Once I began engaged on the brand new version of Head First C# again in 2023, AI instruments like ChatGPT and Copilot have been already altering how builders write and study code. It was clear that I wanted to cowl them. However that raised an attention-grabbing problem: How do you educate new and intermediate builders to make use of AI successfully?
Virtually all the materials that I discovered was geared toward senior builders—individuals who can acknowledge patterns in code, spot the refined errors usually present in AI-generated code, and refine and refactor AI output. However the viewers for the ebook—a developer studying C# as their first, second, or third language—doesn’t but have these expertise. It turned more and more clear that they would wish a brand new technique.
Be taught quicker. Dig deeper. See farther.
Designing an efficient AI studying path that labored with the Head First technique—which engages readers by way of lively studying and interactive puzzles, workout routines, and different components—took months of intense analysis and experimentation. The outcome was Sens-AI, a brand new sequence of hands-on components that I designed to show builders how you can study with AI, not simply generate code. The identify is a play on “sensei,” reflecting the function of AI as a instructor or teacher somewhat than only a software.
The important thing realization was that there’s an enormous distinction between utilizing AI as a code era software and utilizing it as a studying software. That distinction is a crucial a part of the educational path, and it took time to completely perceive. Sens-AI guides learners by way of a sequence of incremental studying components that get them working with AI instantly, making a satisfying expertise from the beginning whereas they progressively study the prompting expertise they’ll lean on as their growth expertise develop.
The Problem of Constructing an AI Studying Path That Works
I developed Sens-AI for the fifth version of Head First C#. After greater than twenty years of writing and educating for O’Reilly, I’ve realized lots about how new and intermediate builders study—and simply as importantly, what journeys them up. In some methods AI-assisted coding is simply one other ability to study, but it surely comes with its personal challenges that make it uniquely tough for brand new and intermediate learners to choose up. My objective was to discover a solution to combine AI into the educational path with out letting it short-circuit the educational course of.
Step 1: Present Learners Why They Can’t Simply Belief AI
One of many greatest challenges for brand new and intermediate builders making an attempt to combine AI into their studying is that an overreliance on AI-generated code can truly stop them from studying. Coding is a ability, and like all expertise it takes follow, which is why Head First C# has dozens of hands-on coding workout routines designed to show particular ideas and methods. A learner who makes use of AI to do the workout routines will wrestle to construct these expertise.
The important thing to utilizing AI safely is belief however confirm—AI-generated explanations and code could look appropriate, however they usually include refined errors. Studying to identify these errors is crucial for utilizing AI successfully, and growing that ability is a crucial stepping stone on the trail to changing into a senior developer. Step one in Sens-AI was to make this lesson clear instantly. I designed an early Sens-AI train to exhibit how AI might be confidently improper.
Right here’s the way it works:
- Early within the ebook, learners full a pencil-and-paper train the place they analyze a easy loop and decide what number of instances it executes.
- Most readers get the proper reply, however after they feed the identical query into an AI chatbot, the AI virtually by no means will get it proper.
- The AI sometimes explains the logic of the loop effectively—however its last reply is virtually all the time improper, as a result of LLM-based AIs don’t execute code.
- This reinforces an essential lesson: AI might be improper—and typically, you might be higher at fixing issues than AI. By seeing AI make a mistake on an issue they already solved appropriately, learners instantly perceive that they’ll’t simply assume AI is correct.
Step 2: Present Learners That AI Nonetheless Requires Effort
The following problem was educating learners to see AI as a software, not a crutch. AI can clear up virtually all the workout routines within the ebook, however a reader who lets AI do this gained’t truly study the talents they got here to the ebook to study.
This led to an essential realization: Writing a coding train for an individual is strictly the identical as writing a immediate for an AI.
In truth, I noticed that I might check my workout routines by pasting them verbatim into an AI. If the AI was capable of generate an accurate resolution, that meant my train contained all the data a human learner wanted to resolve it too.
This was one other key Sens-AI train:
- Learners full a full-page coding train by following step-by-step directions.
- After fixing it themselves, they paste your entire train into an AI chatbot to see the way it solves the identical drawback.
- The AI virtually all the time generates the proper reply, and it usually generates precisely the identical resolution they wrote.
This reinforces one other crucial lesson: Telling an AI what to do is simply as tough as telling an individual what to do. Many new builders assume that immediate engineering is simply writing a fast instruction—however Sens-AI demonstrates {that a} good AI immediate is as detailed and structured as a coding train. This offers learners a right away hands-on expertise with AI whereas educating them that writing efficient prompts requires actual effort.
By first having the learner see that AIs could make errors, after which having them generate code for an issue they solved and evaluate it to their very own resolution—and even use the AI’s code supply of concepts for refactoring—they achieve a deeper understanding of how you can have interaction with AI critically. These two opening Sens-AI components laid the groundwork for a profitable AI studying path.
The Sens-AI Strategy—Making AI a Studying Device
The ultimate problem in growing the Sens-AI strategy was discovering a means to assist learners develop a behavior of participating with AI in a optimistic means. Fixing that drawback required me to develop a sequence of sensible workout routines, every of which provides the learner a particular software that they’ll use instantly but in addition reinforces a optimistic lesson about how you can use AI successfully.
One among AI’s strongest options for builders is its skill to clarify code. I constructed the subsequent Sens-AI aspect round this by having learners ask AI so as to add feedback to code they simply wrote. Since they already perceive their very own code, they’ll consider the AI’s feedback—checking whether or not the AI understood their logic, recognizing the place it went improper, and figuring out gaps in its explanations. This gives hands-on coaching in prompting AI whereas reinforcing a key lesson: AI doesn’t all the time get it proper, and reviewing its output critically is important.
The following step within the Sens-AI studying path focuses on utilizing AI as a analysis software, serving to learners discover C# subjects successfully by way of immediate engineering methods. Learners experiment with totally different AI personas and response types—informal versus exact explanations, bullet factors versus lengthy solutions—to see what works greatest for them. They’re additionally inspired to ask follow-up questions, request reworded explanations, and ask for concrete examples that they’ll use to refine their understanding. To place this into follow, learners analysis a brand new C# subject that wasn’t coated earlier within the ebook. This reinforces the concept AI is a helpful analysis software, however provided that you information it successfully.
Sens-AI focuses on understanding code first, producing code second. That’s why the educational path solely returns to AI-generated code after reinforcing good AI habits. Even then, I needed to fastidiously design workout routines to make sure AI was an support to studying, not a substitute for it. After experimenting with totally different approaches, I discovered that producing unit checks was an efficient subsequent step.
Unit checks work effectively as a result of their logic is straightforward and simple to confirm, making them a secure solution to follow AI-assisted coding. Extra importantly, writing immediate for a unit check forces the learner to explain the code they’re testing—together with its habits, arguments, and return sort. This naturally builds robust prompting expertise and optimistic AI habits, encouraging builders to think twice about their design earlier than asking AI to generate something.
Studying with AI, Not Simply Utilizing It
AI is a strong software for builders, however utilizing it successfully requires extra than simply figuring out how you can generate code. The largest mistake new builders could make with AI is utilizing it as a crutch for producing code, as a result of that retains them from studying the coding expertise they should critically consider all the code that AI generates. By giving learners a step-by-step strategy that reinforces secure use of AI and nice AI habits, and reinforcing it with examples and follow, Sens-AI offers new and intermediate learners an efficient AI studying path that works for them.
AI-assisted coding isn’t about shortcuts. It’s about studying how you can assume critically, and about utilizing AI as a optimistic software to assist us construct and study. Builders who have interaction critically with AI, refine their prompts, query AI-generated output, and develop efficient AI studying habits would be the ones who profit essentially the most. By serving to builders embrace AI as part of their skillset from the beginning, Sens-AI ensures that they don’t simply use AI to generate code—they discover ways to assume, problem-solve, and enhance as builders within the course of.
On April 24, O’Reilly Media will likely be internet hosting Coding with AI: The Finish of Software program Growth as We Know It—a dwell digital tech convention spotlighting how AI is already supercharging builders, boosting productiveness, and offering actual worth to their organizations. When you’re within the trenches constructing tomorrow’s growth practices in the present day and fascinated about talking on the occasion, we’d love to listen to from you by March 5. You could find extra data and our name for shows right here.
