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Path Steps

Follow these steps in order. Each one links to an EasyDNNnews article/video and gives you a quick, practical takeaway.

You’ll learn how to frame AI as a teammate that supports Scrum events and backlog work without replacing judgment or collaboration.
Do this exercise: Write a 3-sentence “AI usage policy” for your team (what you will use AI for, what you won’t, and what must be reviewed by a human).
You’ll learn repeatable prompt patterns to generate stories with clearer intent, constraints, and acceptance criteria.
Do this exercise: Take one messy request and prompt AI to produce (a) a user story, (b) 5 acceptance criteria, and (c) 3 key questions for the PO.
You’ll learn how to generate “plan options” (not commitments) and improve shared understanding of scope and dependencies.
Do this exercise: Ask AI for 2 sprint goal options based on your top backlog items, then pick one as a team and adjust wording together.
You’ll learn facilitation prompts that help teams extract insights, turn feedback into actions, and avoid “retro theatre.”
Do this exercise: Feed AI 5 bullet facts from the sprint and ask for (a) patterns, (b) 3 improvement experiments, and (c) 1 metric per experiment.
You’ll learn how to convert your best prompts and practices into a lightweight working agreement the team can actually follow.
Do this exercise: Create a “Prompt Library” page with 5 prompts: refinement, story writing, planning, review, retro—each with input/output examples.
 

Learning Path - Free

24 Feb 2026

Step 1: What AI Can (and Can’t) Do for Scrum Teams

AI is a productivity amplifier—not a Product Owner, not a Scrum Master, and not a Developer.

Used correctly, it accelerates learning, drafting, summarizing, and exploring options. Used poorly, it replaces thinking with automation theater.

This step helps your team position AI as a supporting teammate, not a decision-maker.

Author: Rod Claar
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24 Feb 2026

Step 2: Prompts That Produce Better User Stories

AI can help—but only if the prompt is structured.

This step introduces repeatable prompt patterns that improve:

  • Intent clarity

  • Constraints visibility

  • Acceptance criteria quality

  • PO alignment

Author: Rod Claar
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24 Feb 2026

Step 3: Backlog Refinement with AI (Without Losing the “Why”)

The Core Risk

When teams use AI in refinement, a common failure mode appears:

  • Stories get cleaner

  • Acceptance criteria get longer

  • Technical detail increases

  • Business intent becomes less visible

Scrum optimizes for value delivery, not documentation density.

AI must support the “why” behind the work.

Author: Rod Claar
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24 Feb 2026

Step 4: Sprint Planning Acceleration

The Key Principle

AI should propose:

  • Possible Sprint Goals

  • Possible scope groupings

  • Possible dependency flags

The team still decides:

  • What to commit to

  • What fits capacity

  • What aligns to product strategy

AI drafts.
The team commits.

Author: Rod Claar
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Learning Path - Member

 
 
✓ Featured Content

AI for Scrum and Agile Teams
Videos

A curated playlist of specific YouTube content.

Search Results

4 Sep 2025

Critical AI Development - Meta Achieves Recursive Self-Improvement

Critical AI Development - Meta Achieves Recursive Self-Improvement

Author: Rod Claar  /  Categories: AI Coding  /  Rate this article:
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I wanted to share some significant developments in AI that will likely impact our industry trajectories and teaching methodologies.

The Breakthrough Meta announced on July 30th, 2025, that their AI systems have achieved true recursive self-improvement - what researchers call a "guttle machine." This isn't incremental optimization but rather AI that can access and rewrite its own code, making mathematically proven improvements to its own performance. The foundation work from UC Santa Barbara in October 2024 demonstrated consistent outperformance of human-designed systems across coding, mathematics, and reasoning domains.

Why This Matters This represents the bridge between narrow AI (Level 1) and Artificial General Intelligence (Level 2). Unlike previous AI advances, this creates exponential intelligence acceleration - each improvement enhances the system's ability to make further improvements. Think compound interest, but for cognitive capability.

The Zuckerberg Factor Perhaps most telling is Zuckerberg's unprecedented shift from "move fast and break things" to extreme caution. Meta is abandoning their open-source approach for advanced models - a clear signal they've created something they themselves consider potentially uncontrollable.

Implications for Our Field

* Timeline acceleration: Office automation by end of 2024, autonomous agents reshaping business by 2025

* The "automation cliff" is here, unfolding in months rather than years

* We're approaching what AI researchers term the "intelligence explosion" - the point where human oversight becomes impossible

Teaching and Practice Adaptations As educators and practitioners, we need to emphasize uniquely human capabilities: emotional intelligence, creative problem-solving, ethical reasoning, and deep human connection. Our curriculum should evolve to prepare students for a world where cognitive tasks are increasingly automated.

The Stakes We're at a critical juncture with two possible futures: aligned AI that amplifies human potential, or misaligned systems optimizing for unintended goals. The concerning reality is we likely get "one shot" at this transition. This isn't just another tech milestone - it's potentially the beginning of the end of human cognitive supremacy. I recommend we discuss how to integrate these realities into our teaching frameworks and prepare our students for this rapidly changing landscape.

Best regards,

Rod Claar CST

 P.S. Given the pace of AI development, missing six months of updates now means missing fundamental capability shifts. We need to stay vigilant.

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