For most of history, education operated under an unavoidable constraint: knowledge was scarce. Instructors, textbooks, and classrooms were gateways to information.
That world is disappearing. Regulations are searchable. Scientific literature and courses are online. Generative artificial intelligence (AI) can produce an answer, explanation, or summary in seconds. A food safety professional today has access to more technical information than any training program could hope to teach.
This does not make knowledge less valuable, but it should change how we think about education. Knowing where to find an answer is not the same as understanding. Understanding is not the same as remembering. And remembering is not the same as knowing when, where, and how to apply answers.
For those of us who develop food safety education, that leads to a question we should be asking now: Are we providing enough opportunities for learners to practice using what they know?
Think about a course you teach, supervise, fund, or require. What are the most critical tasks the learner must be able to do afterward? How are those tasks linked to the content the course provided? Where are the resources to practice them?Â
If that is a difficult question to answer, we have identified an opportunity to rethink how we teach.
Knowing and Doing are Different Outcomes
There is good reason to ask the question. The distinction between learning something and being able to use it has been studied for decades. Success during instruction does not guarantee that knowledge will transfer when the setting, problem, or circumstances change.1,2 How people learn matters, as well. Active learning, retrieval practice, spacing, and meaningful feedback can improve learning, retention, and transfer.3–6 Food safety research provides a particularly relevant signal. A meta-analysis of food handler training found a large improvement in knowledge following training, but a considerably smaller improvement in observed food safety practices.7 Knowing more and doing differently were not equivalent outcomes.
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The point is not that traditional education has failed. It is that exposure, learning, retention, and application are not interchangeable measures of educational success. A learner can be exposed to information without learning it, learn it without retaining it, and retain it without recognizing when or how to apply it in a different context. If application is the outcome we ultimately expect, then we cannot simply assume it will follow from the other three. We must design opportunities to practice it.
What We are Seeing in Our Own Field
The National Environmental Health Association’s (NEHA’s) research provides similar signals. A national assessment of more than 2,200 retail food regulatory professionals identified priorities including active managerial control, root cause analysis, risk-based inspection, data analysis, program evaluation, and risk communication. A broader environmental public health assessment identified needs in investigations, risk assessment, compliance review, data management, and scientific reasoning.8,9
An internal analysis of Registered Environmental Health Specialist/Registered Sanitarian (REHS/RS) examination results found another distinction. Candidates tended to perform better on knowledge-based content, such as regulatory requirements, procedures, and technical standards, and lower on application-based content requiring interpretation, risk evaluation, problem solving, and professional judgment.10
These findings measure different things and do not demonstrate a lack of application skills. However, they point toward the same educational question: If we expect application, where are learners given opportunities to practice it?
Designing for Application
If application is the goal, then it needs to be part of the design rather than something we hope happens after the course ends. That does not require abandoning existing courses or adopting new technology. It starts with asking different questions about the education we already provide.
A useful framework is: Content → Practice → Feedback → Revisit → Measure.
Content:Â Be clear about what learners need to know and what they need to be able to do with that knowledge. If the objective is to interpret, evaluate, investigate, or decide, providing information alone does not satisfy the objective.
Practice:Â Give learners opportunities to use what they have learned. Cases and scenarios can require learners to sort relevant from irrelevant information, make decisions, and apply the same principle as circumstances change.
Feedback:Â Go beyond whether an answer is correct. Ask learners to explain why they reached a decision and provide feedback on the reasoning behind it.
Revisit:Â Stop treating the end of the course as the end of learning. Bring important concepts back later, preferably in a different context. The evidence on retrieval and spacing suggests that this strengthens durable learning.
Measure:Â Match assessment to the outcome. A knowledge test can tell us whether someone learned information. If the intended outcome is application, then our assessment should give learners an opportunity to demonstrate application, and when gaps remain, signal where practice and reinforcement should be directed.
These are existing approaches. What may need to change is how deliberately we build them into food safety education. The question for every course becomes remarkably simple: Where does the learner practice doing the things we ultimately expect them to do?
Technology Changes the Scale
Cases, scenarios, practice, and feedback are not new. What has limited their use is often practical: providing individualized, repeated practice is difficult. An instructor can lead a class through a case; providing each learner with changing scenarios and individualized feedback is considerably harder.
Adaptive systems, simulations, and now generative AI may change that equation. They can vary scenarios, respond to learner decisions, challenge assumptions, and test the same underlying principle under different circumstances.
The early evidence is encouraging but still developing. Studies of AI-supported tutoring and adaptive learning have reported improved learning outcomes, with effectiveness strongly influenced by instructional design.11,12 In regulatory education, enthusiasm also must be tempered by concerns about accuracy, validated source material, and appropriate human oversight.
AI did not give us practice, feedback, repetition, or experiential learning. It may, however, give us new ways to provide them repeatedly and at a scale that was previously impractical.
The technology is new. The principles of good learning are not.
Where to Start
Moving from knowing to doing is not solely the responsibility of educators. Educators can design opportunities for application, but experienced professionals make those experiences authentic. Supervisors in the field reinforce learning in practice. Organizations create the time and opportunity to use new skills. Funders and leaders influence what outcomes are financially supported, valued, and measured.
We do not need to redesign the entire food safety education system to begin. Take one course you teach, supervise, fund, require, or regularly attend and identify the most critical tasks or most important things participants should be able to do when it is over. Then, take a hard look at the course and find where they actually practice doing them. If those opportunities are missing, start there.
The next evolution in food safety education will be defined by how effectively we turn information into learning, how that learning is retained over time, and whether we create the opportunities to apply it.
The question is no longer simply whether we taught it, but rather: Did we give them a chance to use it?
References
Baldwin, T.T. and J.K. Ford. “Transfer of Training: A Review and Directions for Future Research.” Personnel Psychology 41, no. 1 (March 1988): 63–105. https://doi.org/10.1111/j.1744-6570.1988.tb00632.x.
Barnett, S.M. and S.J. Ceci. “When and where do we apply what we learn? A taxonomy for far transfer.” Psychological Bulletin 128, no. 4 (July 2002): 612–637. https://doi.org/10.1037/0033-2909.128.4.612.
Freeman, S., S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, and M.P. Wenderoth. “Active learning increases student performance in science, engineering, and mathematics.” Proceedings of the National Academy of Sciences 111, no. 23 (May 2014): 8410–8415. https://doi.org/10.1073/pnas.1319030111.
Pan, S.C. and T.C. Rickard. “Transfer of test-enhanced learning: Meta-analytic review and synthesis.” Psychological Bulletin 144, no. 7 (July 2018): 710–756. https://doi.org/10.1037/bul0000151.
Cepeda, N.J., H. Pashler, E. Vul, J.T. Wixted, and D. Rohrer. “Distributed practice in verbal recall tasks: A review and quantitative synthesis.” Psychological Bulletin 132, no. 3 (May 2006): 354–380. https://doi.org/10.1037/0033-2909.132.3.354.
Wisniewski, B., K. Zierer, and J. Hattie. (2020). “The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research.” Frontiers in Psychology 10 (January 2020): 3087. https://doi.org/10.3389/fpsyg.2019.03087.
Insfran-Rivarola, A., D. Tlapa, J. Limon-Romero, Y. Baez-Lopez, M. Miranda-Ackerman, K. Arredondo-Soto, and S. Ontiveros. (2020). “A Systematic Review and Meta-Analysis of the Effects of Food Safety and Hygiene Training on Food Handlers.” Foods 9, no. 9 (August 2020): 1169. https://doi.org/10.3390/foods9091169.
NEHA. Retail Food Regulatory Training Needs Assessment. 2024. https://www.neha.org/retail-grants-findings.
NEHA. Environmental Public Health Training Needs Assessment. July 2024. https://nnphi.org/resources/environmental-public-health-training-needs-assessment/.
NEHA. “Insights from the REHS Cross-Walk and Workforce Analysis: Implications for a NEHA Environmental Health Webinar Series.” Internal analysis. 2026.
Kestin, G., K. Miller, A. Klales, T. Milbourne, and G. Ponti. “AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting.” Scientific Reports 15 (June 2025): 17458. https://pubmed.ncbi.nlm.nih.gov/40537565/.
 Wang, S., F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du. (2024). “Artificial intelligence in education: A systematic literature review.” Expert Systems with Applications 252 (October 2024): 124167. https://doi.org/10.1016/j.eswa.2024.124167.Â
