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In the case of artificial intelligence (AI), certainly one of the primary things I take into consideration is the longer term. We speak about AI being the longer term and the way it is going to help us shape the longer term. I did an AI experiment recently that jogged my memory after we talk in regards to the future, we can’t forget the past.
I used to be working on some revisions to a training program recently and I felt the content needed a brand new icebreaker. I assumed, “Hey! I’ll ask AI to present me an icebreaker suggestion.” So, I asked GPT-4o (developed by OpenAI) and Gemini (developed by Google) the identical query:
Tell me an icebreaker exercise to make use of with 20 skilled adults in a virtual training program.
- GPT-4o suggested “Two Truths and a Lie”. This activity asks participants to provide you with three statements about themselves. Obviously, two of them are true and one is … a lie. The remainder of the group takes turns guessing which statement is the lie.
- Gemini suggested “Would You Quite?”. On this activity, the facilitator comes up with questions that ask, “Would you relatively do A or B?” and participants share their responses. The group can discuss their decisions.
Each AI programs provided instructions for conducting the icebreaker activity. In the event you’re interested, we’ve put them in a single page PDF that you would be able to try here.
It was interesting to see how each program responded to the prompt. Gemini included an inventory of materials including a mention of Google Docs. It also highlighted the advantages of using this activity – engaging, low pressure, and divulges preferences. GPT-4o included timing for every step within the activity and added some suggestions for working with a big audience.
But neither of them created anything recent. Each icebreaker activities have been around for a long time. They will not be original. I’m not saying either certainly one of them is bad. I’m saying that I used to be surprised that AI suggested something so … well, old. It was an academic experience for me for a number of reasons.
- AI remains to be learning. One in every of the things we proceed to listen to about AI is that it must learn. That’s why we see organizations changing their terms of service and/or asking us to opt in in terms of their AI tools and learning.
- We want to have the proper expectations. Possibly things that were developed up to now are classics and exactly the proper things to suggest. I also can see how some things that were developed years ago could be old and outdated, so that they may not get beneficial.
- We want to judge the data provided by AI. That is where humans and AI will work together. AI doesn’t know every little thing, like your training audience. These icebreaker suggestions might be great, or they might be a disaster depending in your participants.
Just in case you’re wondering … while we appreciated the AI suggestions, for this particular training program we were working on we stuck with the classic “interview your partner” icebreaker. Each AI-suggested icebreakers did include a reminder about keeping comments skilled, light, and positive to avoid any discomfort.
This AI experiment made me realize that AI and I are learning together. And since we’re, there could come times when I actually have to regulate my expectations about what AI can and can’t do. That’s a superb thing for each of us.
Image captured by Sharlyn Lauby while exploring the streets of Chicago, IL
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