As AI continues to disrupt industries and reshape the workplace, I’m convinced that clarity of mind and higher-order thinking is the key to staying ahead. Moreover, we’re at a critical juncture where AI is taking on more cognitive tasks. As a result, humans must focus on developing the skills that differentiate us – critical thinking, creativity, and informed decision-making. Additionally to harness the power of AI, especially Large Language Models (LLMs), we need to relook at our decision-making processes at both the individual and organizational levels. This includes rethinking how we collaborate with AI to unlock its full potential.
In this article, I’ll share my insights on why clarity of mind and higher-order thinking are the keys to decision-making in the AI era. Moreover, I’ll examine the new AI dynamics affecting our decision-making and analytical processes. This includes the types of skills that are required to effectively harness the strengths of LLMs. Lastly, I’ll introduce you to an innovative, collaborative approach for analytical thinking and decision-making in the age of AI. Indeed, we need to relook at how we interact with AI in order to amplify the effectiveness of our cognitive abilities, both humans and AI. This will help us to stay ahead of the curve and make smarter decisions.
1. Clarity of Mind: A Relentless Pursuit Toward Higher Order Thinking.
In today’s information-rich world, clarity of mind is crucial for making complex decisions. Without a doubt, our brains are naturally wired to identify patterns and find meaning, a trait that has served us throughout history. To get a better understanding of how we can think more clearly and make good decisions in the age of AI, let’s first look at what clarity of mind means. Also, I’ll examine how we’ve traditionally approached complex problems through learning, knowledge acquisition, and higher-order thinking.
a. The Age-Old Pursuit of Clarity of Mind to Make Better Decisions.
The quest for clarity of mind is not a modern phenomenon; it has been a cornerstone of philosophical and intellectual pursuits throughout history. From ancient Greek philosophers to modern-day thought leaders, the importance of clear thinking has been consistently emphasized as a precursor to sound decision-making. To better understand what it means to have clarity of mind, let’s start with a definition:
“Clarity Of Mind is … a sophisticated interplay of cognitive processes, emotional regulation, and ethical frameworks that shapes our understanding of reality and informs our actions within it.”
Lorne Michael Cousins
To put it another way, clarity of mind enables individuals and organizations to cut through the noise, prioritize effectively, and focus on what truly matters. Without a doubt, clarity of mind is essential for both leadership and management. By decision-makers achieving a clearer mental state, the following happens:
How Clarity of Mind Improves Decision-Making
- Focus. First, clarity of mine purposefully enables us to concentrate on what is important, avoiding information overload.
- Situational Awareness. Moreover, it results in us understanding relationships and what is relevant, connecting the dots. For more on this topic, see article, Organizational Situational Awareness: How To See Remarkably In The World Of Digital Tech and AI.
- Discernment. Additionally, it enables us to better select the best options, differentiating what is important and keeping biases at bay.
- Goals Alignment. Further, it helps us avoid emotional reactions. More importantly, it aids us in making better decisions that align with what is truly valuable to both the organization and the individual.
- Coherence. Lastly, it enables us to consistently take clear-eyed, decisive actions. This is because with clarity of mind we have the foresight to craft harmonious action plans, assuring successful outcomes. Indeed, coherence for organizations unifies, invokes initiative, and energizes all those who are part of the team, no matter what the challenge.
For a more detailed discussion on this topic, see Lorne Michael Cousins’ article, Clarity of Mind. Also, for more on the benefits of Clarity, see Gregg Vanourek’s article, The Problem with Lacking Clarity in Your Life.
b. Higher Order Thinking and Bloom’s Taxonomy: The Traditional Way of Thinking and Learning.
Most of us have heard of the phrase “Higher Order Thinking”, but did you know it comes from a foundational learning framework called Bloom’s Taxonomy? Specifically, this framework categorizes cognitive processes into different levels ranging from basic recall to complex processes like analysis, evaluation, and creation. Originally developed in 1956, many educators have adopted Bloom’s Taxonomy over the years. Moreover, it was slightly revised in 2001 to make it a more adaptable model using action verbs instead of static nouns. Also, businesses use this taxonomy for training employees in the areas of knowledge acquisition and critical thinking skills. To better understand Bloom’s Taxonomy (Revised) let’s examine each of its phases from an analytics and decision-making perspective.
Traditional Order of Learning: From Basic Thinking to Higher Analytical Skills
- Remember Phase. Here, we memorize basic facts, dates, events, persons, places, concepts and patterns. Action verbs include: define, list, memorize.
- Understand Phase. At this stage, we can explain concepts in our own words and interpret data. Action verbs include: classify, explain, identify.
- Apply Phase. Now at this stage, we can use our knowledge to solve problems and execute basic tasks. Action verbs include: implement, demonstrate, use.
- Analyze Phase. At this Higher Order Thinking phase, we can break down complex information, deriving insights by understanding their components and relationships. Action verbs include: examine, question, experiment.
- Evaluate Phase. At this level, we continue with Higher Order Thinking, we exercise judgement and critical thinking. Also, we assess the value or effectiveness of something, based on criteria and standards. Action verbs include: appraise, judge, select
- Create Phase. This is the apex of both Higher Order Thinking and Bloom’s Taxonomy (Revised). Ideally from a business perspective, we are now innovative, producing something new, original, and of competitive advantage. Action verbs include: design, formulate, conjecture
For more on Bloom’s Taxonomy and Higher Order Thinking, see Valamis’ article, Bloom’s Taxonomy, Utica University’s chart, BLOOM’S TAXONOMY REVISED, and University of North Carolina’s tips on, Higher Order Thinking: Bloom’s Taxonomy.
2. Unlocking Higher Order Thinking with Large Language Models (LLM): Crafting Effective AI Prompts.
In this age of AI, it is essential for us to have clarity of mind to fully take advantage of this new type of technology. Without a doubt, Large Language Models, a form of Generative AI, has the potential to significantly enhance our Higher Order Thinking. Specifically, this AI is a collaborative tool to help us analyze complex ideas and generate novel solutions.
However, the effectiveness of this collaboration depends heavily on the quality of the prompts we use to interact with the AI. The reason for this is that LLMs such as ChatGPT basically use our user prompts to generate text by predicting the next word in a sentence. So, by crafting an effective prompt, you are essentially guiding the AI model’s predictions. Below are key principles for crafting effective LLM prompts.
Principles for Crafting Effective LLM AI Prompts
- Clarity. First, prompts should be straightforward and unambiguous, allowing the model to understand the task directly. For example, “Explain the concept of artificial intelligence in simple terms” is clearer than “Tell me about AI.”
- Context. Here, context helps the model understand the background and specific requirements of the task. For instance, “Explain the benefits of renewable energy sources to a 10-year-old” gives context about the target audience.
- Precision. In this case, be specific about what is required in the response, such as format or content. For example, “List the top 5 benefits of using public transport in a table format” is more precise than “Tell me about public transport benefits.”
- Role-Play. Lastly, tell the LLM to assume a specific role or persona, which can help in generating more targeted and relevant responses. For example, “Respond as a customer service representative to a complaint about a delayed shipment” demonstrates role-play by specifying the persona.
For more tips on LLM AI prompt engineering, see DataCamp’s article, A Beginner’s Guide to ChatGPT Prompt Engineering. Also, see ChatGPT’s Prompt Engineer for more answers.
“With AI, answers are abundant and cheap; the challenge is crafting the right prompt”
3. What Else is Needed Besides Clarity of Mind and Higher Order Thinking to Leverage LLM AI for Optimal Decision-Making?
So, to effectively leverage LLMs for critical thinking and decision-making, both individuals and organizations must develop new competencies. Moreover, these competencies are more than just effective prompt engineering. In fact, most of these advanced skills are not new. However, organizations will need to reevaluate their problem-solving processes to effectively leverage AI. To list, below are the competencies needed to empower critical thinking and decision-making using LLMs:
Competencies Needed to Leverage LLM AI for Optimal Decision-Making
- Clarity of Mind. Again, nothing new, but essential to harness AI.
- Ability to Craft Effective AI Prompts. This is a new skill that is needed to tap into the deep knowledge of LLMs. Without effective prompts, the AI will frequently provide misguided answers.
- Able to Critically Evaluate LLM’s Answers. In this case, we need to leverage traditional critical thinking skills, but in collaboration with LLMs.
- Decision-making that Masterfully Employs AI. Here, we need to rethink our processes for decision-making, analytics, and learning to maximize AI’s capabilities.
By incorporating these competencies, both individuals and organizations can harness the benefits of LLMs while avoiding its pitfalls. Indeed, it’s time to look at possible new approaches to analytics and decision-making using LLMs.
4. Maximizing AI Use for Decision-Making: Do We Need a New Approach to Critical Thinking?
Without a doubt, the rise of Large Language Models (LLMs) is transforming how we process information and make decisions. However, the question is how best can we use LLM AI to make better decisions? One idea for leveraging AI for decision-making is to rethink conventional analytical frameworks such as Bloom’s Taxonomy. As previously discussed, this framework is used by educators and businesses to enhance knowledge acquisition and critical thinking skills. My idea is to use a modified version of Bloom’s Taxonomy to assist us with incorporating LLMs into our decision-making processes. This novel approach is inspired by Michelle Kassorla’s article on learning, Inverted Bloom’s for the Age of AI.
In the example below, I reorder the stages of the Bloom’s Taxonomy when problem-solving with AI. Surprisingly, many of us who use LLMs such as ChatGPT are already using a similar analytical approach when working with LLMs. For instance, an user will first prompt the LLM and then AI will provide a response. This is much like the Create Phase, the last phase of Bloom’s Taxonomy, where instead of AI, the student would have created a solution. Moreover with our LLM interactions, many of us already evaluate AI answers and then ask follow-up questions. So with LLMs, many of us are already doing a collaborative analysis similar to the Bloom’s Taxonomy other cognitive phases, except in reverse order.
Reversing Bloom’s Taxonomy: LLM AI-Assisted Decision-Making
a. Create Phase.
Here, the user prompts AI to generate an initial solution to a problem. For example, a marketing team uses AI to create a social media campaign strategy for a new product launch.
b. Evaluate Phase.
In this phase, the users assess the output from the Create phase, either independently or with AI assistance, resulting in feedback and conclusions. For instance, the marketing team evaluates the AI-generated campaign strategy, considering factors like target audience and budget constraints.
c. Analyze Phase.
Next, the user works with AI to break down the results from the Evaluate phase, gaining new insights. For example, the marketing team analyzes the evaluated campaign strategy with AI, identifying potential strengths and weaknesses.
d. Apply Phase.
During this phase, the user may take action based on the insights gained, becoming increasingly self-directed in the process. For instance, the marketing team implements the refined campaign strategy, using AI-generated content and adjusting their approach as needed.
e. Understand Phase.
In this phase, the user can confidently explain and classify key information related to the solution. For example, the marketing team can now clearly articulate the campaign’s objectives, target audience, and expected outcomes.
f. Remember Phase.
In this last phase, the user has become fully knowledgeable about the situation, enabling confident decision-making. For instance, after successfully executing the campaign, the marketing team remembers key takeaways and applies them to future campaigns.
g. Repeat Process, As Needed.
Here, this thinking process continues as necessary. This can involve iterating through part or all of the phases with AI assistance to refine the decision or implementation. For example, the marketing team repeats the Evaluate and Analyze phases to assess the campaign’s mid-term results and make adjustments accordingly.
Final Thoughts.
So, in this article, I have shared my insights on why clarity of mind and higher-order thinking are the keys to decision-making in the AI era. Moreover, there are new AI dynamics that are cause for us to relook at our decision-making and analytical processes. It is essential for us to review the types of skills we need to effectively harness the strengths of LLMs. Also, this includes identifying a more collaborative approach to working with AI to improve our analytical thinking and decision-making processes.
More References.
- Michelle Kassorla’s article, Inverted Bloom’s for the Age of AI. More on using an inverted Bloom’s Taxonomy for learning and critical thinking when using LLMs.
- David Weller’s article, How AI Is Changing Bloom’s Taxonomy for TEFL. More tips on how to work with LLMs and different levels of Bloom’s Taxonomy for student learning.
- Mindjoy’s article, Bloom’s Generative Taxonomy offers tips on working with AI, specifically within the Create Phase of Bloom’s Taxonomy.
- Unvarnished Fact’s article, Think Critically: It’s More Than A Process, It Is A Perspective That Will Empower Results.
For more from Unvarnished Facts, see the latest articles on Decision-Making, Learning and Innovation.
Writer and advisor in supply chain technology and operational analytics. Passionate about giving actionable insights on information technology, business, innovation, creativity, and life in general.