Background: Persisting symptoms affect about one-third of youth following concussion. Mental health history, distress, and coping style are key predictors of prolonged recovery. Early and scalable psychological interventions, such as mindfulness-based intervention (MBI) delivered via smartphones, may improve patients’ ability to regulate their emotions and neurophysiologically recover, reducing overall symptom burden. However, no digital therapeutic (DTx) trials in adolescents experiencing concussion exist. Objective: This study primarily aimed to assess the feasibility of conducting a larger randomized controlled trial (RCT) evaluating the effectiveness of a DTx-MBI in adolescents with a concussion compared with an attention-matched sham intervention. Methods: This was a Health Canada-regulated, parallel-group, blinded, single-crossover feasibility RCT. Adolescents aged 12 to <18 years presenting to a Pediatric Emergency Department or interdisciplinary concussion clinic within 7 days of a physician-diagnosed concussion were approached for participation from November 2022 to June 2024. After providing consent, participants were randomized (1:1), stratified by sex, to either the experimental group (DTx-MBI) or the control group (sham, attention-matched math puzzle game). The DTx-MBI was delivered via the AmDTx platform (Mobio Interactive Pte Ltd, Singapore) as a custom-designed 4-to-8-week program of 8 standardized modules for adolescents with concussion, including audio-recorded guided mindfulness exercises, goal setting, journaling, and psychoeducation. The control intervention, delivered through the same interface, excluded mindfulness content and instead featured the open-source game “2048”. Participants in both groups were encouraged to engage with the app for at least 10 minutes/day, at least 4 days/week. Feasibility criteria to support progression to a full-scale RCT included: eligibility rate >40% of those screened; recruitment rate >50% of eligible participants randomized; intervention credibility >70% scoring above the midpoint on the credibility and expectancy questionnaire (CEQ) at 1 week; retention >75% of randomized participants completing 4-week outcomes; and adherence >70% completing 10 minutes of intervention on at least 4 days/week for 4 weeks. Results: A total of 124 out of 195 (63.6%) screened youth met eligibility criteria. Of these, 99/124 (79.8%) consented and were randomized to either the DTx-MBI group (n=49, median [IQR] age=15.28 [13.66‐16.19] years, 30 [61.2%] female) or the Sham group (n=50, median [IQR] age=14.92 [13.32‐16.71] years, 30 [60.0%] female). Credibility was high, with 62/83 (74.7%) of participants scoring above the credibility midpoint (DTx-MBI: 75.0%; Sham: 74.4%). Retention was strong, with 89/99 (89.9%) of participants completing the 4-week outcomes (DTx-MBI: 89.8%; control: 90.0%). Overall adherence was moderate (54/99 [54.5%]; DTx-MBI: 59.2%; control: 50.0%), and a little higher among outcome assessment completers (53/89 [59.6%]; DTx-MBI: 63.6%; Sham: 55.6%). Feasibility indicators were similar between groups. Conclusions: This feasibility trial supports the implementation of a larger RCT, with modifications to enhance adherence, to rigorously evaluate the clinical efficacy of the DTx-MBI. By targeting modifiable psychological risk factors through a scalable digital platform, DTx-MBI could be a low-burden, cost-effective adjunct to pediatric concussion care. Trial Registration: ClinicalTrial.gov NCT05105802; International Registered Report Identifier (IRRID): RR2-10.2196/57226
WASHINGTON — The Supreme Court is leaving women’s access to a widely used abortion pill untouched until at least Thursday, while the justices consider whether to allow restrictions on the drug, mifepristone, to take effect.
Justice Samuel Alito’s order Monday allows women seeking abortions to continue obtaining the pill at pharmacies or through the mail, without an in-person visit to a doctor. It prevents restrictions on mifepristone imposed by a federal appeals court from taking effect for the time being.
A South American court upheld the steps taken by the Colombian government when it issued a compulsory license two years ago for an HIV medicine, a move that confirmed the legal framework for using such an approach in the future.
The Court of Justice of the Andean Community — a tribunal that settles trade, intellectual property, and labor disputes for Bolivia, Colombia, Ecuador, and Peru — also ruled that the Colombian government had properly justified the reasons for issuing a license and appropriately set an expiration date for its license.
“The court concluded that Colombia did not incur a breach of Andean regulations, since such measures are valid when there are reasons of public interest,” the health ministry said in a statement. “Colombia adequately complied with the obligation to determine the duration of the compulsory license” for the medicine, which is sold by ViiV Healthcare.
President Trump on Monday suggested the federal government could move to approve some forms of 7-OH, an opioid derived from the naturally occurring kratom plant.
“We’re looking very seriously at natural 7-OH and getting that approved,” Trump said.
It was not clear what Trump meant by “natural 7-OH.” Small amounts of the compound, shorthand for 7-hydroxymitragynine, occur naturally in kratom, which is increasingly used as a recreational drug and an unapproved pain treatment. While kratom is significantly less dangerous than potent synthetic opioids like fentanyl or prescription pain pills, it can still cause addiction and overdose.
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Did you know the U.S. Mint requires gold coins to be made with American-made gold, but instead it gets illegally mined gold that can be traced back to a Colombian drug cartel? Truly mind-blowing stuff from this New York Times investigation. Let me know what the health care angle is on that one: bob.herman@statnews.com.
It’s tough to make predictions, especially about the future
Two years ago, my old pal Rachel Cohrs Zhang and I reported how Medicare’s actuaries predicted the new Alzheimer’s drug Leqembi would cost the program $3.5 billion in 2025. It turns out that prediction was way off.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.
Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI. A California gubernatorial candidate said last week that he wants to tax corporate AI use and pay victims of “AI-driven layoffs.”
On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. I spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.
AI agents
One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.
“I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.
One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018. For example, an x-ray technician juggles 30 different tasks, from taking down patient histories to organizing archives of mammogram images. A worker can naturally switch between formats, databases, and working styles to do this, Acemoglu says, but how many individual tools or protocols would an AI require to do the same?
Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally. AI companies are in heated competition to prove that their AI agents can work independently for ever longer periods without making mistakes, sometimes exaggerating the results—but Acemoglu says many jobs will be spared from an AI takeover if agents can’t fluidly switch between tasks.
The new hiring spree
For years Big Tech has been offering staggering salaries to recruit AI researchers. But I asked Acemoglu about a different hiring spree I’ve noticed: AI companies are all building in-house economics teams.
OpenAI hired Ronnie Chatterji from Duke University in 2024 to be its chief economist and announced last year that Chatterji will work with Jason Furman—Harvard economist and former advisor to Barack Obama—to research AI and jobs. Anthropic has convened a group of 10 leading economists to do similar work. And just last week, Google DeepMind announced it had hired Alex Imas, an economist from the University of Chicago, to be its “director of AGI economics.”
Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).
“What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.
AI apps
I don’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents.
“Anybody could install these on their computer and get them to do the things that they want them to do,” he says. They spread accordingly.
“We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use.
But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI: anecdotes that college grads are finding the job market worse and worse, but no noticeable effect of AI on productivity, for example. “There’s a huge amount of uncertainty,” he says. And that’s the most telling thing about the AI economy right now: the certainty of the rhetoric alongside the uncertainty of everything else.
The tobacco industry chalked up another win on Friday with a new policy announced by the Food and Drug Administration that gives what one expert called a “get-out-of-jail-free card” to some manufacturers illegally selling e-cigarettes and nicotine pouches.
The FDA has a significant backlog of applications from the makers of vapes and nicotine pouches seeking authorization to sell their products. Some have gone ahead and put their products on sale anyway while awaiting word from the agency. In the new guidance, first reported by the New York Times, the agency said it will not prioritize cracking down on illegal sales under two conditions.
Despite years of digitization, organizations capture less than one-third of the value expected from digital investments, according to McKinsey research. That’s because most big companies begin with technological capabilities and bolt applications onto them, rather than starting with customer needs and working backward to technology solutions. Not prioritizing the customer can create fragmented solutions; disjointed customer experiences; and ultimately, failed transformations.
Organizations that achieve outsized results from AI flip the script. They adopt a “customer-back engineering” mindset, putting customers at the heart of technology transformation.
It’s a strategy in which products and services are developed with the customer experience first in mind, including the customers’ challenges, needs, and expectations. Product development teams then work backward in a nimble and agile way to find the steps necessary to design and build solutions that achieve the desired experience.
“When you get your engineers closer to customers, you get a lot more sideways innovation,” says Ashish Agrawal, managing vice president of business cards and payments tech at Capital One. “That leads to a multiplier effect, because engineers can approach a problem from a different dimension that can be unique to the sales or product perspective.”
The case for customer-centricity in engineering
Engineers are problem-solvers by nature, says Agrawal. When they hear about challenges customers are experiencing, or how they are using products and services in the real world, they can devise ways to efficiently address customer needs, since they are naturally closer to systems and data than many other teams across the company.
“Fostering a customer-centric culture has a motivational effect on engineers when they actually start seeing how the core changes they’re making, or the features they’re adding, are having a direct impact on the lives of customers,” says Agrawal.
It also takes discipline. Agrawal explains that Capital One has set a goal for every engineer in his organization to establish several touchpoints with customers throughout the year in different forms, including:
Digital empathy sessions to observe user journeys and identify where users hit friction
Embedded customer support for periods of time to deepen understanding of servicing needs
Engineering ride-alongs, in which engineers join customer success, sales, and support staff on calls or on-site visits
Hackathon competitions to build solutions around real customer problems
The AI opportunities with customer-centricity
“The biggest challenge engineers within large companies face is a lack of direct access to customers,” says Agrawal. “This can make it harder for technologists to work with customers to identify problems and innovate solutions.”
AI has accelerated the challenges as well as the opportunities. The lifecycle of launching products has become significantly faster. But the good news is that engineers are closer to the data that feeds into AI, so they can more rapidly apply AI-informed data techniques to solve customer problems.
Agrawal outlines a recent scenario: In the customer servicing space, conversations can instantly be summarized and give a customer agent context on the member’s original request and remaining action points. Agentic AI can also be enabled to ask pointed follow-up questions about the interaction that would otherwise take human agents time to read through the entire thread.
“A solution would have been a lot harder in an ecosystem without a lot of high-quality data,” says Agrawal. “But when you combine a rich data ecosystem with agentic tools, you move from incremental fixes to high-velocity transformation.”
By investing in AI data and tools and focusing on rapid experimentation, Agrawal says the cycle of deploying solutions can be accelerated. Teams learn that if they meet customer needs and iterate on a wider range of solutions much faster, then the entire innovation cycle speeds up.
For example, Capital One used customer insights to build a state-of-the-art, multi-agent AI framework called Chat Concierge to enhance the customer experience for car buyers and dealers. In a single conversation, Chat Concierge can perform tasks like comparing vehicles to help car buyers decide on the best choice and scheduling test drives or appointments with salespeople.
Agrawal explains that car buyers can engage with Chat Concierge directly through participating dealer websites. Dealers can access and can take over the chat through Navigator Platform. The AI assistant consists of multiple logical agents that work together to mimic human reasoning, allowing it to provide information and take action based on the customer’s requests.
The elements of an AI-first mindset
According to a recent MIT Technology Review Insights survey, 70% of leaders say their firm uses agentic AI to some degree. Roughly half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%), reducing cost and increasing efficiency (41%), and improving the customer experience (41%).
Looking into the future, achieving these outcomes looks even more likely. More than half of the banking executives surveyed say they expect to continue to improve fraud detection (75%), security (64%), and the customer experience (51%). Agentic AI use cases that show strong potential to transform the customer experience in financial services include responding to customer services requests, adjusting bill payments to align with regular paychecks, or extracting key terms and conditions from financial agreements.
Placing the customer at the center of a transformation requires an AI-first mindset. Companies must shift from simply augmenting an existing product to fundamentally reimagining the problem and the user’s needs through the lens of AI’s capabilities.
A few best practices that Agrawal recommends include:
Reimagine the core function of AI to solve a user’s problem: “The true value isn’t in chasing the AI hype; it’s in solving meaningful customer problems. By focusing on impact, we ensure that our innovation isn’t just fast; it’s transformative,” says Agrawal.
Start with high-quality, well-governed data as the foundation: “Data readiness and unified information across systems are the non-negotiable foundations of AI. A clean data layer is what orchestrates the agentic loop— enabling the perception, reasoning, and execution required to solve a customer’s problem before they even have to ask,” explains Agrawal.
Rebuild workflows with AI embedded from the start: “People treat models as black boxes, but agentic systems require tremendous rigor and oversight. Having a data ecosystem that is well-governed and responsible AI standards are essential pillars for building trust in these systems,” says Agrawal.
Build a cross-functional team involving data science, engineering, product, design, and other partners: Agrawal advises, “It’s important to be open and nimble to transforming how we work and create impact as AI becomes more integrated into workflows. It’s also important to take a ‘crawl, walk, run approach’ if you are new to AI, as opposed to simply jumping into it.”
In the end, achieving end-to-end transformation depends on empowering engineers and partner teams to start with customer needs and work backward to technology solutions, rather than starting with technological capabilities first and finding applications for them. When organizations make a customer-back approach second nature, they are able to not only reimagine the customer experience from the inside out, but to also place the customer front and center from the very start.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
By Mai El Shoush, Partnerships Campaign Manager, Stavros Niarchos Foundation (SNF) Global Center for Child and Adolescent Mental Health at the Child Mind Institute
In Conversation with Waves for Change
The world’s oceans have long been profound forces that shape coastlines, cultures, and scientific discovery. And today, through targeted programs, they also serve as therapeutic environments transforming youth mental health worldwide.
As global health systems continue to explore solutions that minimize resource constraints while addressing child and adolescent mental health demands, innovative approaches like surf therapy are demonstrating remarkable effectiveness as low-intensity initiatives. From the beaches of California to the coastal communities of South Africa, Australia, Hawai’i, the United Kingdom, and Senegal, these programs are creating accessible entry points for young people.
Wave for Change (W4C) — a South Africa-based organization and valued implementation partner of the Stavros Niarchos Foundation (SNF) Global Center at the Child Mind Institute — has developed an evidence-based Surf Therapy program for youth in underserved communities. We spoke with their chief development officer Paula Yarrow and senior grant manager Jill Sloan about the award-winning program. As a highly regarded Cape Town‑based NGO that uses surfing as a therapeutic tool to support youth mental health, W4C offers safe spaces, evidence‑based emotional regulation tools, community mentorship, and a pathway to resilience for young people growing up in challenging environments.
The partnership includes the identification of workforce gaps and training needs for frontline workers such as NGOs, to further expand evidence-based support and brief interventions through culturally appropriate, low-intensity psychological therapy approaches. The context-specific training materials are expected to be piloted later in the year in South Africa and are intended to improve access to quality mental health care for young people.
W4C launched Surf Therapy in 2009, which has since helped more than 10,000 adolescents experiencing high-stress environments gain valuable coping skills across its hubs in the Western and Eastern Cape as well as Cape Town. Participants learn how to build positive social networks and develop self-regulation skills to support healthy emotional and behavioral responses to stress, with coaches themselves aged between 18-25. The program creates a fun, culturally relevant environment through the Take 5 model — a framework W4C has designed to be adapted for a range of sports, arts, and cultural initiatives. The model has been utilized by several leading global organizations, including UNICEF.
Waves for Change also played a key role in the founding of the International Surf Therapy Organization (ISTO), connecting practitioners, clinicians, and researchers to advance science research, raise awareness, and support surf therapy.
Catching a wave at Surf Therapy – Image Nelson Rosier Coulhan
How does Waves for Change use evidence-based Surf Therapy and capacity building as a solution to fill the gap in youth mental health care?
Approximately 90 percent of the world’s adolescents live in low- and middle-income countries (LMICs). In the most underserved communities, adolescents may experience repeated exposure to violence, unmet basic needs, and limited access to safe spaces or trusted caregivers. Typically, there are very few mental health services that are accessible to such youth.
The more Adverse Childhood Experiences (ACEs) a child or adolescent has whilst growing up, the more likely they are to develop toxic stress — an ongoing stress state without respite. This can often lead to mental health conditions such as anxiety, depression, substance misuse, and cognitive impairment. This can also result in the development of physical health conditions such as heart disease as they grow into adulthood.
The main problem we’ve identified is that there aren’t enough trained workforces (e.g., sports coaches, youth facilitators) that are able to deliver simple, fun, structured play-based sessions with consistency at scale. Our work provides a response to this issue within the adolescent mental health promotion and illness prevention arena. Additionally, our initiatives significantly increase the number of individuals — coaches, teachers, mentors or others — who are already in contact with young adolescents and can provide them with mental health support to foster their immediate and longer-term mental health.
How has Waves for Change adapted the organization’s Surf Therapy program to develop the Take 5 model?
Waves for Change’s Take 5 training model has been incubated, tested, and rigorously evaluated within W4C’s award-winning Surf Therapy program. Take 5 distils the key components of our Surf Therapy program, providing coaches with the essential skills they need to build and sustain caring relationships with children. And it uses a simple teaching routine that creates consistently engaging, fun, structured programs for children and adolescents that suit their language, culture, and context.
Take 5 is low intensity and cost-effective — tailored for high-stress environments and the unique mental wellness needs of adolescents living in multidimensional poverty, conflict, or crisis.
How has partnering with young people to research and co-develop programs made the work more impactful?
In research studies we’ve conducted, adolescent participants (ages 10-16) reported experiencing between 6-8 adverse events every year, including violence and abuse. When asked what sorts of spaces they wanted to see at Waves for Change, the adolescents identified core components such as access to a safe space where they could have fun, be heard, and learn skills to cope. These components now form the bedrock of our Surf Therapy program. We initially worked with 9-12-year-olds and have since developed the follow-on programme for adolescents up to age 16 who have graduated the Surf Therapy programme. This is called Surf Club and is available to all Surf Therapy graduates.
Waves for Change also conducts pre- and post-intervention surveys with participants to monitor the impact of our work. Our coaches (ages 18-25) are at the frontline of delivering our services. A key role they play is to listen with care and respect to the adolescents’ concerns, and to share them with our Child Protection team for review and follow-up when needed.
Surf Therapy at Hout Bay – Photo credit Waves for Change
What makes your partnership with the SNF Global Center at the Child Mind Institute unique?
Working with the Child Mind Institute allows Waves for Change to collaborate with and learn from colleagues doing similar work in the adolescent mental health space across South Africa, the United Kingdom, and Brazil. The partnership offers an opportunity to learn about approaches that have been successful in other health systems. It has also allowed Waves for Change to share detailed information about the training and supervision protocol used to develop key competencies in the coach workforce that leads Surf Therapy in South Africa. This has helped the Child Mind Institute to develop a comprehensive guide for other similar workforces.
Can you expand on the importance of partnerships in strengthening youth mental health care and community empowerment?
Partnerships allow for the consolidation of skills and resources so that a greater impact can be achieved. For example, at Waves for Change, we work with over 70 referral partners every year to identify young adolescents who can benefit from our Surf Therapy program. We are also partnering with the Department of Cultural Affairs and Sport to use our Take 5 model to train MOD and YearBeyond coaches and mentors, who are already reaching large numbers of children and young adolescents through their work. And we’re contributing to building the broader ecosystem of mental health support for adolescents and children by training large national NGOs, government agencies, and humanitarian organizations with our Take 5 model.
How can non-profits further help foster strong peer networks and inclusive safe spaces?
Some of the key lessons we have learnt are the following:
In the field of youth mental health, make youth the leaders on program implementation
Provide youth with skills, opportunities, supervision, and support so that they can grow and develop further
Maintain a strong culture of protection, respect, and communication so that all participants feel safe, welcome, accepted, and heard
Read more about W4C’s Surf Therapy from Youth Liaison Officer, Azola Sibanda and Training Manager Jamie-Lee Davids
In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of the most tightly regulated functions in the enterprise is now among the most experimentally transformed.
What’s emerging is a layered shift in how work gets done. From variance commentary and fraud detection to contract review and close narrative drafting, AI is embedding itself across workflows, particularly where unstructured data once slowed down everything. Yet, as Glenn Hopper, head of AI and managing director at VAi Consulting, puts it, “the proliferation of AI happened kind of before governance and before a real plan came about.” That bottom-up adoption is forcing a recalibration at the top, where executives must now reconcile productivity gains with oversight, risk, and accountability.
Just as critical is reframing AI’s role. “AI as a means to an end, as opposed to AI being the end,” says Ranga Bodla, VP of industry and field marketing at Oracle NetSuite, underscores a growing consensus: the technology is most effective when it disappears into existing processes rather than outright replaces them. Embedded systems, seamless integrations, and tools like model context protocol (MCP) are accelerating this shift, making AI an ambient capability. Notably, ease of integration, not cost savings or new features, has become the strongest driver of adoption.
Still, the real constraint may be neither data nor technology, but people. “Talent is the actual root cause,” Hopper argues, pointing to a widening gap between domain expertise and AI fluency. Even as concerns about data security and model opacity persist, the more pressing risk may be misunderstanding the tools altogether or restricting them so tightly that employees look for workarounds beyond leadership control. “The auditability of it, I think, is critical,” Bodla notes.
Looking ahead, the trajectory is clear but variable. AI agents capable of executing complex, multi-step tasks are beginning to materialize, while expanding context windows and interoperable systems promise deeper, more persistent intelligence. But the real transformation may be a gradual shift toward systems that bolster judgement, automate routines, and allow finance teams to spend less time reconciling the past and more time shaping what comes next.
This webcast is produced in partnership with Oracle NetSuite.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
