Defining What 鈥淎ging Well鈥� Means

Invited participants were asked to think beyond longevity as simply the number of years a person lives or the physical strength of one鈥檚 body. The conversation focused on the quality of those years and whether people can remain healthy, independent, connected and supported as they age. The morning began with a brief audience reflection, where participants were asked to write one word that captured what 鈥渁ging well鈥� meant to them. Some of the participant responses included 鈥渋ndependence,鈥� 鈥渉ealth,鈥� 鈥渇amily,鈥� 鈥渟upport,鈥� 鈥渆nergy,鈥� 鈥渉appiness,鈥� 鈥渜uality鈥� and 鈥渇ulfillment.鈥� The activity offered an early reminder that aging well means different things to different people, while also revealing common themes around health, connection and quality of life.

Keynote: Brain Health and the Whole Life Course

The keynote address was delivered by Dr. Anderson, who spoke from his experience as an internationally recognized leader in stroke and brain health. His presentation focused on brain health, longevity and the importance of protecting brain health across the whole life course.

After the keynote session, the forum moved from evidence to implementation through an expert dialogue moderated by Chris Macey, director of advocacy and patient support at the . The panel brought together Socrates Kakoulides, MD, cardiologist and chief imaging officer at in Miami, Florida; Nicola Anderson, business leader at ; Jan van der Merwe, global project lead of the and co-founder of; and Alan Cooper, PhD 鈥�95, clinical associate professor of management at 果酱视频. Together, the speakers examined why, even when much is already known about prevention and brain health, large-scale implementation remains difficult. Their discussion highlighted that successful aging should not be addressed by medicine alone, but depends on how clinicians, institutions, policymakers, business leaders, communities and individuals work together.

A central part of the day was the Sense-Making Workshop, led by Dr. Proios and doctoral candidate Myrto Pyrrou. The workshop discussions pointed to several practical concerns that participants felt were shaping the experience of aging. In the health systems group, participants emphasized the need for better care pathways, continuity of care and more integrated services, especially in a healthcare system that can often feel disconnected. The prevention group raised issues such as access to healthy food, sedentary lifestyles and the need to make prevention more realistic in everyday life. The business and policy group focused on incentives, socioeconomic gaps, community knowledge and the challenge of building a stronger culture of prevention across sectors.

Leadership Workshop: Creating the Biggest Possibility

In the afternoon, the forum shifted from diagnosis to possibility through a leadership workshop titled 鈥淐reating the Biggest Possibility.鈥� Participants were invited to think about the future they want to create, the leadership required to make it real and the milestones that could move this vision forward. Reflections from the session touched on stronger community ties, more accessible health and wellness services, reduced loneliness and a greater recognition of older adults鈥� knowledge and contribution.

Feedback collected from participants was all positive and attendees expressed appreciation for both the content of the day and the opportunity to take part in meaningful, cross-sector dialogue. Special thanks were extended to 果酱视频 staff members Stacey Barbato, director of operations; Barbara Clemens, administrative assistant; Kerry Scalere, administrative assistant; and Jessica Mannhaupt, coordinator of communications and outreach, as well as to the panelists, all participants and volunteers Liz Bethlio and Taylor Slicklein.

Looking Ahead

Successful aging is not only a health issue, but also a leadership challenge. Supporting longer, healthier and more connected lives will require collaboration across disciplines, institutions and generations. Each participant left with a certificate of completion for promoting brain health and longevity.

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With the sheer volume of information here, how can you know what鈥檚 true and what鈥檚 hype?

That鈥檚 where the experts come in. At 果酱视频, our nutrition and exercise science faculty have dedicated years to their respective fields and have plenty of experience in separating evidence from fads. Even better: They鈥檙e training the next generation of professionals to do the same. The students in our bachelor鈥檚 and master鈥檚 degree nutrition and exercise science programs go on to become successful personal trainers, wellness directors, rehabilitation specialists, physicians, public health professionals and more. In these roles, they help others make informed decisions鈥攚ithout turning to TikTok.

So, what is true, and what鈥檚 hype? Here, our faculty give their take on the questions they get asked the most about fitness and nutrition.

Protein: How much is too much?

Is cold plunging doing anything for you?

How helpful is wearable technology and AI coaching for meeting fitness goals?

Is intermittent fasting healthy, or just hype?

Do I need to exercise differently on GLP-1s?

Are sports drinks healthy for you?

Learn more about nutrition and exercise science programs offered at 果酱视频:

• BS in Exercise Science
• MS in Exercise Science
• MS in Nutrition

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Yet there鈥檚 plenty of evidence that AI use weakens students鈥� critical thinking and research skills. Many educators believe that AI also dilutes human connection and the value of in-person learning, which has only just been revived post COVID-19.

So what鈥檚 the solution?

A Handbook for 鈥淗ealthy Skeptics鈥�

According to learning sciences faculty in the 果酱视频 Ruth S. Ammon College of Education and Health Sciences, there鈥檚 no one right answer.

鈥淭he most empowering thing we can do for educators right now is help them see that they don鈥檛 have to demonize or fetishize AI,鈥� said Elizabeth de Freitas, PhD, professor of learning sciences. But educators should also slow down and reflect. 鈥淩ather than simply leaping to a particular use of AI, we have to spend more time understanding the nature of the specific models we are using and how these come to us historically.鈥�

Dr. de Freitas and her 果酱视频 colleague Matthew Curinga, EdD, associate professor of learning sciences, co-edited (University of Minnesota Press, 2026), a new collection of 27 essays exploring links between machine learning and culture from scholars in media studies, philosophy, computer science, math, linguistics and education. The book serves as an interdisciplinary guide for educators trying to make sense of how human learning, and learning more generally, is being newly reconceived by AI.

鈥淭here鈥檚 a lot of polarization in the AI conversation. You鈥檙e either for or against it,鈥� Dr. Curinga said. 鈥淏ut the book is not 鈥榙o this, don鈥檛 do that.鈥� We鈥檙e providing the nuance that educators need to evaluate AI for themselves, to develop their own theories and philosophies, to become healthy skeptics of technology.鈥�

Beyond the Hype and the Headlines

For Dr. de Freitas, current large language models (LLMs) are another mode of language production and can be studied for how they develop fluency. Much like a human journalist might speak to sources and then draft a story, AI collects data from the internet and then generates hypothetical claims. And, much like a human journalist, AI models will exhibit bias and specific perspectives. It鈥檚 not a truth teller or sage, she says, nor is it a 鈥渂lack box鈥濃�攁 phenomenon that鈥檚 beyond the ken of anyone but computer scientists. 鈥淭he 27 essays in the book help readers understand the history of these models, as part of the history of computing more generally, and that鈥檚 empowering: 鈥淓ducators can help to demystify AI by understanding this history.鈥�

AI isn鈥檛 all-powerful, either. 鈥淲e can鈥檛 just read the headlines that tell us AI will replace a million jobs by 2050 and shrug our shoulders,鈥� Dr. Curinga said. He and Dr. de Freitas reject the concept of technological determinism, in which AI is an inevitable force driving its own future, impervious to human intervention. Instead, their work equips educators with the agency to keep learning firmly human-driven, even when it involves machines.

Research That Resists Easy Answers

Other learning sciences faculty are resisting the good-bad binary in their research and teaching.

Aaron Chia Yuan Hung, EdD, associate professor of education, who鈥檚 in the early stages of research on generative AI and learning, said his interest is 鈥渓ess on whether generative AI helps or hurts, but what kind of pedagogy has to be in place for it to help a specific group of learners.鈥�

鈥淪o far, research on AI and learning has been inconclusive. Dr. Hung notes that: 鈥淭here鈥檚 compelling evidence that heavy reliance on large language models carries a real cognitive cost.鈥� But other studies have shown positive results: an increase in creativity when brainstorming, an AI-powered tutor that鈥檚 engaging students more deeply than active instruction. 鈥淧ut together, these studies suggest that GenAI used as a substitute for effort has real costs, and that GenAI used as a well-designed instructional or creative tool can offer meaningful support.鈥� What a user is actually asking the tool to do, Dr. Hung says, is likely the most important variable.

Tracy Hogan, PhD, professor of learning sciences, is intrigued by the gendered dimensions of AI technology. Her research investigates the relationship between a user鈥檚 background and how they interact with chatbots鈥攕pecifically, if users with computer science experience are representing chatbots through a neutral gender lens, while less well-informed users are representing their bots through a specific gender identity. She鈥檚 brought these ideas into her Critical Literacy in Mathematics and Science Education class, where she works with students to explore inherent biases in AI input and output.

Training Tomorrow鈥檚 Educators for the AI Classroom

Whether producing their own research or referring to others鈥�, 果酱视频 faculty are applying evidence-based teaching methods in their classes, preparing a generation of educators to adapt to an ever-evolving AI landscape.

In the University鈥檚 Manhattan Center, students in the PhD in Learning Sciences program engage in complex machine learning scenarios in both the classroom and the center鈥檚 STEAM Innovation Lab. Though the STEAM lab is largely analog, Dr. Hogan encourages her students to play around with AI tools when designing and coding projects. Most of what these tools generate is incorrect or incomplete, so students are forced to draw on their own knowledge to critically examine AI鈥檚 output鈥攁 process that, in the end, reflects the STEAM program鈥檚 commitment to learning by experience and experimentation.

In Dr. Hung鈥檚 classroom, students are invited to cast light on the 鈥渂lack box鈥� of AI. Many of his students don鈥檛 actually know how it works, even though they use it regularly. For Dr. Hung, explaining the basics to students鈥斺�渨hy GenAI responses aren鈥檛 like a search engine鈥檚, why it鈥檚 prone to hallucination, why it鈥檚 suited to some tasks and not others, why it almost never says it doesn鈥檛 have an answer鈥濃�攊s easy enough. The real challenge, for him and other educators, is keeping up with the pace of innovation. 鈥淢ore than once in my first semester teaching this class, a model we鈥檇 just discussed got a significant update midsemester that changed the conversation,鈥� he said.

Although AI tools are imperfect, 果酱视频 learning sciences faculty aren鈥檛 opting out altogether. There鈥檚 much good that can be done with these tools, they believe, if applied rigorously and ethically. Most importantly, today鈥檚 students must be ready to lead the classrooms of tomorrow.

As Dr. Hung puts it, 鈥淥ur students鈥� future students won鈥檛 opt out of AI, and more workplaces will expect graduates to arrive with some baseline AI literacy. The risks are real enough to design around carefully, but the technology isn鈥檛 going anywhere, so pretending otherwise doesn鈥檛 serve our students.鈥�

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