Recipient Organization
GRADED GAMING, LLC
5222 SAVANNAH SPRINGS DR
LOUISVILLE,KY 40219
Performing Department
(N/A)
Non Technical Summary
Secondary career and technical education (CTE) is facing significant challenges, including teacher shortages, inefficient teaching methods, and a lack of qualified teachers leaving students disengaged and unprepared for post-secondary life. Innovative and accessible approaches to education are urgently required to equip students with practical skills to prepare them for a rapidly changing world. GradEd Gaming is revolutionizing education by developing a platform that combines gamification, virtual reality (VR), and artificial intelligence (AI) to provide equitable, hands-on learning opportunities in nutrition-sciences. The platform's AI teacher will help students engage with content and develop skills through VR gaming and instruction aligned with the National Institute of Food and Agriculture's (NIFA) funding opportunity in topic area 8.5 - Food Science and Nutrition (USDA-NIFA-SBIR-009962). In Phase I, we will develop and test a VR-simulated course module in "Advanced Foods and Nutrition" and integrate an AI teacher to guide students through the module. We will provide an immersive learning opportunity that teaches healthy nutritional choices to combat diet-related chronic disease while offering real-time student feedback through the AI teacher. The central hypothesis is that our AI-led VR simulation will have robust functionality, excellent usability for educators/students, and high levels of student engagement that will result in a powerful, user-friendly, and captivating educational experience. We will assess this using 3 metrics: 1) system usability scale, 2) student knowledge acquisition, and 3) positive stakeholder feedback. At the end of Phase I, we expect to have completed and evaluated the feasibility and usability of the VR module. In addition, our ultimate goal is to have developed a product that will: 1) promote lifelong health to end-users, 2) cultivate culinary skills and knowledge, 3) foster food sustainability, 4) foster career readiness, 5) increase community engagement with learning opportunities, 6) increase critical and analytical thinking skills for end-users, and 7) increase cultural compentence around diverse food traditions that promote inclusivity and understanding within communities.
Animal Health Component
30%
Research Effort Categories
Basic
20%
Applied
30%
Developmental
50%
Goals / Objectives
The major goal for this project is to is to engineer a VR platform that employs AI-led instruction to deliver high-quality, hands-onlearning in nutrition-science courses.For this Phase I study, we will build and test the feasibility and usability of one full VR module for the course "Advanced Foods and Nutrition", a nutrition focused CTE course, in the Agriculture and Family and Consumer Sciences career pathways, that will include a learning arc and gamified components around the following student learning tasks:Develop a plan for weight loss, weight gain, or maintenance while examining nutrition through the life cycle (infant, children, teens, pregnancy, adulthood, and elderly)Plan and examine a diet plan for a specific need such as high fiber, low fat, low cholesterol, low sodium, diabetic, athlete, heart disease, or lactose intoleranceDemonstrate and determine the correct cooking methods for a variety of food products such as roasting, baking, broiling, smoking, grilling, sautéing, frying, deep frying, braising, stewing, poaching, steaming, woking, convection, microwaving, and other emerging technologiesPrepare a variety of regional and international foods using appropriate methods and techniquesOperate tools and equipment following safety procedures and OSHA (Occupational Safety and Health Administration) requirementsDemonstrate and practice knowledge of food service safety and sanitation procedures and the factors that contribute to foodborne illnessTo achieve this, we propose the following 4 objectives:Objective 1: VR Environment Development. Build and test a robust and immersive VR environment that replicates real-world scenarios relevant to the Advanced Foods and Nutrition course.Objective 2: Realistic Simulation. Create and evaluate realistic simulations within the VR environment that allow students to practice and apply skills relevant to the Advanced Foods and Nutrition course.Objective 3: AI Integration. Incorporate the AI teacher into the VR environment, allowing it to interact with students and offer personalized instruction.Objective 4: Innovation, Evaluation, and Iteration. Evaluate how users engage with the platform, the effectiveness of personalized instruction, and the impact on student learning and satisfaction.
Project Methods
Objective 1: VR Environment Development & Realistic Simulation.Task 1.1: Research and implement rendering techniques to create realistic 3D models, textures, lighting, and sound effects in the VR environment.1.1.1 Identify and select appropriate software tools and libraries for implementing the desired rendering techniques.1.1.2 Acquire and create high-quality 3D models, textures, and sound effects relevant to the course.1.1.3 Implement the selected rendering techniques in the chosen software environment.Task 1.2: Develop algorithms for optimized rendering, minimizing latency and achieving smooth performance.1.2.1 Evaluate and select techniques for optimizing rendering performance in VR.1.2.2 Design and implement algorithms that dynamically adjust rendering settings based on relevant factors.1.2.3 Test and benchmark the rendering algorithms on various hardware configurations and VR devices to ensure compatibility and performance across different platforms.Task 1.3: Measure the feasibility of our VR environment.1.3.1 Administer the System Usability Scale (SUS) to stakeholders, to evaluate the ease of use, learnability, efficiency, and performance of the VR environment.1.3.2 Gather SUS responses and feedback from users, analyze SUS scores and comments to assess the usability of our innovation, and identify specific areas of strength and weakness based on SUS results.1.3.3 Use the SUS findings to inform iterative development efforts.1.3.4 Continuously monitor and apply improvements to ensure feasibility and user-friendliness.1.3.5 Conduct interviews and focus groups of educators, end-users, technical support teams, and parents to collect data for a thematic analysis of user experiences to identify recurring themes, patterns, and insights.Success Criterion: The successful outcome here is a final iteration of the VR environment that closely mirrors real-world scenarios relevant to the Advanced Foods and Nutrition course module with a SUS score of 68 and above, an above-average usability score.Objective 2: Realistic Simulation.Task 2.1: Research and implement physics simulations, dynamic object interactions, and simulated scenarios within the VR environment to replicate real-world CTE challenges. 2.1.1 Identify and select appropriate software tools and libraries for implementing physics simulations in the VR environment.2.1.2 Define and design the specific CTE challenges or scenarios that will be simulated within the VR environment.2.1.3 Implement the physics simulations and dynamic object interactions according to the defined challenges, incorporating realistic behaviors and responses of virtual objects.Task 2.2: Develop algorithms for accurate simulations and realistic behavior of virtual objects and scenarios.2.2.1 Design and develop algorithms that accurately simulate the behavior and interactions of virtual objects.2.2.2 Implement the algorithmsto create a realistic and authentic simulation.Task 2.3: Measure the feasibility of our VR simulations.2.3.1 Administer the SUS to stakeholders to evaluate the ease of use, learnability, efficiency, and overall performance of the VR simulations.2.3.2 Gather SUS responses and feedback from users, analyze SUS scores and comments to assess the usability of our innovation, and identify specific areas of strength and weakness based on SUS results.2.3.3 Use the SUS findings to inform iterative development efforts.2.3.4 Continuously monitor and apply improvements to ensure feasibility and user-friendliness.2.3.5 Conduct interviews and focus groups of educators, end-users, technical support teams, and parents to collect data for a thematic analysis of user experiences to identify recurring themes, patterns, and insights.Success Criterion: The successful outcome of this objective is a final iteration of the VR simulation that closely mirrors real-world scenarios relevant to the Advanced Foods and Nutrition course with a SUS score of 68 and above.Objective 3: AI Integration & User Testing.Task 3.1: Evaluate and select communication protocols and data exchange methods between the AI teacher and VR platform for seamless integration.3.1.1 Investigate and select existing AI foundational models, communication protocols, and standards used for integrating AI systems with VR platforms.3.1.2 Identify and incorporate the data exchange requirements between the AI teacher and VR platform.3.1.3 Design and implement a communication layer that facilitates seamless integration between the AI teacher and VR platform.Task 3.2: Implement AI algorithms for NLP, knowledge retrieval, and adaptive learning within the Advanced Foods and Nutrition course module.3.2.1 Research and select appropriate AI foundational models, algorithms, and techniques for NLP, knowledge retrieval, and adaptive learning.3.2.2 Design and develop AI algorithms that can understand and interpret end-user input and queries within the VR environment.3.2.3 Integrate the AI algorithms with the VR platform to enable knowledge retrieval and adaptive learning.Success Criterion: Our criteria for this objective is effective interactions and integration between the AI teacher and end-users within the VR environment as measured by positive qualitative data themes and a SUS score of 68 and above.Objective 4: Innovation, Evaluation, & Iteration.Task 4.1: Data Collection, Analysis, Evaluation, Success Assessment, and Reporting4.1.1 Experiment: Assign participants randomly to either the experimental or control group. In a single-blind design, participants will not know whether they are in the experimental (AI-led VR experience) or control group (just VR experience without the AI teacher) to minimize expectancy effects. Administer pre-test assessments to both groups to measure baseline knowledge, interest in learning, and problem-solving skills. An AI and VR-enhanced learning tool will be implemented in the experimental group's curriculum, and post-test assessments will be conducted after the academic semester to measure changes in knowledge, interest, and problem-solving skills. Conduct observations to gather data on end-user engagement (e.g. session duration, frequency of sessions, and participation in interactive activities), interaction with VR (e.g. user navigation patterns, objects interacted with, and responses to VR simulations), and AI usage.4.1.2 Feedback: Surveys, interviews, and questionnaires will be administered tocollect data on their experiences, attitudes and perceptions of the VR and AI-led learning.4.1.3 Analysis: Conduct thematic analysis of open-ended feedback from end-users, teachers, and guardians. And evaluate for positive feedback and insights on the tool's impact on education. Perform regression analysis to explore relationships between usage patterns and learning outcomes. Calculate and compare pre-test and post-test scores to determine statistically significant improvements in knowledge (e.g., t-tests, ANOVA), looking for a p-value of less than 0.05, suggesting statistical significance. Assess the frequency and duration of VR sessions and AI interaction time to measure increased engagement. Calculate effect sizes to determine the practical significance of observed differences and utilize regression analysis to explore relationships between usage patterns and learning outcomes. Measure success based on the significant improvement in knowledge scores, increased engagement, positive user satisfaction, usability, and learning outcomes.4.1.4 Reporting: Compile the findings highlightingthe tool's effectiveness, strengths, and areas for improvement. Use the results to make evidence-based recommendations for optimizing the VR and AI tool.Success Criterion: Our criteriais evidence that the AI provides personalized instruction and dynamic adaptation as measured by an increase in user knowledge, learner satisfaction, and statistical significance in our data points.