Il & D reflections Conference: Ai vs. Mankind

What L & D’s title?

I’ve been talking about L & D conferences for over ten. I always had the same way of learning: discussing industry leaders in what they do in reality, addressing the participants. This article (one of 2) checked the common methods from the ATD LearningtoLogies Conference.

Even the Tech formation conference is about people

This year, the total of ATD’s Technowm Monfel can be summed up on the missing shirt:

MANHY · MANDER (be both of us)

We, people, is complex. Some of us are very wise 🙂 How we treat each other, especially those who may agree with us, play a major role that artificial intelligence (AI) training. After all, AI is trained in human details; Humans are, that is. Mankind is about stories of hidden people. Talk to people. Listen to their stories. You never know you have come to know the rest of their story. Be kind. Don’t expect the technology (even ai) to resolve your people’s story.

Why is all this time the end?

Due to the initial display below: Find and understand the right problem to resolve, and look at the solution (AI or not). Besides, you will get tired and frustrated about how quick a tool changes in your hand while you are running problems that they can solve. Many people fight today are in line with changes, especially in the Ai. Donald H. Taylor’s “” Hot to L & D “Surveys Indicate the same: AI is more than mental. [1]

Conference of conference

1. Many people are tired and frustrated

The speed of change is painful. Many people lose energy by reading articles, listening to podcasts, or watching tiktoks. At present you read, show, apply, and share the latest, may not work. Say just a few books in Ai.

AI also has negative effects of existing technology. View the EDTECH! They know they are behind the curve if they do not say they have AI. Content creation is a simple login place. The original Fameters began using live chat features to use AI. Today, you can talk to the good 3D letters. In a few years, this feature will be all good to learn a respected.

Permanent change for making decisions: We have to wait until the full-set aspect is right, or should we start “hacking” the current version? Creating the Apis and the use code can be a technical debt for a few years when traders bring their own.

• How to reduce the tiredness of change
• • Know that you are not alone
    While in the holes of your organization, it may feel like age, and everyone in Lickerin is an expert in Ai and L & D. Talk to others. The network. Go to the conference. You will see that everyone has the same problems.
  • You can’t do everything: Preaprpecte
    You will not be king of all trading. That period is gone. They prioritize what is important. What is important to you, in your group, and your organization?
  • Start With the Problem
    Most of your challenges did not change. Because you can now have technology that can solve the wrong problem to resolve, it is not worth it.
  • Follow others in three circles
    • Circle
      People in the Watchtower Have Similar challenges
    • Gathering
      People who are six months – one year before you want to be; People who impart you to focus on a short time.
    • The circle is inspiration
      People up to the coming years; Those dealing with the Tsunami of New Information, Research, and technical changes. Use them as a buffer. Let them work on what is important and what is disturbing.

2. To remove AI: Back to Moral Modes?

We have issued a Co-ploot driver, and no one is using.

This recycling article reminds me of my work when the company “issues” the profiles where employees should complete their details for them, interests, etc. The result? The rate of income. Solution? A valid HR authorization: People must complete their profile.

You do not issue technology as a red carpet. People are not from events because there is a red carpet. They walked in a red carpet because the event exists.

Make a “event” meaningful, and they will come. The same for AI: Yes, you need to be treated for change. The management of behavior changes, that is. There is a perfect science after that! The learning design is not enough to change the minds. Start automatic determination, bj foggg model, com-b, or similar foundations. As for the building of your issuance system, here is a practical issue of the Game Design World:

• Section 1: Determined
  Find people, problems, and procedures when challenges are available. Show the potential value! Ask them to see the future, where you go, first, before I give steps to step instructions.
• Article 2: Examination
  Received the first purchase. Motive is high; The experience is low. This is the first time to use AI. May require hand-generated transport and sensitive errors. They gave the victory original! Any little thing that works as a progress. Provide basic data and AI writing.
• Section 3: Scaffolding
  They now use tools to solve their problems. Promotation diminishes as they have a powerful power, but experience and skills to becoming increasing, which helps them to continue involvement. Support where necessary, but do not hold everyone in formal live. Let them build and share solutions. Critical challenges with support tools and materials.
• Stage 4: Mastery (and beyond)
  You have players. Working for workmates (“I see me doing this well”) Solve the new challenges. Connection and relationship that makes up a solution. Provide continuous support (this partnership; you will not be an expert in everything). Experts can help find new staff based on lessons learned. Communities can support them. The best habits can be stored and stolen within ai system (no need for the other Sharepoint-site).

3. UPSKilling in AI: Where to start?

One of the most common challenges mentioned at the conference went up workers on a scale. I often find the word “climb” and the word “to close the miscondition of” misleading skills. What do you need to make up someone from the level? Three Things: Level where available, quality where you want to be, and the shortest way to connect this two.

But in some way, we often focus on the desired position. Besides knowing where people are, we build when we go and one road to us. Then, forcing everyone to return to the end of the road to start the trip no matter where they are.

The “if I don’t see you, no policy” is not a good thing to rely on. Employees use their personal access to AI to help them solve problems and return them to work. It may not be a higher secret (hopefully), but there is something much consideration there. What is the company’s feedback? Blocking the attached copying from outside. Yes, there is an email.

Create a policy that provides critical risk awareness. Then, you can think of the SUPSKILLING, including how to check the current skills to enable a short way.

4. Do the quick engineer worth reading?

Last year, rapid engineering is one of the most hot skills. Take ahead and today, we have thousands of acrhrons and “how to write appear. You can even ask your favorite llm to produce quickly. Because the generative AI uses the environmental processing, quitting is not a “engineering” in the native sense. No code is required.

Two cents that you have to learn why, not acronyms or templates. If you understand why you need to give someone context, it is easy to get used to. One thing as well: These types continue to appear. What you learned about them last year may not have to do not have to do. Focus on the definition of a problem, instead of a proposed format and format from the project based on the success of the past year.

I always give a time consumption of time, think, and double check the best and recent answer. Before there, it usually suggests the first, popular, for example, in the codes, and then there is just reduced. Also, remember you can request a model to update the answer. Repeatedly. People wouldn’t tolerate that, but Ai enjoys doing.

5. Ai-active use of AI (for L & D)

The most widely used case I saw is a desired generation. Immediate, instant text and content content to solve the effective content. Health is more healthful means that we can create more content for little hard work. The challenge is sleeping on the other side of the balance of the balance: efficiency. Creating further content immediately does not mean that we make a big impact on work. In fact, this works well often leads to “free time” that is eaten by additional content. I think we should use AI in the opposite way: reducing the content.

In our program, we share effective AI: training, training, skills, skills, and construction of the negotiations of Ai-Cho-Cho-Choice Responses).

Without reading, check some cases of using AI in business [2]. In the next article, I will continue with the next five themes from the Conference:

1. Available: Who is caring for others?
2. Waiting for gormon?
3. Tech cooperation does not use them, people do
4. Different thoughts, better result
5. In a personal book: To stay next door and Alice. Alice? Who [beep] Is Alice?

References:

[1] International Symbol survey 2025

[2] Artificial Intelligence (AI) Use Apps and Apps