The Pentagon is angling to introduce artificial intelligence across its workforce within nine months following the reorganization of its key AI office.

Emil Michael, under secretary of defense for research and engineering at the Department of Defense, talked about the agency’s plans for introducing AI to its operations as it continues its modernization journey. 

“We want to have an AI capability on every desktop — 3 million desktops — in six or nine months,” Michael said during a Politico event on Tuesday. “We want to have it focus on applications for corporate use cases like efficiency, like you would use in your own company … for intelligence and for warfighting.”

This announcement follows the recent shakeups and restructuring of the Pentagon’s main artificial intelligence office. A senior defense official said the Chief Digital and Artificial Intelligence Office will serve as a new addition to the department’s research portfolio.

Michael also said he is “excited” about the restructured CDAO, adding that its new role will pivot to a focus on research that is similar to the Defense Advanced Research Projects Agency and Missile Defense Agency. This change is intended to enhance research and engineering priorities that will help advance AI for use by the armed forces and not take agency focus away from AI deployment and innovation.

“To add AI to that portfolio means it gets a lot of muscle to it,” he said. “So I’m spending at least a third of my time –– maybe half –– rethinking how the AI deployment strategy is going to be at DOD.”

Applications coming out of the CDAO and related agencies will then be tailored to corporate workloads, such as efficiency-related work, according to Michael, along with intelligence and warfighting needs.

The Pentagon first stood up the CDAO and brought on its first chief digital and artificial intelligence officer in 2022 to advance the agency’s AI efforts.

The restructuring of the CDAO this year garnered attention due to its pivotal role in investigating the defense applications of emerging technologies and defense acquisition activities. Job cuts within the office added another layer of concern, with reports estimating a 60% reduction in the CDAO workforce.

Artificial intelligence (AI) has become one of the fastest-growing technologies in the world today. In many industries, individuals and organizations are racing to better understand AI and incorporate it into their work. Surgery is no exception, and that is why Clinical Congress 2025 has made AI one of the six themes of its Opening Day Thematic Sessions.

The first full day of the conference, Sunday, October 5, will include two back-to-back Panel Sessions on AI. The first session, “Using ChatGPT and AI for Beginners” (PS104), offers a foundation for surgeons not yet well versed in AI. The second, “AI: Who Is In Control?” (PS 110), will offer insights into the potential upsides and drawbacks of AI use, as well as its limitations and possible future applications, so that surgeons can involve this technology in their clinical care safely and effectively.

“AI: Who Is In Control?” will be moderated by Anna N. Miller, MD, FACS, an orthopaedic surgeon at Dartmouth Hitchcock Medical Center in Lebanon, New Hampshire, and Gabriel Brat, MD, MPH, MSc, FACS, a trauma and acute care surgeon at Beth Israel Deaconess Medical Center and an assistant professor at Harvard Medical School, both in Boston, Massachusetts.

In an interview, Dr. Brat shared his view that the use of AI is not likely to replace surgeons or decrease the need for surgical skills or decision-making. “It’s not an algorithm that’s going to be throwing the stitch. It’s still the surgeon.”

Nonetheless, he said that the starting presumption of the session is that AI is likely to be highly transformative to the profession over time.  

“Once it has significant uptake, it’ll really change elements of how we think about surgery,” he said, including creating meaningful opportunities for improvements.

The key question of the session, therefore, is not whether to engage with AI, but to do so in ways that ensure the best outcomes: “We as surgeons need to have a role in defining how to do so safely and effectively. Otherwise, people will start to use these tools, and we will be swept along with a movement as opposed to controlling it.”

To that end, Dr. Brat explained that the session will offer “a really strong translational focus by people who have been in the trenches working with these technologies.” He and Dr. Miller have specifically chosen an “all-star panel” designed to represent academia, healthcare associations, and industry. 

The panelists include Rachael A. Callcut, MD, MSPH, FACS, who is the division chief of trauma, acute care surgery and surgical critical care as well as associate dean of data science and innovation at the University of California-Davis Health in Sacramento, California. She will share the perspective on AI from academic surgery.

Genevieve Melton-Meaux, MD, PhD, FACS, FACMI, the inaugural ACS Chief Health Informatics Officer, will present on AI usage in healthcare associations. She also is a colorectal surgeon and the senior associate dean for health informatics and data science at the University of Minnesota and chief health informatics and AI officer for Fairview Health Services, both in Minneapolis.

Finally, Khan Siddiqui, MD, a radiologist and serial entrepreneur who is the cofounder, chairman, and CEO of a company called HOPPR AI, will present the view from industry. HOPPR AI is a for-profit company focused on building AI apps for medical imaging. As a radiologist, Dr. Siddiqui represents a medical specialty that is thought to likely undergo sweeping change as AI is incorporated into image-reading and diagnosis. His comments will focus on professional insights relevant to surgeons.

Their presentations will provide insights on general usage of AI at present, as well as predictions on what the landscape for AI in healthcare will look like in approximately 5 years. The session will include advice on what approaches to AI may be most effective for surgeons interested in ensuring positive outcomes and avoiding negative ones.

Additional information on AI usage pervades Clinical Congress 2025. In addition to various sessions that will comment on AI throughout the 4 days of the conference, various researchers will present studies that involve AI in their methods, starting presumptions, and/or potential applications to practice.

Access the Interactive Program Planner for more details about Clinical Congress 2025 sessions.

But the technology keeps changing as they do this, there are constant reports of students cheating their way through their degrees.

The AI and assessment problem has put enormous pressure on institutions and teachers. Today’s students need assessment tasks to complete, as well as confidence the work they are doing matters. The community and employers need assurance university degrees are worth something.

In our latest research, we argue the problem of AI and assessment is far more difficult even than media debates have been making out.

It’s not something that can just be fixed once we find the “correct solution”. Instead, the sector needs to recognise AI in assessment is an intractable “wicked” problem, and respond accordingly.

What is a wicked problem?

The term “wicked problem,” was made famous by theorists Horst Rittel and Melvin Webber in the 1970s. It describes problems that defy neat solutions.

Well-known examples include climate change, urban planning and healthcare reform.

Unlike “tame” problems, which can be solved with enough time and resources, wicked problems have no single correct answer. In fact there is no “true” or “false” answer, only better or worse ones.

Wicked problems are messy, interconnected and resistant to closure. There is no way to test the solution to a wicked problem. Attempts to “fix” the issue inevitably generate new tensions, trade-offs and unintended consequences.

However, admitting there are no “correct” solutions does not mean there are not better and worse ones. Rather, it allows us the space to appreciate the nature and necessity of the trade offs involved.

Our research

In our latest research, we interviewed 20 university teachers leading assessment design work at Australian universities.

We recruited participants by asking for referrals across four faculties at a large Australian university.

We wanted to speak to teachers who had made changes to their assessments because of generative AI. Our aim was to better understand what assessment choices were being made, and what challenges teachers were facing.

When we were setting up our research we didn’t necessarily think of AI and assessment as a “wicked problem”. But this is what emerged from the interviews.

Our results

Interviewees described dealing with AI as an impossible situation, characterised by trade-offs. As one teacher explained:

We can make assessments more AI-proof, but if we make them too rigid, we just test compliance rather than creativity.

In other words, the solution to the problem was not “true or false”, only better or worse.

Or as another teacher asked:

Have I struck the right balance? I don’t know.

There were other examples of imperfect trade-offs. Should assessments allow students to use AI (like they will in the real world)? Or totally exclude it to ensure they demonstrate independent capability?

Should teachers set more oral exams – which appear more AI resistant than other assessments – even if this increases workload and disadvantages certain groups?

As one teacher explained,

250 students by […] 10 min […] it’s like 2,500 min, and then that’s how many days of work is it just to administer one assessment?

Teachers could also set in-person hand-written exams, but this does not necessarily test other skills students need for the real world. Nor can this be done for every single assessment in a course.

The problem keeps shifting

Meanwhile, teachers are expected to redesign assessments immediately, while the technology itself keeps changing. GenAI tools such as ChatGPT are constantly releasing new models, as well as new functionalities, while new AI learning tools (such as AI text summarisers for unit readings) are increasingly ubiquitous.

At the same time, educators need to keep up with all their usual teaching responsibilities (where we know they are already stressed and stretched).

This is a sign of a messy problem, which has no closure or end point. Or as one interviewee explained:

We just do not have the resources to be able to detect everything and then to write up any breaches.

What do we need to do instead?

The first step is to stop pretending AI in assessment is a simple, “solvable” problem.

This not only fails to understand what’s going on, it can also lead to paralysis, stress, burnout and trauma among educators, and policy churn as institutions keep trying one “solution” after the next.

Instead, AI and assessment must be treated as something to be continually negotiated rather than definitively resolved.

This recognition can lift a burden from teachers. Instead of chasing the illusion of a perfect fix, institutions and educators can focus on building processes that are flexible and transparent about the trade-offs involved.

Our study suggests universities give teaching staff certain “permissions” to better address AI.

This includes the ability to compromise to find the best approach for their particular assessment, unit and group of students. All potential solutions will have trade offs – oral examinations might be better at assuring learning but may also bias against certain groups, for example, those whose second language is English.

Perhaps it also means teachers don’t have time for other course components and this might be OK.

But, like so many of the trade offs involved in this problem, the weight of responsibility for making the call will rest on the shoulders of teachers. They need our support to make sure the weight doesn’t crush them.

David Boud receives funding from the Australian Research Council, and has in the past recieved funding from the Office for Learning and Teaching

Margaret Bearman receives funding from the Novo Nordisk Fond and the Royal Canadian College of Physicians and Surgeons. In the past she has received funding from a broad range of organisations including the Tertiary Education Quality and Standards Agency (TEQSA), the Office for Learning and Teaching, Victorian and Commonwealth governments and a range of health professional education organisations, including the College of Intensive Care Medicine and the Royal Australasian College of Surgeons.

Phillip Dawson receives funding from the Australian Research Council, and has in the past recieved funding from the Tertiary Education Quality and Standards Agency (TEQSA), the Office for Learning and Teaching, and educational technology companies Turnitin, Inspera and NetSpot.

Thomas Corbin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

This article was originally published on The Conversation. Read the original article.