AI Career Paths

Learning Objectives

This is the second-to-last lesson in World 6, and it turns the lesson before it — building with AI tools — into a question about the future: what happens to work once AI is everywhere? The lesson answers that with twelve concrete example careers rather than a vague "AI will change everything" pep talk, so keep the conversation anchored to those examples rather than letting it drift into generic career-day territory. By the end, a student should be able to:

• Name that AI creates careers in every field, not only programming. The lesson's three scenes deliberately move from purely technical roles (ML engineer, data scientist) to creative and social roles (AI policy, AI journalism, AI education) to AI showing up inside completely ordinary fields (medicine, environment, music, sports) — the shape of the lesson is the argument.
• Sort a given job into "builds AI technology" or "uses AI within another field." The app itself tags each of the twelve example careers this way (isAI: true/false), and Builder mode and up runs a short timed sorting activity on exactly this distinction — it's worth being able to explain why, say, "AI policy" is tagged differently from "ML engineer" even though both are unmistakably AI careers.
• Explain what an AI safety researcher does, in one sentence, and why the lesson calls it a growing field. This is the one career every age band's quiz asks about, so it's worth having a clean one-line answer ready: someone whose job is making sure AI systems behave the way they're supposed to, and don't cause harm as they get deployed more widely.
• State that the most valuable AI professionals combine AI knowledge with another skill or passion, rather than knowing only how to code. This is the lesson's single biggest idea, repeated in every age band's summary in slightly different words — a doctor, teacher, or musician who also understands AI is more valuable than someone who knows AI but nothing else.
• (Ages 15+) Apply "comparative advantage" to a career decision — identifying where a person's specific combination of skills and interests creates more value than either skill would alone, and explain why a portfolio of real projects has become at least as important as a credential when it comes to AI-adjacent hiring.

Teacher Background

You do not need a technology or careers-counseling background to teach this lesson — the app does the heavy lifting by naming twelve specific, real jobs. Your job is to be able to explain each one in a sentence and to keep steering the conversation back to the lesson's one big claim: AI is not a career track, it's a skill that shows up inside almost every career track. The lesson organizes its twelve examples into three groups, and it's worth knowing all of them before class.

The first group is technical paths — jobs where building AI systems is the actual work. An ML (machine learning) engineer builds and trains the AI models themselves — the kind of system that powers everything else in this app's curriculum. A data scientist is a related but distinct role: instead of building the model, they dig through large amounts of data to find patterns and insights, and they need to be able to explain what they found to people who aren't technical. An AI safety researcher works on making sure AI systems behave as intended and don't cause harm — testing for bias, failure modes, and unintended behavior before and after a system is deployed. A robotics engineer combines AI with physical hardware, building machines that use AI to sense their surroundings, move, and manipulate objects.

The second group is creative and social paths — jobs that shape how AI is designed, governed, explained, and taught, without necessarily writing the code that runs it. AI + design covers the people who design the interfaces and interactions between humans and AI products, plus artists and designers who use AI tools in their creative work. AI policy is the work of writing the rules, laws, and regulations that govern how AI can be built and used — a genuinely urgent and growing field as governments worldwide (the EU's AI Act is the most developed example so far, and the Philippines has its own ongoing data-privacy and AI-governance discussions) work out how to regulate a technology that changes faster than most legislation can keep up with. AI journalism means reporting on and explaining AI to the public — one of the fastest-growing beats in journalism precisely because most readers don't have a technical background and need someone to translate. AI education means teaching AI literacy at any level, from kindergarten through university — worth pointing out to your class that the app they are using right now is a product of exactly this career path.

The third group, "every field + AI," is the lesson's clearest illustration of its central claim: AI shows up inside fields that have nothing to do with computers on the surface. Doctors who understand AI-assisted diagnosis are positioned to give better care. Climate scientists use AI to model environmental systems that are too complex to analyze by hand. Music producers and sound designers increasingly work with AI-assisted composition and mixing tools. Sports teams hire AI analysts to optimize training, predict injuries, and plan strategy. None of these four people would describe themselves as "working in AI" the way an ML engineer would — but all four now need to understand it to do their existing job well.

For your oldest students, two more precise terms are worth having ready, since both appear directly in the Architect quiz. Comparative advantage is an economics idea — originally about trade between countries, now commonly applied to careers — that says the most valuable thing a person can do isn't necessarily the thing they're best at in an absolute sense, but the thing where their specific combination of skills creates the most value relative to everyone else's options. Applied to an AI career, this is the T-shaped idea taken further: your comparative advantage might not be being the best programmer in the room, but being the person who understands both AI and, say, agriculture, in a room full of people who only understand one or the other. An alignment researcher is a real and current job title — someone whose work is specifically making sure an AI system's behavior matches what its designers and users actually intend, which is a harder and more open-ended problem than it sounds, and a distinct research area from simply making a model more capable.

Philippine Career Context

The app's twelve example careers and salary figures are drawn largely from the U.S. tech industry — and that's the biggest gap in this particular lesson. "Learn AI, then get a $150K job in Silicon Valley" isn't a plan most of your students can follow. What follows is the local detail the app itself doesn't give you, so you can answer the question every student is actually asking: what does this look like for me, here, in the Philippines?

Where to study, for the technical path. Several Philippine universities offer strong programs leading toward roles like ML engineer or data scientist: the University of the Philippines (UP Diliman's BS Computer Science and BS Statistics are both well-regarded, and UP now offers data science at the graduate level), Ateneo de Manila University (BS Computer Science, with close ties to the local startup scene), De La Salle University (BS Computer Science and Software Technology), and Mapúa University (long-established engineering and IT programs, including data-science tracks). These aren't the only path in — plenty of working developers here are self-taught or came from other schools — but naming a few real programs turns "study computer science" from vague advice into something a student can look up tonight.

The BPO-to-AI pathway. The Philippines is one of the world's largest hubs for business process outsourcing (BPO) — call-center and back-office work for companies abroad, employing well over a million Filipinos, many of them fresh graduates. That industry is visibly shifting: pure voice customer service is increasingly layered with AI, and major employers here (Accenture, Concentrix, Teleperformance, TaskUs, and IBM all run large PH operations) are hiring for AI-adjacent roles on top of the traditional BPO skill set — AI-assisted quality monitoring (using AI to flag issues in call transcripts instead of a human reviewing every call), data annotation and labeling (the human work of tagging the examples that train AI models, a job category that exists directly because of the AI boom), and analytics roles that turn customer data into insight. This matters because it isn't a hypothetical future career — it's an existing, hireable pathway that a large share of your students' own relatives, or the students themselves, are realistically positioned to walk into.

Remote work for foreign employers. A growing number of Filipino developers, data scientists, and other AI-adjacent tech workers are hired directly and remotely by companies based outside the Philippines, rather than through a local employer or a BPO. It's worth naming to students as a distinct third option alongside "work for a Philippine company" and "move abroad" — a path more Filipino tech workers are genuinely taking today.

Materials & Prep

Nothing beyond AIQ's usual requirements: one device per student (or per small group) with a modern browser and an internet connection to open aiq.ph. No login, no printouts, and no advance setup — the lesson runs in one 5–15 minute sitting inside the app. If you plan to run the discussion questions as a full class conversation, budget an extra 5–10 minutes.

For Builder mode and up, the in-app lesson includes a short timed sorting activity where students sort job-related tasks into "AI" or "not AI" as quickly as they can. This runs automatically after the main scenes, needs no extra prep, and works fine solo or in pairs on one device — it's a good moment to pause and talk through a couple of the trickier sorts together (journalism writing and teaching kids are both "not AI" in the activity, even though both jobs increasingly involve AI tools, because the underlying task — writing a story, connecting with a student — is still fundamentally human work).

No specialized vocabulary needs pre-teaching for Explorer or Builder. For Hacker and Architect classes, it's worth previewing "T-shaped" and, for the oldest students, "comparative advantage" before starting — both quizzes for those age bands build directly on that vocabulary.

Common Misconceptions

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