As I read online, I bookmark resources I find interesting and useful. I share these links about once a month here on my blog. This post includes links related to how AI is affecting entry-level jobs, generating and using AI images, an AI literacy framework, and rebuilding an old simulation with AI.
How AI is affecting entry-level jobs
The Perils of Using AI to Replace Entry-Level Jobs | Harvard Business Impact Education
One theme I see coming up repeatedly in L&D conversations is that using AI requires expertise to evaluate AI-generated results. That’s fine right now where we have experienced people who built skills pre-AI. But what about entry-level workers now and in the future? If those entry-level jobs are eliminated, how will the next generation learn those skills and judgment? This article has some ideas about restructuring work. This also points to some areas where L&D could help support people whose jobs are redefined and restructured. Training people to question and evaluate AI output and apprenticeships are two possibilities to address the challenges.
Imagine recruiting managers who have never worked at the front lines, never handled customer complaints, never written up notes from consequential meetings, never grappled with the minutiae of operational work. Leadership would become abstract, detached, and dangerously naive.
Junior roles must no longer be defined by the repetitive, automatable tasks that AI can do better and faster. Instead, they should be designed to expose people to the why behind the work.
AI is only useful when paired with critical thinking. Productivity gains are meaningless if they come at the expense of professional judgment.
The default use of AI is substitution: Let the machine do the work and cut headcount. A smarter approach is to redesign workflows, so AI handles rote execution while humans focus on framing the problems, asking better questions, and building relationships.
Consider the analogy of education: If a student outsources every essay to generative AI, they bypass the intellectual struggle that produces deep learning. It is like microwaving ideas: fast, convenient, and unsatisfying. The effort, even the pain, of thinking for yourself is what builds a student’s capacity.
—All quotes above by Amy C. Edmondson and Tomas Chamorro-Premuzic
Is AI closing the door on entry-level job opportunities?
AI will result in both job losses and opportunities, changing the career ladder that used to provide entry-level opportunities and a mostly linear path upward. But eliminating entry-level roles will dramatically affect the long-term talent pipeline.
AI images
Three Steps for L&D Professionals to Legally Use AI Images and Video
Debbie Richards shares tips for using AI image and video legally. Personally, I’d probably add a few other image tools to that list of professional options (like Flora and Freepik), but she’s correct that Firefly is the safest for images based on training on licensed data. Free tools are fine to experiment with, but don’t use them for commercial projects.
Simple Ways to Create Consistent Characters in ChatGPT
Tom Kuhlmann shares his workflow for generating consistent character images using ChatGPT using a GPT or a project with saved instructions. Even if you don’t use ChatGPT for image generation, the descriptions of image styles in the download are useful for working with any tool.
The AI Image Generation System for Learning Designers
Despite the article title of “How to Get Consistent, On-Brand Course Images from Any AI Image Tool,” this system doesn’t actually work with all AI image tools. The process is different with Midjourney, Recraft, Brushless, Flora, etc. But this process will work with any of the LLM-based image tools like Nano Banana, ChatGPT, and Copilot, since those all work in similar ways. That means it covers what most people have access to. The character reference images are key to getting scenes with consistent characters in LLM-based image tools.
The fix is an 3-step process which gives you superpowers in AI image generation:
Write a visual brief — answer six questions that close the creative and pedagogical gaps before you generate a single image.Build a mood board — gather images that capture the lighting, energy, and environment of your learner’s world. Select the 3 that look like they were shot by the same photographer on the same day and upload them individually as style references.Create character anchors — your style references fix the visual world; your character references fix the people inside it. For each named character, generate a head-and-shoulders image on a neutral background, facing forward. This is your master reference. Attach it alongside your style references every time you generate a scene featuring that character — and the tool stops making casting decisions on your behalf. —Dr. Philippa Hardman
AI literacy framework
AI Literacy Framework updated April 2026
Stella Lee has updated her AI Literacy Framework to include 8 interconnected domains covering foundational knowledge, critical practice, and responsible leadership.
Rebuilding simulations with AI
I Rebuilt a 10-Year-Old Simulation with AI in Half a Day. Here’s What I Learned.
Trina Rimmer shares her experiences rebuilding an old Rise scenario activity as a new experience with Claude. You can see the projects side-by-side to compare. Trina’s reflections add a lot of value here; you can see how the AI tools enabled her to do something new, but that was only possible because of her existing instructional design skills. If she didn’t have that expertise, then she couldn’t have gotten as successful a result with AI.
And then there was Claude’s desire to help. It was relentless. I pushed back constantly on anything that broke the “fourth wall,” anything that handed the learner an insight before they’d reached for it themselves, and anything that sounded like a training exercise instead of a real situation. Productive struggle is where learning happens.
AI is a design collaborator, not a design replacement. Every meaningful improvement in the rebuilt project came from my instructional design judgment, not from Claude’s defaults. The dialogue, the model interaction structure—me insisting that learners identify what worked before being told—and the debrief—me pushing back on Claude’s first version until it asked more than it told—that was all me. Claude made those things possible faster, but without the ID judgment driving the prompts, the end result would have been slicker but shallower.
—Trina Rimmer
Additional curated resources
Check out my complete library of links or my previous bookmarks posts.
