I was struggling to find a list of practical interview-ready AI skills that every graduating undergraduate should be able to demonstrate. So I created one.
One competency that is an issue right now (Claude throttling) and ought to be part of the fundamental skillset is token/compute conservation. It is easy enough to throw a ton of junk into an AI thread and drag the dialog through 100 exchanges, but it comes at a terrible (mostly invisible) cost. Turn off unused Skills! Summarize and restart.
Using tokens efficiently saves compute and improves performance. You could even classify this as an ethical issue.
Good list. You may want to check out what businesses are already looking for when hiring. For example, here is what Zapier considers the minimum fluency bar for getting a job with them: https://zapier.com/blog/raising-ai-fluency-bar-in-hiring/ We are past the point of "know how to use different AI tools well" and getting into a world where the expectation is embedding AI into our existing workflows in repeatable ways (rather than one off prompts) and demonstrating quality/efficiency improvements.
I love your list, Andrew, but I wonder where ethics and perhaps legal issues might fit. Students need to be able to ethically assess the work within their contexts and make choices or pivot as needed. A couple of the points "hint" at this, and #12 starts to point in that direction, but I'd like it stated more explicitly.
Thanks Amy - of course always a balance knowing how much to lean into this in what context. Despite my work on AI ethics and responsible AI I decided to fold it into the skills in a way that would avoid students taking a stance in interviews rather than demonstrating skills. But of course this is why I wanted the feedback 😀
Excellent! Perfect timing! As we need to move from AI literacy to AI fluency, it is very important to pinpoint in detail what AI literacy is, in a specific and measurable way.
Andrew, my structure is probably too complicated (below). However, I find it extremely helpful for course development and training projects. Sometimes, I use an AI Literacy self-efficacy scale to better understand where my incoming students stand. With proper attribution, I will borrow your infographic to show that I am not the only freak professor in town :-)
One competency that is an issue right now (Claude throttling) and ought to be part of the fundamental skillset is token/compute conservation. It is easy enough to throw a ton of junk into an AI thread and drag the dialog through 100 exchanges, but it comes at a terrible (mostly invisible) cost. Turn off unused Skills! Summarize and restart.
Using tokens efficiently saves compute and improves performance. You could even classify this as an ethical issue.
Absolutely! Missed this one - but really important.
https://hemmani.substack.com/p/what-if-ais-greatest-limitation-is?utm_source=share&utm_medium=android&r=87plup
As I was reading through I kept thinking "You can use AI to learn how to use AI for many of these things" and then there it was at "14"!
Good list. You may want to check out what businesses are already looking for when hiring. For example, here is what Zapier considers the minimum fluency bar for getting a job with them: https://zapier.com/blog/raising-ai-fluency-bar-in-hiring/ We are past the point of "know how to use different AI tools well" and getting into a world where the expectation is embedding AI into our existing workflows in repeatable ways (rather than one off prompts) and demonstrating quality/efficiency improvements.
Thanks Boris - this is a great resource!
I love your list, Andrew, but I wonder where ethics and perhaps legal issues might fit. Students need to be able to ethically assess the work within their contexts and make choices or pivot as needed. A couple of the points "hint" at this, and #12 starts to point in that direction, but I'd like it stated more explicitly.
Thanks Amy - of course always a balance knowing how much to lean into this in what context. Despite my work on AI ethics and responsible AI I decided to fold it into the skills in a way that would avoid students taking a stance in interviews rather than demonstrating skills. But of course this is why I wanted the feedback 😀
Excellent! Perfect timing! As we need to move from AI literacy to AI fluency, it is very important to pinpoint in detail what AI literacy is, in a specific and measurable way.
Thanks Val!
Andrew, my structure is probably too complicated (below). However, I find it extremely helpful for course development and training projects. Sometimes, I use an AI Literacy self-efficacy scale to better understand where my incoming students stand. With proper attribution, I will borrow your infographic to show that I am not the only freak professor in town :-)
https://doi.org/10.5281/zenodo.18714923