Nature Podcast: Hunger, Hormones and AI in Higher Education

The Nature podcast dives into three interconnected science-and-technology stories: in mice, how hunger and hormonal signals integrate in the brain to influence behavior via a tiny group of neurons and a key brain region; in academia, how generative AI is reshaping learning, assessment, and pedagogy on campuses around the world; and in medicine, the advent of a blood-based Alzheimer’s biomarker test that can help rule out the disease in primary care settings. The episode also discusses the evolving debate around AI testing and safety, including the future of AI evaluation beyond the Turing test. Read on for concise takeaways across neuroscience, education technology, and clinical biomarkers.

Hunger, Hormones and Neural Decision-Making

The episode opens with a deep dive into a surprising link between hunger, hormonal signals, and social behavior in mice. Researchers manipulated AGRP-expressing neurons, the brain’s hunger hub, and showed that activating these cells could induce pup-directed aggression in sated mice and that inhibiting them could suppress aggression under food deprivation. A key finding was that the rate of aggression varied with the mouse estrous cycle, and that the ratio of progesterone to estrogen strongly predicted aggression levels. The team traced this hormonal sensing to a projection from hunger neurons to the medial preoptic area (MPOA), a region associated with parental behavior. This work suggests that instincts are flexibly tuned by internal states, and that single neurons can integrate multiple concurrent signals to drive context-appropriate actions. The broader implication is that neural decision-making sits at the intersection of hunger, stress, and reproductive state, and that high-dimensional internal states must be distilled into actionable choices.

“the ratio between progesterone and estrogen correlated really well with the rate of aggression in these animals” — Minran, lead researcher behind the project.

AI in Higher Education: Policies, Pedagogy and Assessments

The podcast then shifts to the campus landscape as generative AI tools like ChatGPT become ubiquitous in student work. Surveys indicate substantial use of AI for explanation and editing, with a smaller fraction used to generate raw text for assessments. Universities face a policy and pedagogy challenge: how to harness AI without eroding critical thinking. A prominent example is the University of Sydney’s dual-track assessment approach, combining secure, proctored exams with unobserved assessments where AI is allowed, to cultivate skills that transfer to secure evaluations. Separately, the impact of AI on learning is being explored through active-learning studies, including Harvard researchers building AI tutors around active-learning frameworks who reported learning gains when AI supported instruction. The prevailing spirit is one of cautious adoption, ongoing evaluation, and a pivot toward teaching core human skills such as analysis, discussion, and judgment that AI cannot replace. A common concern is whether AI undermines deep learning, with researchers seeking robust evidence on cognitive engagement and memory retention when AI is part of the learning process.

“these tools have just arrived and now they're kind of scrambling to introduce policies which guide students and faculty into how to use AI” — Helen Pearson, AI in education expert.

Alzheimer’s Blood Test: Primary Care Screening and Early Intervention

In the medical highlights, the episode covers the FDA-approved blood test for a phosphorylated form of tau (P-tau181) as a screening biomarker for Alzheimer’s disease in primary care settings. The test demonstrates a high negative predictive value, correctly ruling out Alzheimer’s in a large majority of cases when the result is negative, with follow-up MRI/CSF tests used to confirm positive results. The promise is early detection, which could enable timely therapeutic interventions that slow disease progression. While researchers caution that sensitivity and specificity values, as well as gray-zone results, require further data, the development represents a significant step toward accessible, non-invasive screening that complements existing imaging and CSF analyses. Neurologists emphasize that early diagnosis could dramatically affect treatment strategies and patient outcomes.

“the test correctly ruled out Alzheimer's 98% of the time” — a neurologist involved in evaluating the test.

Turing Test, AGI and the Next Era of AI Evaluation

The final science discussion centers on the ongoing debate sparked by AI language models. The performance of AI on the classic Turing test has reignited questions about how we define intelligence and how we should test AI systems. Some researchers argue for tests that focus on AI safety, robustness, and alignment with human values rather than pursuing artificial general intelligence (AGI) as a single milestone. Anel Seth highlights a call to describe the AI we want and then tailor tests to those capabilities. The conversation also touches concerns about AI’s potential to deskill humans, propagate biases, and generate harmful content, underscoring the need for safeguards while acknowledging the rapid pace of technological change. The episode frames this as part of a broader societal dialogue about how to regulate, deploy, and evaluate AI in a way that benefits learning, health, and knowledge creation.

“let's figure out the kind of AI we want and test for those things” — Anel Seth, AI researcher.

Context, Implications, and Forward Look

Throughout the show, the emphasis remains on how science translates into real-world practices—whether it is translating neural signals into behavioral predictions, rethinking assessment to preserve learning in the AI era, or bringing a blood-based test to primary care. The episode closes by underscoring that better data, rigorous evaluation, and thoughtful policy are essential as AI and biomedical innovations accelerate. The conversations illustrate the cross-disciplinary nature of modern science, where neuroscience, education technology, and clinical biomarkers converge to influence research, policy, and everyday life.