Eva, on your Cheatsheet, I see your directive to AI chatbot to analyze your metacognition log/journal.
I like this idea, but I'm curious about the pros and cons of using an ongoing AI thread as a place for your log/journal. One advantage would be getting quicker feedback as if you were showing your coach your journal after each session.
Great question Griff! Using an ongoing AI thread as a metacognition journal has some clear advantages—namely, instant feedback, pattern recognition, and structured reflection without friction. It’s like having a cognitive coach available 24/7.
However, as you mention, there are also trade-offs. AI excels at identifying patterns in learning logs, but it lacks the deeply personal context that self-reflection brings. There’s also a risk of over-relying on external feedback rather than strengthening your own metacognitive awareness.
One potential hybrid approach:
1) Self-reflect first – Write your thoughts before AI input to strengthen your own processing.
2) Then, let AI analyze – Have it highlight blind spots or suggest improvements.
Refine with human intuition – Decide what resonates and tweak your strategies accordingly.
This way, AI enhances metacognition rather than replacing it. Curious—how do you approach tracking and refining your learning?
I like your hybrid approach. I could add reflections as often as I wanted, but I could tell AI something like, "Here is my reflection from today's practice. But I don't want you to comment on it just yet."
I chatted with Claude about this, and he (hah!) liked it and added:
"You could also use different levels of feedback. For example:
Level 1: Just highlight patterns the AI notices
Level 2: Add specific questions to help deepen your reflection
Level 3: Full analysis with suggestions"
I am planning to implement this, either in Claude Projects or in Notebook LM, to see which I prefer.
Eva, I'll first answer your question about how I approach tracking and refining my learning for mountain biking skills practice.
(After reading your post, I'm now thinking of reflection as one component of metacognition.)
My reflection practice:
* I jot brief notes in a phone app during breaks in practice or immediately afterward.
* Hours later I expand on these notes in a hand-written journal. I sometimes review these entries before I plan my next practice session
* If I'm practicing with one or more riders, we'll often engage in conversation about what we're doing/trying/learning/wondering about.
* I'll sometimes discuss my reflections with other riders online, either in a one-to-one chat with a riding buddy or in a forum thread with a group of riders.
I plan to continue these habits, but I'd like to add more depth and structure with AI.
FYI, I've published 5 Substack posts about the practice of reflection over the past couple of years:
That sounds super rigorous —layered, intentional, and embedded in both solo and social learning. I’ll check out your Substack pieces—reflection is such an underrated skill.
Eva, on your Cheatsheet, I see your directive to AI chatbot to analyze your metacognition log/journal.
I like this idea, but I'm curious about the pros and cons of using an ongoing AI thread as a place for your log/journal. One advantage would be getting quicker feedback as if you were showing your coach your journal after each session.
Great question Griff! Using an ongoing AI thread as a metacognition journal has some clear advantages—namely, instant feedback, pattern recognition, and structured reflection without friction. It’s like having a cognitive coach available 24/7.
However, as you mention, there are also trade-offs. AI excels at identifying patterns in learning logs, but it lacks the deeply personal context that self-reflection brings. There’s also a risk of over-relying on external feedback rather than strengthening your own metacognitive awareness.
One potential hybrid approach:
1) Self-reflect first – Write your thoughts before AI input to strengthen your own processing.
2) Then, let AI analyze – Have it highlight blind spots or suggest improvements.
Refine with human intuition – Decide what resonates and tweak your strategies accordingly.
This way, AI enhances metacognition rather than replacing it. Curious—how do you approach tracking and refining your learning?
I like your hybrid approach. I could add reflections as often as I wanted, but I could tell AI something like, "Here is my reflection from today's practice. But I don't want you to comment on it just yet."
I chatted with Claude about this, and he (hah!) liked it and added:
"You could also use different levels of feedback. For example:
Level 1: Just highlight patterns the AI notices
Level 2: Add specific questions to help deepen your reflection
Level 3: Full analysis with suggestions"
I am planning to implement this, either in Claude Projects or in Notebook LM, to see which I prefer.
I love these additions! Thanks for sharing!
Eva, I'll first answer your question about how I approach tracking and refining my learning for mountain biking skills practice.
(After reading your post, I'm now thinking of reflection as one component of metacognition.)
My reflection practice:
* I jot brief notes in a phone app during breaks in practice or immediately afterward.
* Hours later I expand on these notes in a hand-written journal. I sometimes review these entries before I plan my next practice session
* If I'm practicing with one or more riders, we'll often engage in conversation about what we're doing/trying/learning/wondering about.
* I'll sometimes discuss my reflections with other riders online, either in a one-to-one chat with a riding buddy or in a forum thread with a group of riders.
I plan to continue these habits, but I'd like to add more depth and structure with AI.
FYI, I've published 5 Substack posts about the practice of reflection over the past couple of years:
https://mtbpracticelab.substack.com/t/reflection
That sounds super rigorous —layered, intentional, and embedded in both solo and social learning. I’ll check out your Substack pieces—reflection is such an underrated skill.