P Ariya, S Khanchai, K Intawong, K Puritat - Computers & Education: X Reality, 2025
This study investigates how generative AI-based virtual assistants embedded within immersive virtual reality (VR) environments can enhance user engagement and cultural learning in virtual museums. Situated at the Wieng Yong House Museum in …
S Barua, CJ Paller, N Randhawa, A Rao - Frontiers in Artificial Intelligence
… outputs, appropriate patient communication about AI-derived insights, and … AI literacy into medical education at all levels, emphasizing complementary human-AI collaboration rather than algorithmic deference.Patient and Caregiver Literacy …
… ArtificialIntelligence (AI) technologies in various aspects of human life and career, second and foreign language (L2) educators and practitioners highlighted AI readiness and literacy … (EFL) students are ready to accept and implement AI tools …
Frontiers has announced that external fraud‑screening tools – Cactus Communications’ Paperpal Preflight, and Clear Skies’ Papermill Alarm and Oversight – have been integrated into its own Artificial Intelligence Review Assistant (AIRA) submission-screening system.
The expansion delivers what the companies describe as “an unprecedented, multilayered defence against organised research fraud, strengthening the reliability and integrity of every manuscript submitted to Frontiers”.
AIRA was launched in 2018, making Frontiers one of the early adopters of AI in submission checking. In 2022, Frontiers added its own papermill check to its comprehensive catalogue of AIRA checks, with the aim of tackling the industry-wide problem of manufactured manuscripts. The latest version, released in 2025, uses more than 15 data points and signals of potential manufactured manuscripts to be investigated and validated by a human expert.
Dr Elena Vicario, Head of Research Integrity at Frontiers, said: “Maintaining trust in the scholarly record demands constant innovation. By combining the unique strengths of Clear Skies and Cactus with our own AI capabilities, we are raising the bar for integrity screening and giving editors and reviewers the confidence that every submission has been rigorously vetted.”
Commenting on the importance of the partnership, Nikesh Gosalia, President, Global Academic and Publisher Relations at Cactus Communications, said: “This partnership with Frontiers reflects the confidence leading publishers have in our AI-driven solutions. Paperpal Preflight is a vital tool that supports editorial teams and existing homegrown solutions in identifying and addressing potential issues early in the publishing workflow.
“As one of the world’s largest and most impactful research publishers, Frontiers is taking an important step in strengthening research integrity, and we are proud to collaborate with them in this mission of safeguarding research.”
Adam Day, Founder and CEO of Clear Skies, added: “Clear Skies is thrilled to be working with the innovative team at Frontiers to integrate AIRA with Oversight. This integration makes our multi-award-winning services, including the Papermill Alarm, available across the Frontiers portfolio.
“Oversight is the first index of research integrity and recipient of the inaugural EPIC Award for integrity tools from the Society for Scholarly Publishing (SSP). As well as providing strategic Oversight to publishers, our detailed article reports support human Oversight of research integrity investigations on publications as well as journal submissions.”
To address this, Prime Minister Anthony Albanese has convened a productivity round table next month. This will coincide with the release of an interim report from the Productivity Commission, which is looking at five pillars of reform. One of these is the role of data and digital technologies, including artificial intelligence (AI).
But what do we really know about how AI impacts productivity?
What is productivity?
Put simply, productivity is how much output (goods and services) we can produce from a given amount of inputs (such as labour and raw materials). It matters because higher productivity typically translates to a higher standard of living. Productivity growth has accounted for 80% of Australia’s income growth over the past three decades.
Productivity can be thought of as individual, organisational or national.
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Your individual productivity is how efficiently you manage your time and resources to complete tasks. How many emails can you respond to in an hour? How many products can you check for defects in a day?
Organisational productivity is how well an organisation achieves its goals. For example, in a research organisation, how many top-quality research papers are produced?
National productivity is the economic efficiency of a nation, often measured as gross domestic product per hour worked. It is effectively an aggregate of the other forms. But it’s notoriously difficult to track how changes in individual or organisational productivity translate into national GDP per hour worked.
AI and individual productivity
The nascent research examining the relationship between AI and individual productivity shows mixed results.
A 2025 real-world study of AI and productivity involved 776 experienced product professionals at US multinational company Procter & Gamble. The study showed that individuals randomly assigned to use AI performed as well as a team of two without. A similar study in 2023 with 750 consultants from Boston Consulting Group found tasks were 18% faster with generative AI.
A 2023 paper reported on an early generative AI system in a Fortune 500 software company used by 5,200 customer support agents. The system showed a 14% increase in the number of issues resolved per hour. For less experienced agents, productivity increased by 35%.
But AI doesn’t always increase individual productivity.
A survey of 2,500 professionals found generative AI actually increased workload for 77% of workers. Some 47% said they didn’t know how to unlock productivity benefits. The study points to barriers such as the need to verify and/or correct AI outputs, the need for AI upskilling, and unreasonable expectations about what AI can do.
A recent CSIRO study examined the daily use of Microsoft 365 Copilot by 300 employees of a government organisation. While the majority self-reported productivity benefits, a sizeable minority (30%) did not. Even those workers who reported productivity improvements expected greater productivity benefits than were delivered.
Prime Minister Anthony Albanese has convened a productivity round table in August.Lukas Coch/AAP
AI and organisational productivity
It’s difficult, if not impossible, to attribute changes in an organisation’s productivity to the introduction of AI. Businesses are sensitive to many social and organisational factors, any one of which could be the reason for a change in productivity.
Nevertheless, the Organisation for Economic Co-operation and Development (OECD) has estimated the productivity benefits of traditional AI – that is, machine learning applied for an industry-specific task – to be zero to 11% at the organisational level.
A 2024 summary paper cites independent studies showing increases in organisational productivity from AI in Germany, Italy and Taiwan.
In contrast, a 2022 analysis of 300,000 US firms didn’t find a significant correlation between AI adoption and productivity, but did for other technologies such as robotics and cloud computing. Likely explanations are that AI hasn’t yet had an effect on many firms, or simply that it’s too hard to disentangle the impact of AI given it’s never applied in isolation.
AI productivity increases can also sometimes be masked by additional human labour needed to train or operate AI systems. Take Amazon’s Just Walk Out technology for shops.
More generally, think about the unknown number (but likely millions) of people paid to label data for AI models.
Amazon’s Just Walk Out technology intended to reduce labour as customer purchases would be fully automated.John G. Mabanglo/EPA
AI and national productivity
The picture at a national level is even murkier.
Clearly, AI hasn’t yet impacted national productivity. It can be argued that technology developments take time to affect national productivity, as companies need to figure out how to use the technology and put the necessary infrastructure and skills in place.
The common narrative around AI and productivity is that AI automates mundane tasks, making us faster at doing things and giving us more time for creative pursuits. This, however, is a naive view of how work happens.
Just because you can deal with your inbox more quickly doesn’t mean you’ll spend your afternoon on the beach. The more emails you fire off, the more you’ll receive back, and the never-ending cycle continues.
Imagine a world in which AI isn’t simply about speeding up tasks but proactively slows us down, to give us space to be more innovative, and more productive. That’s the real untapped opportunity with AI.
Sergio Toro, CEO of market intelligence group Aterio, shared research with The Epoch Times that showed there are 1,827 active data centers in the United States, with another 1,726 announced and 419 currently under construction.
Hundreds of the new centers are being planned or built in areas suffering from water scarcity or prolonged drought, prompting alarm from those working in sustainable urban development and environmentalism.
Based on the findings Toro shared, 1,082 data centers are being planned or built across 10 states that are experiencing some degree of water stress.
In states grappling with acute water stress, such as Nevada, Arizona, Texas, Utah, California, and Colorado, 437 data centers are planned or are currently under construction.
On average, non-hyperscale facilities use roughly 6.57 million gallons of water per year. By comparison, hyperscale centers—the kind required to power AI—use an estimated 200 million gallons per year.
“These facilities will require substantial energy and water for cooling. Conventional large-scale data centers can consume up to 5 million gallons of water per day, equivalent to the daily water use of a town with 20,000 to 50,000 residents.”
