Howard R Moskowitz¹*, Taylor Mulvey², Stephen D. Rappaport³, Sharon Wingert⁴, Tonya Anderson⁴ and Martin Mulvey¹

¹Cognitive Behavioral Insights, LLC, USA
²St. Thomas More School, USA
³Stephen D. Rappaport Consulting LLC, USA
⁴Tactical Data Group, USA

*Corresponding Author: Howard R Moskowitz, Cognitive Behavioral Insights, LLC, Albany, NY, USA.

Received: May 31, 2024; Published: June 29, 2024

Abstract

Using artificial intelligence (LLMs, large language models), the paper shows how one may design a short course for school students, with the focus on mental health and good nutrition. LLMs help to generate mind-sets, different ways of thinking about the topic of mental health and nutrition. LLMs suggest three mind-sets (Ignorant, Cautious, Informed), and then presents the details of a question-and-answer session where each mind-set in turn presented to the audience their point of view about the topic. The paper ends with a simulated question-and-answer session, presenting relevant information. The paper focuses on the combination of explicating a topic with entertaining the school student through a lively presentation of the material.

Keywords:Artificial Intelligence; Low-Income Students; Mental Health; Mind Genomics; Synthesized Mind-Sets

References

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Citation

Citation: Howard R Moskowitz., et al. “Interesting Low-Income School-Age in Nutrition for Better Mental Health: Synthesizing Student Mind-Sets Using Artificial Intelligence to Understand and to Communicate". Acta Scientific Nutritional Health 8.7 (2024): 86-92.

Copyright

Copyright: © 2024 Howard R Moskowitz., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.