Come back tomorrow

The mechanic

Daily point caps

A daily point cap limits how many points someone can earn in a single day, even if they complete more than enough to earn them. It sounds backwards — why cap engagement? — but it is a deliberate pacing mechanic.

Why it works:

• It prevents “binge and vanish” — where someone does a huge batch of work in one sitting and then disappears for weeks, satisfied they have maxed out.
• It spreads effort across several days instead of concentrating it in one.
• It creates a reason to come back tomorrow specifically, because today’s ceiling has been reached.
• It protects the system from inflation and gaming, which matters if points are redeemable for anything real.

The pedagogy lines up too: a daily cap nudges toward a little each day rather than cramming.

I have this game I play that uses point caps to manipulate me. Most games have a limit of “energy” for doing the main play — missions, clearing boards. The energy limit seems to help curb the amount of time one spends on the screen.

Until you look up from spending an hour or two grinding the small things you can do to earn energy. And that’s not counting the fact that it is too easy to charge your account and buy more energy.

Play the game. I will. Games are an important part of our life and help us in numerous ways.

But when that energy runs out… put it down. Come back tomorrow.

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Ethical AI

Notes worth taking

Sadly, the art of notetaking is dead.

Teachers might run slides for a presentation. When this happens, you have a couple of types of students.

First, you have the student that has to copy everything from the slide. Word. For. Word. And they get upset if you go too fast for them to write.

Second, you might have a student that zones out and asks, “What happened on slide 2?” This of course only happens when you are on slide 11.

I am not advocating that we burn slide shows and watch the digital smoke rise to heaven. But what if we made them easier to digest?

This is what AI is genuinely good at — reshaping dense notes into something a student can actually study from:

• Q&A pairs. Feed in a chapter or lecture notes, get flashcards with the question on one side and a concise answer on the other, rather than copying whole sentences.
• Spaced-repetition-ready formatting. Output in a form you can drop straight into Anki, Quizlet, or a CSV.
• Difficulty tiering. Easy, medium and hard versions of the same concept, so you are not stuck re-reading one static list.
• Active recall prompts. Instead of definitions, framing cards as “why” or “what happens if” questions, which tests understanding rather than just memorization.
• Study guide structure. Organizing a topic into key concepts, then supporting details, then common exam traps — more useful than a wall of text.

My daughter is the Queen of Flashcards. She will take notes and turn them into these 3x5 pearls of paper wisdom. When she is studying, she has stacks of them. For her, writing the facts down helps her to learn them. Wait. Did anyone else hear that? SHE USED HER WRITING SKILLS TO LEARN!

Don’t just give the notes in these condensed versions. Have your scholar write them down. Use flashcards. Fill a notebook. Sticky notes are like candy to students in my experience.

Wanna be different? Give bonus points for doodles that relate to the topic. If you don’t understand what a game changer this is, you should message me.

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The read

Standing in the hallway

Variety — A Q&A with Tilly Norwood offers fewer reasons for her existence

This article feels like that kid in class who raises a really good question… and then just kind of stares at you instead of answering it. The writer keeps circling “Who is this for?” — which is honestly the right question — but leans more on vibes (uneasy, weird, slightly dystopian coffee shop energy) than actually digging into the answer. The experience with the AI is described well, and yeah, it is unsettling. But the piece stops at “this feels off” instead of pushing into “here’s what this actually means for the future of storytelling.” Honestly, I am not a fan. This was not created for me.

Where it gets interesting — almost accidentally — is in what the AI reveals about itself. It’s repetitive, a little stiff, and clearly boxed in by guardrails, which might be the strongest argument against it right now. The article hints at bigger issues — jobs, creativity, originality — but doesn’t really unpack them. It’s like opening a door to a deep conversation and then deciding, “Nah, we’ll just stand in the hallway.” They try to make it light and fun.

Bottom line: there are deepfakes out there that are almost indistinguishable from the real deal. There was a recent battle in Hollywood where actors and actresses had to legally fight to keep their faces and voices from being used in AI creations. This? This feels like a distraction.

Growing up, I would never have heard a big name actor’s voice in a commercial. Now? It seems that advertising is pulling in A-listers from blockbuster movies.

And here is this article trying to garner an emotional reaction to a low-end AI created actress. Not for me. There are too many amazing artists out there that need my support.

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Field notes

The day the AI apocalypse hit my classroom

The day the AI apocalypse hit my classroom, I didn’t start with a lecture — I started with a warning. The system had “gone live,” and it had already begun making decisions for them. On the board was a dashboard tracking their “autonomy levels,” slowly decreasing as the AI optimized everything from their problem-solving steps to their final answers.

Their job wasn’t just to complete the assignment — it was to prove they could still think. Every task became a choice: rely on the AI’s suggestion and lose a little control, or challenge it using evidence, reasoning, and actual scientific understanding. Some students immediately trusted the system, clicking through prompts like it had all the answers, while others pushed back, questioning every recommendation like they were trying to outsmart something that was just a little too confident.

What stayed with me wasn’t who “won” the simulation — it was how they responded to it. Students began debating not just the science, but the decisions behind it: why the AI chose certain solutions, where it failed, and how human thinking filled the gaps. They weren’t asking me if something was correct — they were arguing whether the AI should be trusted in the first place.

The room shifted from compliance to curiosity, from passive acceptance to active challenge. By the end, the lines between user and thinker felt a little clearer to them. And standing there watching it unfold, I realized the lesson had gone beyond content — because in a world where answers are easy to generate, the real skill is knowing when not to accept them.

But what do I know? Maybe if we don’t talk about using AI ethically, it will just go away.

If you think so, I have some ocean front property in Arizona you might be interested in purchasing.