AI Will Write the Lesson Plan, and Teacher Preparation Should Be Glad

Lesson planning is problem solving, and teacher preparation has been treating it as something else. The artifact has been mistaken for the skill. A novice produces a plan that meets the rubric, the supervisor checks the boxes, and everyone agrees the candidate is ready. The plan is the visible output. The reasoning that produced it, which is the actual job, has been left largely to chance and to the quality of mentorship in the placement school. AI is about to make this confusion impossible to sustain, and the news is good rather than bad.

Consider what a teacher actually does when planning a lesson. She holds in mind the standards she must address, the students she has (who they are, what they did yesterday, what they fear, what they already know), the time available, the materials at hand, the colleague next door whose schedule overlaps, the assessment due Friday, the child who was crying in the hall this morning. She makes a sequence of decisions under all these constraints. The plan is the residue of that work. A second teacher with the same standards and a different group of children produces a different plan, and the difference is not stylistic. It reflects different problems being solved.

Programs do not generally teach lesson planning this way. They teach the form. The plan template, the objective-and-anticipatory-set sequence, the alignment to a standard. These are the easy parts, and they are also the parts AI now performs at a competent first-year level. A model handed a standard, a grade, and a topic will produce a workable plan in seconds. If the form was the skill, the skill is gone. If the form was the residue, what was always missing from preparation is what remains, and what remains is the actual teaching job.

I have spent enough years near teacher preparation to know that the field has long understood this in principle. The actual teaching job is solving complex, contextual problems, both pedagogical and relational, repeatedly. The repetition is the part that matters. Judgment is not transmitted by lecture. It accretes through volume of supervised decisions under varied conditions. A candidate should encounter hundreds of small cases before graduating. A reading group is stalling on a text from which two students have visibly disengaged. A child has stopped speaking in class after a family event the teacher heard about indirectly. A planned activity is collapsing in second period and four periods remain. Each case demands a decision. The decision can be defended or critiqued. The next case is different enough that the previous lesson does not simply transfer. This is how master teachers were built when the on-ramp existed in schools. The on-ramp has been narrowing for years. AI does not narrow it further. AI provides the platform on which preparation programs can finally do this work at scale.

Here is the part that should reorganize the curriculum. Students should learn to feed the complexity of a real classroom into the AI, and to evaluate what comes back. Not "write me a lesson plan on the water cycle for fifth grade." That prompt deserves the generic plan it gets. Rather, "I have twenty-eight fifth graders, six of whom read two years below grade, four English learners at different proficiency levels, a classroom with no working sink, forty-five minutes, and a state standard that asks for an investigation. Yesterday's lesson left them confused about evaporation. Propose three different approaches and explain the trade-offs." The skill lives in the input and the evaluation, both of which require knowing what could go wrong with each option. That is judgment in the precise sense, applied through the medium of a competent partner. The AI plans. The teacher decides.

Other professions discovered this structure long ago. Business case method has been the spine of MBA programs for a century, because case after case forces the student to decide under conditions the textbook did not anticipate. Law clinics serve the same function for the willing. Medical residency, at its best, does too. Teacher preparation has the analogous structure in student teaching, but student teaching arrives at the end, depends on the placement, and rarely produces deliberate variation across cases. The model is right. The proportion is wrong.

Putting this into practice means a smaller share of credits on form, methods, and standards memorization, and a much larger share on repeated case decisions with AI as the planning partner. It means faculty time for serious case construction and supervision, which is the most expensive thing a program can spend. It means accepting that some of what currently fills the curriculum is now cheap and must be reallocated rather than retained out of habit. None of this is free, and pretending otherwise will produce a watered-down version that disappoints everyone. The genuine version, however, is something teacher preparation has wanted to be for fifty years and never had the conditions to become. The conditions have arrived.

"First try with AI"; On the advantages of organic learning

Some people advocate for structured training programs and dedicated time for AI learning, but a more organic approach is more effective and efficient.

The secret to successfully incorporating AI into your work is to simply start using it for your next task. Rather than setting aside special time for AI learning, dive right in and explore how AI can assist you in your current projects. Need to do something? Write a memo, a long email, a letter, a grant proposal? "First Try with AI."

What do you have to lose? he worst-case scenario is that you waste a little time if AI proves unhelpful for that particular task. However, in most cases, you will discover its usefulness and potential to save you some time, even if it doesn't complete the task entirely.

It's important to recognize that AI never does everything for you. Only the most mindless, bureaucratic,  compliance-related content may be primarily handled by AI. However, for the majority of tasks, you will intuitively learn the right mix of human and AI ingredients to create the best soup. This organic learning process allows you to understand the strengths and limitations of AI within the context of your specific work.

There is nothing wrong with taking courses to learn about AI, but it is worth noting that assignments in such courses often lack authenticity. Those are "pretend tasks." Even after completing a course, you would still need to learn how to transfer your new skills into real-world contexts. In contrast, an organic approach to AI learning allows you to immediately apply your knowledge within the context of your work, resulting in a more motivated, deeper, and faster learning experience.

As you gradually incorporate AI into your daily tasks, you will naturally develop a better understanding of when and how to leverage its capabilities, and where to mitigate its shortcomings. This hands-on, contextual learning approach will not only help you become more proficient in using AI but also enable you to identify new opportunities for its application within your organization.

For educational contexts, we know there is a strong correlation between instructors personally using AI and them allowing students to use it in class. We don't trust things we do not understand, which explains the unreasonably strong worries about cheating. There will be no classroom use without the personal use by instructors first. Once teachers start using it for their own purposes, their anxiety levels go down, and their creativity frees up to invent classroom uses. 

AI revolution minus massive unemployment

The conversation on AI often revolves around efficiency and cost reduction, typically translating into fewer jobs. However, a pivotal shift in perspective—from cutting workforce to enhancing and expanding workforce capabilities—can redefine the role of AI in the corporate world. This approach not only preserves jobs but also adds significant value to customer experiences and broadens the spectrum of services and products a company can offer. 

The traditional method of dealing with technological disruption—laying off workers and hiring new ones with the necessary skills—is not only a waste of human capital but also disregards the cultural knowledge embedded within an organization's existing workforce. Retraining keeps people within the organization, allowing them to shift roles while retaining and applying their invaluable understanding of the company's ethos and operations in new ways.

The first step in a proactive workforce transformation strategy is to map out the anticipated skills and roles that will be in demand. This is not just about foreseeing the obsolescence of certain skills but identifying emerging opportunities where AI can augment human capabilities. For instance, with the rise of AI-driven analytics, there is a growing need for professionals who can interpret and leverage these insights into strategic decisions, enhancing business intelligence far beyond current levels.

Once future needs are mapped, the next step is to develop a compelling incentive structure for retraining. Traditional models of employee development often rely on mandatory training sessions that might not align with personal or immediate business goals. Instead, companies should offer tailored learning pathways that align with career progression and personal growth, supported by incentives such as bonuses, career advancement opportunities, and recognition programs. This approach not only motivates employees to embrace retraining but also aligns their development with the strategic goals of the organization.

With AI's capacity to handle repetitive and mundane tasks, employees can redirect their efforts towards more complex, creative, and meaningful work. This shift enables businesses to expand their service offerings or enhance their product features, adding significant value to what customers receive. For example, financial advisors, freed from the tedium of data analysis by AI tools, can focus on crafting bespoke investment strategies that cater to the intricate preferences and needs of their clients. Similarly, customer service representatives can use insights generated by AI to provide personalized service experiences, thereby increasing customer satisfaction and loyalty.

AI not only optimizes existing processes but also opens new avenues for innovation. For instance, in the healthcare sector, AI can manage diagnostic data with high efficiency, which allows healthcare providers to extend their services into preventive health management and personalized medicine, areas that were previously limited by resource constraints. In the retail sector, AI-enhanced data analysis can lead to the creation of highly personalized shopping experiences, with recommendations and services tailored to the individual preferences of each customer, transforming standard shopping into curated personal shopping experiences.

For successful implementation, organizations must foster a culture that views AI as a tool for empowerment rather than a threat to employment. Leadership should communicate clearly about the ways AI will be used to enhance job roles and the benefits it will bring to both employees and the company. Regular feedback loops should be established to adjust training programs based on both employee input and evolving industry demands, ensuring that retraining remains relevant and aligned with market realities.

By focusing on retraining the workforce to harness AI effectively, businesses can transform potential disruptions into opportunities for growth and innovation. This approach not only preserves jobs but also enhances them, adding unprecedented value to the company and its customers, and paving the way for a future where human ingenuity and artificial intelligence work hand in hand to achieve more than was ever possible before.

No time to learn AI? Use authentic learning

No time to to delve into the world of  AI? If so, you're not alone. Many of us feel a pang of guilt for not being able to spare the time to explore generative AI tools. However, it is much easier than you may think. 

The trick is to use chatbots for the regular tasks life brings to you. Anything, especially tasks you are not looking forward to do is a fair game. Think about updating syllabi, brainstorming assignments, developing grading rubrics, and planning lessons. The list extends to crafting administrative emails, organizing research data, summarizing articles, generating content ideas, and preparing meeting agendas. Try to ask a chatbot first. 

Now, it's important to temper expectations with a dose of reality. In about 70-80% of cases, AI will save you time right off the bat. However, in the remaining tasks, you might not see immediate gains, and they turn out to be easier done by hand. The effectiveness of AI heavily depends on the nature of your work and your willingness to stick to it and learn its nuances.

There's a learning curve, for sure, but it is not very steep.  The results are well worth the effort. For instance, AI's ability to generate first drafts, suggest edits, and even brainstorm ideas can significantly streamline your workflow.

However, it's crucial to understand the limitations of AI. It's not a magical solution to all your problems. Think of it more as a collaborative partner that can take on the heavy lifting of routine tasks, allowing you to focus on the more creative and complex aspects of your work. Literally, ChatGPT and its cousins shine in the most routine, most boring tasks, leaving you more time for creative work. 

The key to effectively integrating AI into your professional life is to start small, use the natural flow of tasks, and gradually expand its role as you become more comfortable with its capabilities. This will allow you to stay ahead of most students. Eventually you will also see how it could be used in instruction. But getting some first-hand experience is the first step.