and sometimes chilling - ways language can expose the truth. The first episode, Timothy Evans: A Case for Forensic Linguistics, launched on 7 March 2025, 75 years after Timothy Evans’ wrongful conviction and subsequent execution (9 March 1950). The Timothy Evans case was instrumental in the UK’s decision to abolish the death penalty, raising critical questions about police interviewing techniques, false confessions and linguistic analysis in legal proceedings. In 1950, Evans was convicted and later hanged for the murder of his baby daughter, Geraldine, while his wife, Beryl Evans, was also presumed to be his victim. However, three years later, his neighbour at 10 Rillington Place, London, John ‘Reg’ Christie, a former police officer, was exposed as a serial killer, responsible for at least eight murders – and almost certainly including Geraldine and Beryl Evans. Despite evidence casting doubt on Evans’ guilt, he was executed before Christie’s crimes came to light. This case was instrumental in the early development of forensic linguistics, as experts later analysed Evans’ police confessions to expose inconsistencies. Tim Grant, professor of forensic linguistics at Aston University, said: “We are delighted to launch Writing Wrongs with this episode focussing on the wrongful conviction and execution of Timothy Evans. This episode clearly shows how language analysis can provide evidence to help resolve one of the most controversial cases in British legal history. “In other episodes we show how contemporary forensic linguists are making contributions to the delivery of justice in cases of murder, rape and terrorism. In each case we discuss with a linguist how they assisted, and demonstrate how providing linguistic evidence to the courts can exonerate or incriminate and change the outcome of cases.” Dr Nicci MacLeod, deputy director of the Aston Institute for Forensic Linguistics, said: “This is the origin story for forensic linguistics, a phrase first coined by Jan Svartvik in his 1968 publication analysing the Evans statements. “Svartvik was able to show that there were clear differences in the language style of the incriminating sections of Evans’ ‘confession’, and other parts of the statements he gave to police. “One feature Svartvik focused on was the use of the word ‘then’ positioned after the subject of a clause, as in “I then came upstairs”, as opposed to what we might consider the more usual ordering of “then I came upstairs”. This is a feature of ‘policespeak’, and was also identified in the infamous Derek Bentley confession by Malcolm Coulthard some years later.” The first three episodes of the eight-part series of Writing Wrongs are available now on Spotify, Apple Podcasts and all major podcast platforms. They include a bonus episode with the author, Kate Summerscale ('The Suspicions of Mr Whicher' and 'The Queen of Whale Cay'), about her latest book ‘The Peepshow: The Murders at 10 Rillington Place’ and an episode featuring Dr Isobelle Clarke, which shows how a series of forensic authorship analyses assisted in the investigation and conviction of a terrorist who planted a pipe bomb in Edinburgh in 2018. Listeners are encouraged to follow, share and engage with the hosts by submitting their forensic linguistics questions. Whether it’s about the cases covered or broader issues in forensic linguistics, Professor Grant and Dr MacLeod welcome enquiries from listeners. Future episodes will be released on the first Friday of the month with episode four, Foreygensic Lingeyguistics: Cracking the Killer’s Code, dropping on 4 April 2025.
Hundreds of nurses and their colleagues at ChristianaCare gathered in a conference room at Christiana Hospital and listened through a livestream across the organization’s campuses and practices for an announcement they’ve been anticipating for many months. “For your commitment to nursing excellence and quality care, we are thrilled to recognize ChristianaCare with its fourth consecutive Magnet designation,” said David Marshall, JD, DNP, RN, chair of the American Nurses Credentialing Center’s Commission on Magnet Recognition. “This accomplishment is a powerful testament to your dedication to the nurses who practice there, the entire health care team, and — most importantly — the patients you serve.” Shouts erupted, balloons and streamers floated up and, in the happy commotion, there was even a little cowbell. As the only four-time Magnet-designated health care organization in Delaware, ChristianaCare has achieved this global recognition — the highest honor in nursing practice — for continued dedication to excellence and innovation, high-quality patient care and experience, nurse engagement and work culture. “Magnet designation recognizes ChristianaCare nurses are simply the best!” said ChristianaCare President and CEO Janice E. Nevin, M.D., MPH. “A fourth Magnet designation is an incredible achievement and reflects the vital importance and commitment of our nurses as we serve together with love and excellence.” ChristianaCare has more than 3,000 nurses, and they make up the largest segment of ChristianaCare’s workforce. ChristianaCare is the largest nonprofit organization and private employer in the state of Delaware. This most recent designation for ChristianaCare includes Christiana Hospital, Wilmington Hospital, ChristianaCare HomeHealth and Community Care Services, through early 2029. What it means to be Magnet “Our fourth consecutive Magnet designation means that our nurses and all of our caregiver colleagues have upheld the ANCC’s very high standards in patient care since our first recognition in 2010,” said ChristianaCare Chief Nurse Executive Danielle Weber, DNP, RN. “That is a long time to bring your ‘A’ game every day — through 15 years of change, including a pandemic — and to sustain growth in professional practice, innovation and culture. Magnet recognition raises the bar for patient care and inspires every member of our team to achieve excellence every day.” The Magnet Recognition Program — administered by the American Nurses Credentialing Center, the largest and most prominent nurses credentialing organization in the world — identifies health care organizations that provide the very best in nursing care, exceptional nurse engagement and professionalism in nursing practice. The Magnet Recognition Program serves as the gold standard for nursing excellence and provides consumers with the ultimate benchmark for measuring quality of care. The ANCC Magnet Recognition Program® has conferred Magnet status to less than 10% of hospitals and health systems in the United States. There are 621 Magnet-designated health organizations internationally. ChristianaCare was the first in Delaware to achieve Magnet designation, in 2010. For nurses, Magnet Recognition means education and development through every career stage, which leads to greater autonomy at the bedside. For patients, it means the very best care, delivered by nurses who are supported to be the very best that they can be. While Magnet is a nursing-led initiative, the designation reflects the work of caregivers across the organization. Magnet redesignation itself is a rigorous process. Health care organizations must reapply for Magnet status every four years and demonstrate adherence to the Magnet concepts for nursing excellence and engagement and measurable improvements in patient care and quality. The ANCC commended ChristianaCare on these exemplars: Advocacy for and acquisition of organizational resources specific to nurses’ well-being. particularly through the Nursing Integrative Care Program. An innovative strategy to address the shortage of certified registered nurse anesthetists in Delaware through a partnership program between ChristianaCare and Wilmington University to launch the state’s first Nurse Anesthesiology program. Outstanding nursing research engagement and growth of the nursing research enterprise especially through the Nursing Research Fellowship in Robotics and Innovation.
Professor Ian Maidment has received a National Institute for Health and Care Research (NIHR) Senior Investigator Award The award recognises his outstanding leadership contributions to the work of the NIHR and his excellent track record of securing NIHR funding Professor Maidment is the first academic at Aston University to receive the honour. Professor Ian Maidment at Aston Pharmacy School has received a prestigious Senior Investigator Award from the National Institute for Health and Care Research (NIHR). The NIHR gives the award to researchers in recognition of outstanding leadership contributions to the work of the NIHR and an excellent track record of securing NIHR funding. As a senior investigator, Professor Maidment will act as an ambassador for NIHR, and help to guide strategy and tackle challenges in the health and social care landscape. He will join the NIHR College of around 200 senior investigators. Professor Maidment is the first academic at Aston University to receive the award and one of few pharmacists in the UK to receive such an award. Professor Maidment joined Aston University in 2012 as a senior lecturer, which marked his first step into academia after more than 20 years working in the NHS, both as a pharmacist and leading R&D. During his time in the NHS, he published 40 papers in peer-reviewed journals. These formed the basis of a PhD by previous publication, and Professor Maidment was the first person to obtain a PhD at Aston University by this route. He was promoted to reader in 2018 and a full chair in 2022. Professor Maidment specialises in the health care of older people and those with mental health conditions, and the use of medication to treat them. This includes projects investigating the long-standing and international healthcare priority of managing anti-psychotic weight gain. From this research project, guidance will be developed both for patients and practitioners. His research with older people has identified the need to focus on reducing medication burden and investigating the link between some medications and dementia. He also studies how to best use the expertise of community pharmacy to improve outcomes, for example in COVID vaccination and more recently how to make independent prescribing by community pharmacy work better; the importance of this issue was identified by UK Prime Minister Keir Starmer. The award also recognises Professor Maidment’s strong links with the NIHR and critically his continued role in supporting its work. This includes mentoring other researchers, leadership and contributing to the development of the NIHR. Professor Maidment said: “Optimising medication in the real world is a key research priority; about half of all people struggle with adherence to medication. Much of my research has been focused on bringing the patient voice to key research questions. If we can fully understand the patient and family carer view, then we can start to get the medication right.” Professor Anthony Hilton, Aston University pro-vice-chancellor and executive dean of the College of Health and Life Sciences, said: “Professor Ian Maidment’s NIHR Senior Investigator Award is a well-deserved recognition of his exceptional research in medication safety and the care of older adults and people with severe mental illness, such as schizophrenia. His work has not only advanced academic understanding but has also shaped real-world healthcare practices, improving outcomes for patients. “This achievement reflects his dedication, expertise and commitment to impactful research and his outstanding leadership contributions to the work of the NIHR. At Aston University, we are delighted to celebrate Ian’s success and the significant contribution he continues to make to the field.”
Digital platforms have emerged as powerful tools for people impacted by the Russo-Ukrainian War. One professor at the University of Delaware has, for over two years, provided reading resources specifically for the children whose lives have been forever changed by this conflict. Roberta Michnick Golinkoff, the Unidel H. Rodney Sharp Chair and Professor at UD's College of Education and Human Development, has developed a website with free interactive e-books, games and other resources to Ukrainian children. A nationally known expert in childhood literacy, Golinkoff worked together with developers to stock the site, Stories with Clever Hedgehog, with materials in both Ukrainian and English. The multilingual platforms allows displaced families all over the world to engage in shared reading with their children, facilitate early literacy development and promote well-being during a time of stress. In addition to enhancing learning experiences, digital platforms provide an essential sense of community and connectivity for students isolated by conflict. Golinkoff, who has appeared in numerous national outlets including NPR, ABC News and The Conversation, is available for interviews on the site as well as literacy in general. Just click her profile to get in touch.
University of Delaware experts exploring Black and brown history and topics all year long
While Black History Month officially ended on Friday, the topic is one that is always top of mind for many professors and experts here at the University of Delaware. Below are a small list of these experts and the areas they explore throughout the year. Click on their profiles or email mediarelations@udel.edu to connect. Roderick Carey, associate professor in the Department of Human Development and Family Sciences, can discuss the importance of gender and race diversity in teaching. Ann Aviles and Ohiro Oni-Eseleh, both professors in the College of Education and Human Development, can share resources for displaced families and guidance for parents, educators and other community members who want to support them. Yasser Payne, professor of sociology, examines notions of resilience, structural violence and gun violence with Black Americans.
