“Let’s get our students to be self-regulated or self-teaching” has been a common request of groups I’ve worked it.

Ed-Technical May 6th, 2025‘s episode explored this topic, and it got me thinking about how we can apply those same ideas to mental performance—and where AI might fit in.

Self-regulation or self-teaching —the idea that learners can take control of their own growth by setting goals, monitoring progress, and adjusting strategies as they go – is a powerful framework that applies to everything from learning a language to building leadership skills. However, it has its challenges: learning is often social and emotional and self-regulated often is confused with “learning alone.”

I’ve seen it work when we borrow lessons from the physical world—like sports.

Why Self-Regulation Is Easier in Physical Training

Self-regulated learning feels intuitive in sports for a few reasons grounded in visibility and feedback:

• You have a clear vision of success. Athletes often have concrete, observable models to aim for—like mimicking a professional’s technique or hitting a measurable time or score. These serve as exemplars that make the goal feel tangible.
• You can measure your performance. Whether it’s lap times, reps, accuracy, or form, there are built-in metrics that allow for ongoing feedback. The gap between current ability and desired performance is visible, trackable, and objective.
• You often have a coach or teammate. This external support system can provide feedback, correct errors in real-time, and help you spot issues that are invisible to you—like posture, timing, or pace. Coaches also offer encouragement and accountability, both of which are critical to staying motivated and on track.

In short, sports training naturally integrates exemplars, feedback loops, and third-party guidance—all essential ingredients for self-regulated learning.

Translating That to Mental Learning

Mental learning needs the same support systems—but they’re much harder to perceive and build without intentional design:

• Exemplars are less visible. Unlike physical performance, the “what good looks like” for a persuasive essay or a debugging strategy isn’t always obvious. Learners need help identifying strong models and understanding why they work.
• Progress is harder to measure. Mental growth can feel abstract, especially when the outcomes are qualitative or long-term (e.g., critical thinking, creativity, or confidence). Without concrete checkpoints, learners can struggle to see improvement or know what to adjust.
• Feedback is delayed or missing. In the absence of a coach or peer, learners often don’t realize where they went wrong or what could be improved until much later—if at all.

As AI tools evolve into academic coaches and tutors, they’re not just delivering content—they’re scaffolding the learning process. They help students become more aware, strategic, and confident in their own ability to grow. And that’s what makes self-regulation not only possible—but sustainable.

Here’s a quick-guide I tossed together (with the help of ChatGPT and Canva):

😬 We can’t really ignore it any longer. ICYMIT, a variety of scientific research confirms the reality that improper use of AI – aka offloading things like critical thinking and analysis – can lead to cognitive atrophy. There’s loads of research out there including this one by Michael Gerlich (1) and this recent one from MIT, but we’ve known about it awhile, as this 2023 from Peking University shows us. Even Cornell University’s research just came out.

As you would expect –> if you offload cognitive thinking to AI, your own critical thinking atrophies.

This is horrible news! I love using AI to make my life easier. How can I continue to use AI without losing my critical thinking?

The key is to use AI for cognitive “wall practice”. Like a tennis player who gets better by using a wall to volley back and forth against themselves, use AI to challenge your ideas and spark creative thinking. A debate or forensics buddy, for those once on the debate team.

Examples.

Like strength training, it’s not as easy. It’s sooooo much easier to just ask the AI to do stuff. But that’s where we fall apart –> our brains are designed to want to burn as few calories as possible. It’s always looking for ways to be energy efficient, so it makes sense that asking AI to do stuff for us feels good. We’re wired that way. However, as we know with all physical care, effort in the right places makes us healthier.

(1). Check out Dr. Phillipa Hardman lit review round up. Yee-haw! You should totally subscribe to her if you haven’t already.

“These aren’t just chatbots. They’re designed dependency machines,”

The Deep View did a nice job back in mid-May staring emotional companion chatbots right in the face.

https://www.thedeepview.co/p/the

Companion bots highlights two equally important realities, IMHO,

1. Humans are still highly social creatures that require social and emotional activities to remain healthy.
2. If you are making a chatbot, bring a social psychologist onboard to ensure your app does no harm.

Number 1 is easy to wrap our heads around.

Number 2 provides an opportunity to prevent unintentional myopic thinking, echo chambers, and IRL isolationist behavior (e.g. opting to only engage with chatbots rather than live humans).

Why is this important?

Myopic thinking and isolation fosters “othering” behavior, which leads to hate and bias. These things left unchecked have never proven to be a good thing.

So, if you are building chatbots, use your powers wisely.

tl;dr: The rise of AI Agent Development is reshaping how we develop software, create professional development pathways, and teach coding.

A recent job post from Firecrawl caught my eye: they’re offering $5,000/month to “hire” an AI Agent—built by someone else—to autonomously perform content creation tasks (Firecrawl Job Post).

I’ve been thinking a lot about this whole “people who build AI Agents” thing, and this job post validates it: a new software stack is emerging, and it’s the AI Agent Development. We’re entering an era where AI Development is becoming a distinct discipline—sitting right alongside Front-end, Back-end, and Database development.

Firecrawl is leaning into this future by treating AI agents as first-class contributors—products in their own right, not just tools. They’re not only contracting these agents, but also providing a potential pathway for their human creators to join the company full-time.

It’s a bold signal: the people who build and orchestrate AI systems are becoming central to modern tech teams.

This shift changes everything. Professional development (PD) for developers will need to evolve, accounting for developing, integrating, collaborating with, and managing autonomous AI systems. The same goes for how we teach programming—skills like prompt design, agent chaining, and orchestration will become table stakes.

Firecrawl’s post is a glimpse into a future that’s already here.

I was reminded of Dr. Buolamwini’s words when I read about a study finding gender bias emerging from some LLMs. The tl;dr of the study:

• If queries presented female (i.e. name, language, or impression), responses were simplified or redirected to less technical stuff (e.g. design over coding).
• If queries presented male, responses includes more detailed steps and technical language (e.g. jargon).
• Bonus-Ugh: If queries presented female, responses were 23% more likely to include phrases like “Don’t worry if this seems complicated.” Aka, the LLM assumed a more emotional response from the user.

Other evidence of our past in the data: Jobs are gender stereotyped.

”Women (were) mainly assigned job titles such as graphic designer, fashion designer, or nurse and men assigned job titles such as software engineer, architect, and executive… ChatGPT has a hard time associating male pronouns with nurses and an even harder time letting female pronouns handle a pilot’s duties of getting a plane ready for landing.”

Why Is This?
Our collective unconscious lives (and thrives) in the training data. Genders are associated with certain jobs because the source material does so. The associated responses to technical questions is because of the training data.

What Can Be Done?
All three of the below are critical to improve our collective training data:

1. Be aware of what you put online. Remember, LLMs often use web content to train on.
2. Report what you find. Most LLMs have a reporting mechanism. Use it.
3. Support an organization.  Some organizations include:
   1. Distributed AI Research Institute (DAIR)
   2. Center for Responsible AI at NYU
   3. Montreal AI Ethics Institute

References:
Buolamwini, J. (2023). Unmasking AI: My journey to hold AI Accountable. Penguin Random House.

Emelyanov, A., & Chuprina, S. (2025). Ethical and security aspects of multimodal foundation models. Array, 19, 100295. https://doi.org/10.1016/j.array.2025.100295

Kennedy, P. (2024, March 22). New study finds gender stereotypes persist in ChatGPT. TechXplore. https://techxplore.com/news/2024-03-gender-stereotypes-chatgpt.html

tl;dr

Problem: Digital-device attention span has dropped to ~40 seconds within just the past 8 years (Duke, 2023).

Solution (for IDs and LDs): Context Challenge Activity Feedback

Dr. Gloria Mark’s research indicates that our screen-based attention spans has dropped from ~75 seconds in 2012 to ~40 seconds in 2020 (Duke, 2023). However, that’s only part of the story. True, there’s a reduction in average attention durations on digital devices, but other things are true too: binge-watching is on the rise (cite), we spend more time on our devices than before (cite), etc. So, what’s the deal?

There are 5 types of attention, and in education:

Information is in abundance, and distractions abound. So, how do Instructional and Learning Designers prevent attention drifting? Answer: Michael Allen’s Content Challenge Activity Feedback (Allens Interactions, 2021).

Context Challenge Activity Feedback

As IDs and LDs, we know behavioral changes require us to define what learners should be able to do by the end, and we know the knowledge learned along the way is a natural part of the scaffolding. CCAF takes it a step further by giving learning a purpose: it wraps activities within the a context and a challenge (or puzzle).

It answers the question why do these things.

1. Context

The situation, or environment, in which learning occurs. Depending on the learning need, this may include some background and relevant circumstances.This way, learners relate to the material and understand its application in real-world scenarios, which provides some motivation. Ideally, create a context the learners are deeply invested in already.

2. Challenge

Allen defines this as the problems or tasks learners must confront within the context. However, I like to frame these as puzzles to solve. The goal is to stimulate critical thinking and problem-solving skills. With meaningful challenges, it stimulates active engagement. Pro-tip: highlight the reward that comes with solving the puzzle. This shifts the mindset from solve this “or else” to the mindset of solve this “and get”.

3. Activity

Defined, specific actions or tasks learners do to solve the challenge. Simulations, deliverables, role-playing, and discussions are some common activities. Bonus, keep learners interactive and collaborative to increase a sense of community among learners.

4. Feedback

Clear feedback that celebrates wins and provides guidance areas that need improvement. I emphasized “need” here because it’s human nature to provide areas of improvement regardless of level of proficiency; things can always be better than what they were. The trick here is to only provide areas of improvement that are necessary. fight the desire to point out every areas of improvement.

Final Thoughts

Philosopher Alasdair MacIntyre, (2007, After Virtue, pp. 216) who called humans “story-telling animals”, believed that our ability to tell stories or place moral identify within contexts and scenarios, is what made us truly human. I like this idea that stories are what give us the human condition. Our brains, which learn by comparing and contrasting new things to what it already knows, build themselves on past context — aka stories and memories.

CCAF creates a story wherein the learner becomes the hero of the story. No wonder it works to prevent someone’s attention from drifting.

 “It is no longer believed that neurons in the brain are incapable of being regenerated. It was once wildly believed that we are born with our full conpletement of neurons and that new neurons are not gerneated. This idea is now untenable, at least in a region called the dentate gyrus.” ~ Jamie Ward (2015) Student’s guide to cognitive neuroscience.  

This means you need to work your brain out to keep it healthy just like your heart. You do this by learning something that’s just beyond the reach of what you think you can do. 

Charlotte Danielson’s article in Education Week has been on my to-read list since it came out, and I’m glad I finally got to it. Chalked full of ideas and thoughtfulness, it’s a must read for any administrator looking to create a better culture around the profession of learning.  

My favorite spot:  

“There is professional consensus that the number of teachers whose practice is below standard is very small, probably no more than 6 percent of the total, according to the Measures of Effective Teaching study and others…

Given this landscape, it makes sense to design personnel policies for the vast majority of teachers who are not in need of remediation. And, given the complexity of teaching, a reasonable policy would be one that aims to strengthen these educators’ practice. Personnel policies for the teachers not practicing below standard—approximately 94 percent of them—would have, at their core, a focus on professional development, replacing the emphasis on ratings with one on learning.”

Yes! Teachers are masters of the social science of learning. Our profession of understanding learning is twofold: one is student-facing and one is profession-facing. It’s like a good psychologist: they know how to navigate a session and what to prescribe for a patient to improve their mental health & at the same time they are constantly learning more about the science of psychology in their field. 

Looking forward to new ways to support this profession as it grows into a respected field.  

IBM is working with a Texas school district to pilot a one-stop-shop for student data, both qualitative and quantitative. IBM’s powerful Watson is spending the year gathering loads of data entered by systems and humans (like teachers) about the students with the hope of providing insight into students that is more comprehensive than traditional assessment scores. 

If this proves to work, this could free up brain space for teachers and staff to take that data and make use of it. 

Read more at EdSurge.