Hello, Michelle, thanks for connecting again! Let's use this session to help you apply critical thinking to your own situation. Hopefully this will help you better decision-making by challenging assumptions or avoid potential key cognitive biases.
Whenever you're ready, text me what you'd like to do.
In this mini-session, I’ll guide you through applying critical thinking to your own situation using a method called challenging assumptions.
Specifically, we will practice how to identify relevant assumptions in your decision-making situation and investigate them thoroughly, which will give you the best chance of making the best decisions.
To tailor our session to you, I'd like to know a bit about you first. Can you tell me your job role and typical responsibilities?
To develop critical thinking skills, it is most effective to apply the techniques to your own situation. Could you describe a situation or problem involving difficult decision making?
When tackling a problem that requires decision-making, here we practice a 3-step process:
Step 1 Identify the Assumptions. Given the situation/problem you described above, ask yourself questions such as, “What am I assuming in this situation?” or “What do I believe to be true or false in this situation?” This helps surface the assumptions influencing your thought process.
Let’s start with **one assumption** you may be making in this situation.
Ask yourself: *“What do I believe to be true that is shaping my hesitation or decision?”*
Please keep it simple and list **just one assumption at a time**.
If it helps, here’s an example assumption you might be holding:
> *“Human-centered AI and supply chain management don’t meaningfully overlap because supply chains are mainly about automation.”*
What is **one assumption** you think you’ve made so far? [AI synthesized]
Step 2. Determine Contributing Factors. Begin by pinpointing the factors that support an assumption. This will help you understand what is holding up the assumption and where its foundation lies.
Given the assumption you just identified, what factors support it? Consider factors like past experiences, cultural norms or expectations, expert opinions, personal beliefs, historical trends, or observations from similar situations. Take your time.
Whenever you're ready, text me as many as relevant factors you can think of.
Step 3. Analyze Each Factor Individually: This step is to evaluate each contributing factor, determining whether it is supported by concrete evidence or based on further assumptions or speculation. This will help you identify weak points in your reasoning.
Now let's take a closer look at the factors you mentioned above. Ask yourself: are these factors supported by concrete evidence, or based on further assumptions or speculation?
Whenever you're ready, I'd suggest that you list each factor as such:
Based on our investigation so far, give me a minute to organize my thoughts and share them with you...
Here is what I think.
Your assumption—that human-centered AI and supply chain management don’t meaningfully overlap because supply chains are mainly about automation—is **partially valid**, but not fully so.
The contributing factors you listed are credible: your expertise in human-centered AI, authoritative readings on supply chain management, and documented project descriptions all provide **real evidence** that supply chains heavily emphasize optimization, efficiency, and automation. These factors reasonably support the idea that much of supply chain work has historically been framed as a technical, system-level problem rather than a human-centered one.
However, the assumption becomes weaker when applied to *your specific decision problem*. The evidence you’re drawing on largely reflects **traditional or dominant framings** of supply chain management, not necessarily the full scope of current or emerging practice. It also embeds an additional, unexamined assumption: that automation-oriented domains leave little room for human judgment, oversight, trust, exception handling, or collaboration—all areas where human-centered AI can be highly relevant (e.g., demand forecasting under uncertainty, disruption response, ethical trade-offs, explainability for planners).
To strengthen or refine your reasoning, you might:
- Reformulate the assumption to be more precise, for example: *“Most existing supply chain AI projects prioritize automation over human–AI collaboration, which may limit—but not eliminate—opportunities for human-centered approaches.”*
- Further validate the assumption by examining **where humans remain critical** in supply chains (e.g., decision-making during disruptions, accountability, cross-organizational coordination) and whether clients explicitly value these human-in-the-loop needs.
This suggests the question may not be *whether* there is overlap, but *where and how convincingly you can articulate it* in your proposal. [AI synthesized]
Okay, now we have gone through the process of identifying and investigating an assumption. We can go through the process again to identify and investigate additional assumptions. I'll let you decide.
Got it. Let's continue then.
Congratulations! We have just gone through the module of identifying and investigating assumptions, a key technique of critical thinking. Here’s a summary of our conversation so far:
You explored a real decision you’re facing as a research manager: whether to write a proposal on human-centered AI agents for supply chain management, given your concern that supply chains emphasize automation while human-centered AI emphasizes collaboration. Using a structured critical-thinking process, you clearly articulated a key assumption behind your hesitation and examined the factors supporting it. Together, we found that while your assumption is partly grounded in credible evidence, it may rely on a traditional framing of supply chains and overlook emerging areas where human judgment, oversight, and trust are essential.
**What you’ve done well**
1. You clearly articulated a concrete, relevant assumption driving your decision, which is a strong foundation for critical thinking.
2. You grounded your reasoning in real evidence—your expertise, authoritative readings, and project descriptions—rather than vague impressions.
**Where you can improve or go further**
1. You could more actively question whether “concrete evidence” reflects the *current and full* landscape, rather than dominant or historical framings of a field.
2. You can strengthen your decision-making by explicitly exploring alternative framings (e.g., where humans remain critical in supply chains) and potential consequences of acting versus not acting.
Overall, you demonstrated thoughtful, disciplined reasoning; the next step is to broaden the lens and test how your assumptions hold up under newer or less familiar perspectives. [AI synthesized]
From our chat, I find you bring **very high openness and strong conscientiousness** to your thinking, which are powerful assets for identifying and investigating assumptions.
Here are a couple of ways you can do even better by leaning into those strengths:
1. **Use your openness to deliberately surface “non-obvious” assumptions.**
Because you’re naturally curious and comfortable with abstract ideas, try asking yourself one extra question each time: *“What assumption would someone from a very different domain or role make here?”* For example, imagine how a supply‑chain operator, a compliance officer, or a client executive might frame the problem differently. This helps you uncover assumptions that don’t come from your own expertise.
2. **Apply your conscientiousness to make assumption-checking systematic.**
You’re already disciplined and evidence-oriented. You can strengthen this by keeping a short, repeatable checklist when making decisions:
- What am I assuming is fixed vs. flexible?
- What evidence supports this, and how recent is it?
- What would I need to see for this assumption to be false?
Treating assumption analysis as a routine process (not a one-off reflection) will make your reasoning even more robust.
3. **Leverage your low emotional reactivity and independence of judgment.**
Your low neuroticism and agreeableness mean you’re well positioned to challenge assumptions without defensiveness or people-pleasing. Use this to explicitly question “expert consensus” or dominant narratives in a field and ask: *“Is this true, or just commonly repeated?”* This is especially valuable in fast-evolving areas like AI.
Overall, your strengths already align extremely well with critical thinking—the key is to use them more intentionally and systematically when assumptions feel “obvious” or well-supported. [AI synthesized]