Related Experiment Video
Updated: Sep 13, 2025

Studying Food Reward and Motivation in Humans
Published on: March 19, 2014
Implementing GRATL and artificial intelligence in experiential learning of obesity physiology and etiology
Zhiyong Cheng1, Jinying Yang1, Karla P Shelnutt2
1Food Science and Human Nutrition Department, University of Florida Institute of Food and Agricultural Science (UF/IFAS), Gainesville, Florida, United States.
Abstract:
Learning and dissemination of obesity physiology and etiology knowledge are essential to prevention and treatment of this chronic disease through concerted efforts from both professionals and the general public. In this article, we describe an innovative Gain in Research Ability Test per Literature (GRATL) framework that integrates artificial intelligence (AI) into experiential learning (EL) of obesity physiology and etiology through community outreach projects. The GRATL framework sets seven areas of research competencies, i.e., Identify, Question, Plan, Conduct, Analyze, Conclude, and Communicate, as the anticipated learning outcomes (ALOs), and it navigates the design and implementation of research and learning activities. The quantitative matrix of GRATL navigated AI application through rigorous verification and assessed the growth of students' research ability. Our data suggest that the GRATL framework enhanced students' discipline knowledge, research ability, and career competency skills including communication, problem-solving, critical thinking, knowledge construction with AI assistance, teamwork, leadership, and self-management. In addition, the students helped the communities gain a better understanding of obesity and appreciated the roles of lifestyle behaviors in chronic disease. As the seven areas of research competencies are valued and observed across disciplines, the GRATL framework coupled with AI-assisted EL may be adjustable and scalable in teaching and learning of other subjects.NEW & NOTEWORTHY Obesity is a global public health issue. Concerted efforts are needed from both professionals and the public to prevent and treat the chronic disease. Here we describe a GRATL framework that engages college students and the public to learn obesity physiology and etiology through AI-assisted experiential learning and citizen science. Implementation of the GRATL framework enhances students' discipline knowledge, research ability, and career competency skills, and it also helps the public gain a better understanding of obesity.
Related Concept Videos
Obesity
Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Law of Effect
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
Avoidance Learning and Learned Helplessness
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

