Operational Literacy with AI
Operational Literacy with AI is the ability to intentionally direct, evaluate, oversee, and responsibly integrate AI systems while maintaining human judgment.
This definition provides an approach in which ‘literacy’ of the individual is focused on knowing how to interact with AI tools, workflows, and systems in a way that is appropriate, responsible, and effective. The Structured Thinking System takes the position that AI literacy is not tool literacy [1][2].
AI tools will continue to change. Automated systems will continue to advance toward more agentic behavior. As these tools evolve the only way individuals can keep up with those changes is to learn how to structurally think with an iterative tool in and of itself.
Traditionally, such a definition was not needed. Specific outputs were consistently generated by specific inputs. This is the historically expected operational mode of technology and up to this point, all human ‘literacy’ with computing technology has been grounded in this truth. This is no longer the case.
AI creates a different learning challenge where the old model of assigning literacy to tool mastery is no longer adequate. If the tool itself is designed to iterate and influence outputs based on user inputs, literacy can no longer begin with tool mastery. It must begin with cognitive mastery and as such the system also claims AI literacy must develop in stages [3][4].
The Structured Thinking System uses both these claims as a foundation. Participants going through the stages of operational literacy do not depend on mastery of specific tools as much as they depend on mastery of their own cognitive development surrounding AI tools in general.
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This approach presents the user with a scale ranging from Exposure (stage zero) to Systems-Level Operational Literacy (stage four). In doing so, it mimics the development process similar to how humans develop language literacy.
The most basic stage of Operational Literacy is defined as Exposure. This is stage zero. Individuals who experimentally use AI, copy and paste prompts for promised outputs, and those using AI for primarily convenience or entertainment are at this most basic level.
This is similar to a younger child placing alphabet magnets on a fridge. There is no reason or justification for what they create, they are simply investigating and playing with something that they do not fully comprehend or understand. Likewise, most can agree that exposure to AI does not create operational literacy.
As the system progresses, the next stage is Assisted Usage (stage one). In this stage we now have individuals who perform basic AI-assisted tasks, much like email summaries or meeting notes but without the refinement tools to direct AI or the evaluation tools to retain human judgment. In fact, the lack of intentionality becomes the differentiating factor between stage one and stage two.
Why intentionality? Without intentionality users are still in a stage where they are just adhering to ‘rules’ or are simply reacting to the AI tool they are using. Intentional actions on the part of the user, no matter what field of study, are an indicator of understanding the logical progression of cause and effect within that field.
When users start to understand that specific actions directly affect and cause specific outputs, they begin to shift from reactionary usage to intentional structure. We call this the ‘Emerging Stage’ because at this stage the threshold of operational literacy begins to emerge.
Much like our actual language literacy parallel, now we have a user who is intentionally beginning to activate the AI tool for specific results. Individuals are not only aware of the tool (stage zero), and they are also not just following the ‘rules’ of how to use the tool (stage one), they are finally beginning to see the patterns, growing in evaluation awareness, and recognizing how to structure their input to get desired outputs.
To be fair, this is a much higher standard than most of the industry is ready to acknowledge. Yet, one cannot successfully argue that because they know what a hammer is, how to use it, and how to hit a nail, they are operationally literate enough to build a house.
Operational Literacy solidifies in the next stage, stage three, the Operational Literacy stage. In this stage the user is intentional in all their interactions with AI and additionally is intentionally integrating AI into operational workflows with an understanding of the implications and operational risks of doing so. They intentionally maintain evaluation, oversight, and workflow awareness. They are no longer responding to AI, they are functionally directing whatever AI tool they use at a workflow level.
It is worth noting that the idea of governance is now fully active in this stage as well. It started as a recommendation in stage one, “you might want to evaluate that” to a self-directed thought in stage two, “I may need to evaluate this output first” to an intentional, consistent evaluation in stage three.
The final stage is stage four, Systems Level Operational Literacy. At this stage the focus shifts from successfully working with AI to successfully managing environments where AI is working. The challenge is no longer producing quality outputs. The challenge is ensuring those outputs remain reliable, scalable, and aligned over time.
This is where users demonstrate systems-level awareness while the AI tool(s) are doing the work. This stage would perhaps best be likened to that of a manager leading a team. Instead of directly being involved in each step of the process, they have now moved into a position of governance and oversight.
Worth noting here is that even as the final Operational Literacy stage, no part of this stage depends on mastering all AI tools in and of themselves.
Just as traditional literacy models are multidimensional, so too is Operational Literacy with AI. The current Structured Thinking System covers five domains inherent to Operational Literacy. To be clear, we do not advocate that these five domains are all that are or will ever be. AI is still a new field of learning and as it continues to evolve so too may be the capabilities through which Operational Literacy with AI is viewed.
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The STS curriculum is designed to encourage mastery in five domains: Intentional AI Interaction Habits; Mental Models & AI Reasoning Frameworks; Evaluation, Verification & Oversight; Workflow & Operational Thinking; and Confidence & Operational Readiness.
We score each domain to indicate if the user is developing in the domain (level one), applying the domain (level two), or integrating the domain (level three).
By breaking down Operational Literacy into domain capabilities the system recognizes the important aspect of human cognitive development in which understanding and effective usage of a given topic or study is multifaceted.
The system holds that it is entirely possible for an Individual to be ‘Systems-Level Operationally Literate’ while not having mastered, and potentially never mastering building or designing a specific system. Society is full of literate individuals who can read, write, speak, and communicate who will never author a book or deliver a historical speech. The latter does not define the former.
For example, in domain one, at the developmental level, learners are starting to build intentional interaction with AI. Therefore, when assessing someone’s overall Operational Literacy with AI, they could be functioning at an Exposure or Assisted Usage stage without having shown any development of this domain.
Likewise, in domain five where we are evaluating confidence & operational readiness, at the application level, level two, users are demonstrating consistent application in real work, structured thought, and repeatable refinement. It is entirely possible that an individual may be only at an emerging operationally literate stage while demonstrating high confidence and readiness to advance.
Because progress in the domains is not linear or uniform and strength in one domain does not guarantee strength in others, in some instances individuals may believe themselves to be more operationally literate than they are. In a similar situation, someone may think themselves more advanced in reading due to a large vocabulary, yet have poor critical analysis skills to really interpret the deeper meaning of what they are reading.
Perhaps the final and most important claim that is made through the Structured Thinking System’s view of Operational Literacy is that true AI literacy eventually expands into governance and stewardship. [5][6] This has already been alluded to when discussing the move individuals make from stage to stage in terms of Operational Literacy.
It bears repeating because this claim is only possible due to Operational Literacy with AI developing in cognitive stages where individuals demonstrate movement from one stage to the next as opposed to a ‘tool-first’ approach. In fact, the stance of a ‘tool-first’ approach flies in direct opposition to everything educational learning theory has discovered throughout human history.
Education has always valued the ‘why’ over the ‘how’ because that is the kind of knowledge that can be transferred across different tools and applied to different contexts. By taking an approach that is primarily grounded in tool education you not only limit an individual's ability to work outside a specific tool, you rob them of the educational foundation that naturally develops the more critical skills needed in more advanced settings.
A simple example would be that I know how to make homemade bread. I also own a bread machine. That machine and the tools used to make bread have changed throughout history. What has not changed? The ingredients needed, the ability to recognize when the dough is the right texture, when it is not, the knowledge of how to provide subtle corrective actions if needed. This knowledge of how to think about the process over the tool allows me to get a desired output (fresh bread) no matter the tool or its state. Thinking will always endure; tools do not.
This has a profound impact on organizations because this is not the approach most are taking. To compete in this new AI environment, organizations will need to rethink not only their approach to adopting AI, but also when, why, and whether they should adopt.
Throughout history, literacy has never been measured by someone's ability to operate a specific tool. Literacy has always been measured by their ability to think, communicate, evaluate, and apply understanding across changing tools and environments.
Operational Literacy with AI should be no different because the future of AI will not belong to those who have mastered specific tools. It will belong to those who have learned how to think with systems intentionally, responsibly, and effectively.
The tools will change. The thinking endures.
That is true Operational Literacy with AI.
[1] Kassorla, M., Georgieva, M., & Papini, A. (2024, October 6). Defining AI literacy for higher education. EDUCAUSE Review. https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/defining-ai-literacy-for-higher-education
[2] Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3313831.3376727
[3] Benner, P. (1984). From novice to expert: Excellence and power in clinical practice. Addison-Wesley.
[4] Dreyfus, H. L., & Dreyfus, S. E. (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. The Free Press.
[5] Bipartisan Policy Center (2024). Navigating the Future: The Growing Need for AI Literacy. https://bipartisanpolicy.org/article/growing-need-for-ai-literacy/
[6] UNESCO. Recommendation on the Ethics of Artificial Intelligence (2022). https://unesdoc.unesco.org/ark:/48223/pf0000381137