Innovation Understanding Nuclear Power

Adaptive Learning & AI: The Next Generation of Nuclear Training

Humans use forms of artificial intelligence (AI) every day. Chances are, you have an AI-based personal assistant within arm’s reach right now. As technology has advanced, we have explored more ways to use AI such as making tasks easier, making decisions with swift data analysis, and predicting trends for both industry and personal use. An emerging technology in education is the use of artificial intelligence to create adaptive learning systems. There is significant research to support the use of adaptive learning to produce a more efficient and positive student experience. 

The next evolution of nuclear training includes adaptive learning, which supports and streamlines the Systematic Approach to Training (SAT) process outlined in ACAD 21-001, by customizing lesson plans according to site-specific student proficiency requirements determined by the utility. 

What is adaptive learning?  

Personalized learning is an effective strategy for learning because it meets the student where they are and adapts the learning experience to meet their unique needs. Adaptive learning provides immediate feedback and specific resources for the student to review and apply. Courses built with adaptive learning have “smart” capabilities to guide the student through the content based on their performance. Other forms of adaptive learning can bypass lessons where students show proficiency saving time and, in the case of industry training, money. Within adaptive learning, technology exists two methods: designed and algorithmic adaptivity. Designed adaptivity requires a teacher to outline and design the teaching sequence that will result in proficiency. Algorithmic adaptivity utilizes algorithms to determine what the student ‘knows’ and then what the student should experience after that (Smart Sparrow, n.d.). 

How does artificial intelligence contribute to the adaptive learning model?  

Artificial intelligence uses programming and algorithms to determine student strengths and areas of need, actions needed to assist the learning process, and problem-solve when obstacles arise. AI in education provides support for teachers to create customized learning paths that are learner specific and supports students in building self-efficacy through immediate feedback and guidance through content. In addition, artificial intelligence and adaptive learning allow students to work through content on their own time outside the walls of a school or the training building which increases student autonomy and therefore produces positive outcomes.  

Ensure effective learning experiences with adaptive learning 

Adaptive learning, charged by artificial intelligence, strips the limitations of traditional learning and gives students the ability to control their progress and efficiency in learning new content. While more research is needed to test the intensity of the advantages of artificial intelligence and adaptive learning, the data so far is promising. A literature review conducted in 2020 reported that in 37 recent studies, 86% reported positive effects of adaptive learning with improved student performance. With substantial amounts of preliminary research reporting improved student outcomes, it is evident that artificial intelligence supporting adaptive learning is an effective tool in virtual learning environments. 

 PLANT: A Real-World Application 

An example of the use of artificial intelligence and adaptive learning in a real-world environment is the use of Accelerant Solutions’ PLANT™ software in nuclear energy training. PLANT™ is a web-based authoring tool and application that allows editing, revising, and organization of all training content in a centralized location that also uses designed adaptive learning. This tool was created to assess incoming trainees to determine their level of proficiency in the nuclear power plant and how much of general reactor fundamentals training they will need for their specific jobs. Instructors set the parameters to determine the proficiency of content according to objectives and industry standards. Students take an entrance quiz, again with parameters set by training managers, and based on performance the student will receive a specialized learning path to follow through the material. The developers of PLANT™ are still in the preliminary stages of data collection but expect data to reflect improved student outcomes for nuclear energy utilities using the adaptive learning feature in PLANT. 

As with any educational technology, there is constant evolution in strategy, method, and tools to deliver instruction. Adaptive learning, supported by artificial intelligence, is another evolution in education with encouraging research to improve student outcomes beyond current capabilities. The data and research seem to be trending upward, making adaptive learning an emerging trend not only in educational technology but specifically in nuclear training. 


 Let’s Talk About Adaptive Learning. Smart Sparrow. 

 Li, F., He, Y., & Qingshui, X. (2021). Progress, challenges, and countermeasures of adaptive learning: A systematic review. Educational Technology & Society. 24(3), 238-255. 

 Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M., (2020). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments. 


Jessika Hernandez

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