{"id":28111,"date":"2025-10-22T01:01:00","date_gmt":"2025-10-22T05:01:00","guid":{"rendered":"https:\/\/www.learningguild.com\/?p=28111"},"modified":"2025-10-29T19:00:27","modified_gmt":"2025-10-29T23:00:27","slug":"ai-is-not-the-future-of-learning-development","status":"publish","type":"post","link":"https:\/\/www.learningguild.com\/articles\/ai-is-not-the-future-of-learning-development","title":{"rendered":"AI Is NOT the Future of Learning & Development"},"content":{"rendered":"\n

By Dr. Amos Glenn<\/strong><\/p>

Advertise Here<\/a><\/div>\"Earn<\/a><\/div>\n\n\n\n

\u201cYou should really be using AI<\/a>.\u201d<\/p>\n\n\n\n

If you\u2019re an instructional designer, you\u2019ve probably heard that from a boss, a vendor, or a gushing article on LinkedIn. The pressure is real. This article is my pushback.<\/p>\n\n\n\n

I\u2019m not against technology. I\u2019ve been excited about its potential for learning since I wrote my first educational game in 1982 (you earned a point for every multiplication problem you got right; not much of a game, but I was nine and the computer had 16KB of memory).<\/p>\n\n\n\n

I\u2019m pushing back against AI for one reason: AI isn\u2019t the future\u2014it\u2019s the present and the past<\/a>. We\u2019ve been using the parts that work for years\u2014we just called it \u201cpredictive text\u201d or \u201cadaptive feedback\u201d (or \u201cFurby\u201d). What\u2019s being hyped as AI today is usually a loose collection of features looking for a purpose and investors.<\/p>\n\n\n\n

Microsoft\u2019s Immersive Reader is a perfect example. It was created at a 2015 hackathon by mashing up existing Microsoft tools. The tech never changed, but in 2019\u2014right when early GPT chatbots were making headlines\u2014it was suddenly rebranded as an \u201cAzure Cognitive Service,\u201d which was later renamed the \u201cAzure AI Foundry.\u201d Same code; new costume.<\/p>\n\n\n\n

Educators aren\u2019t here to impress investors. We\u2019re here to build learning experiences that make a difference. To help us cut through the hype surrounding AI, I\u2019d like to share four rules I derived from human-centered design (HCD) to guide my own decisions about using AI (or VR, AR, etc.). This isn\u2019t a new framework or a provocation. The rules are a lens that cross-functional teams can use to focus design decisions on learners, rather than flashy features.<\/p>\n\n\n\n

1. Solve the real problem<\/h2>\n\n\n\n

AI isn\u2019t snake oil, but when something is being pitched as a solution for everything from low engagement to high development costs, it\u2019s prudent to ask, \u201cWhat really is our problem?\u201d What I\u2019ve noticed is that AI tools typically promise to solve business problems, not learning problems. While both matter, we contribute to the bottom line and the quarterly growth target by solving learning problems: helping people gain the knowledge or skills they need to do something they care about.<\/p>\n\n\n\n

Some confusion about the real problem comes from the way we often measure outcomes<\/a>. Because measuring learning directly is so tricky, we regularly measure proxies instead (such as engagement, satisfaction, and completion). These proxies yield critical insights into the health and efficacy of the learning environment, but are not evidence of learning itself.<\/p>\n\n\n\n

What\u2019s counterintuitive is that addressing a proxy directly will likely muck it up. For example, if you try to bump up your engagement numbers by adding an AI chatbot, you no longer know if the bump reflects genuine improvement in the module\u2019s relevance, clarity, and challenge, or just the chatbot\u2019s novelty.<\/p>\n\n\n\n

Instead, solve the real problem by finding out what is getting in the way of your learners\u2019 engagement. It\u2019s the difference between solving a business problem with learning and merely improving a business metric.<\/p>\n\n\n\n

2. Prefer needs over requirements<\/h2>\n\n\n\n

Remember the hype around the Segway, the scooter that would reshape cities the way the PC reshaped computing? It had cutting-edge technology, heavyweight backing, and lots of buzz.<\/p>\n\n\n\n

The only thing it didn\u2019t have was a reason to buy one. It was an innovative product that nobody needed because the Segway was designed to meet producers’ requirements, not the users\u2019 needs.<\/p>\n\n\n\n

AI is similarly an innovation looking for someone to need it. The hype is about how powerful AI is becoming and how you can\u2019t afford not to use the latest AI tools. There is less talk about which needs are being addressed. The result is an ill-defined fear of missing out that is increasingly causing AI to be listed, usually in some nebulous form, among the requirements for new learning products (and job descriptions) without considering the needs of learners<\/a>.<\/p>\n\n\n\n

For educators, though, \u201cconsidering the needs of learners\u201d is central to our work. Learning occurs in one place and time only: inside the learner, when learning meets one of the learner\u2019s needs.<\/p>\n\n\n\n

No requirement can mandate learning. Hopefully, your team can work together to align requirements with needs. However, if you find ambiguity or conflict between the two, verify that the requirement is actually required and not merely a hedge against missing out.<\/p>\n\n\n\n

3. Apply systems thinking<\/h2>\n\n\n\n

The world is more complex than we like to admit. We naturally limit our thinking to simple cause-and-effect relationships: you take an allergy pill and the sneezing stops. But you may also fall asleep at your desk.<\/p>\n\n\n\n

One action will have multiple, often unintended, effects. Learning is a complex system. Adding something to a learning experience over here causes something that feels unrelated to pop up over there. Applying systems thinking means including as many of those effects as possible when making decisions.<\/p>\n\n\n\n

AI only makes the system more complex, especially when vendors can\u2019t tell you how their AI works. It may help you personalize content for more effective learning, but it may also inadvertently perpetuate the biases in the AI\u2019s training<\/a> or reduce accessibility.<\/p>\n\n\n\n

Complexity is unavoidable, but if you aren\u2019t thinking about the side effects of a feature, you end up playing whack-a-mole with learning problems.<\/p>\n\n\n\n

4. Prioritize the audience over the show<\/h2>\n\n\n\n

This rule is a gut check. Who benefits from this feature: the learner or us? There\u2019s no shame in wanting to create something impressive, but don\u2019t confuse that with the mania surrounding anything \u201cpowered by AI.\u201d Designing for affect over effect is just theatrics.<\/p>\n\n\n\n

I call this the \u201cAvatar Trap.\u201d Yes, it was ground- and record-breaking, but has anyone ever quoted Avatar to you? Meanwhile, my kids yell, \u201cI\u2019m walking here!\u201d without even knowing Midnight Cowboy exists, and finishing the quote, \u201cLife is like a box of…\u201d is practically a reflex.  Spectacle fades no matter how many awards it receives. To make a lasting impression, you need to resonate with the audience.<\/p>\n\n\n\n

Learning works the same way. Adding AI-generated variations of \u2018correct\u2019 or \u2018wrong\u2019 might impress a committee, but not a learner. Microsoft\u2019s Immersive Reader, on the other hand, quickly became the Princess Bride of edtech\u2014no flashy features and no marketing at all, just creators laser-focused on an audience they understood and cared about.<\/p>\n\n\n\n

How to use these rules<\/h2>\n\n\n\n

If your job is solving business problems with learning, the value of your work begins and ends with the change it facilitates in your audience. Human-centered design (HCD) is a mindset for making decisions, whenever possible, based on the needs and values of the human beings we are designing for.<\/p>\n\n\n\n

These four rules are a handy HCD lens that cross-functional teams can use to cut through hype and reduce pressure from more peripheral factors. The next time you\u2019re asked to use an AI tool or your team\u2019s discussion turns to using the latest tech, pause and unpack your reasoning by asking yourself and your team:<\/p>\n\n\n\n