AI & L&D – Good Stuff In for Good Stuff Out

AI knows what it knows – and doesn’t know what it doesn’t know.

It knows what it’s been deliberately fed – and what it can find and make sense of.

I have nothing against AI in L&D – as I see any resistance as futile.

It’s not just coming – it’s already here.

My worry is that the way it is currently being promoted is troublesome. Is it a recommendation engine with Worthy Recommendations – or just Some Recommendations – and as always – Caveat Emptor?

If the goal is to guide a Learner to Worthy Instruction – inclusive of both Performance Support and Learning Experiences – that will Transfer back-to-the-job and have a Positive Impact on the business metrics – what comes out will be only as good as what has gone in, no?

To make the L&D Effective for the Learners/Performers – it needs to address their authentic Output & Task requirements, and include the enabling Knowledge/Skills for those Tasks & Outputs – and generic Topics and Behaviors and Tasks – will force the Learners/Performers from Formal Learning to Informal Social and/or Trial & Error Learning.

That might ultimately be effective – but it will not be efficient.

If you want the L&D to be efficient it should zero in on what the Learners’/Performers’ specific assignment includes, Output & Task-wise, as not everyone who shares a Job Title has the same role and responsibilities. And then the “AI in L&D” Recommendation Engine needs to account for the incoming Knowledge & Skills of each Learner/Performer so as to avoid recommending L&D/Instruction that they already know from Education and/or Experience.

Otherwise, it will be very inefficient and an unnecessary waste of Shareholder Equity.

AI in L&D needs to know the Performance Requirements.

AI in L&D needs to know the enabling Knowledge/Skills per Output-Task cluster.

AI in L&D needs to know each Learner’s/Performer’s specific Job Assignment.

AI in L&D needs to know each Learner’s/Performer’s incoming Knowledge/Skills.

There’s just no avoiding the Analysis data and efforts required to make the “AI in L&D” Recommendation Engine helpful.

Otherwise, it’s just an expensive – and an automated – ruse – to recommend generic content from generic libraries that need to be pushed hard – as their potential impact is dubious, IMO.

There’s just no avoiding the Analysis data and efforts required.

A GIF

My recent book on Instructional Analysis (March 2022). This is from my min-book series. I have several other books that include this content – and other content that addresses various aspects of Analysis – Design – Development of performance-based Instruction – inclusive of both Performance Support and Learning Experiences.

See all 28 of my books on my Amazon Authors Page: https://www.amazon.com/-/e/B08JQC4C4V

###

Leave a Reply

Please log in using one of these methods to post your comment:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.