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Future of Education: Application not Regurgitation of Knowledge – Part II


AI technologies like ChatGPT are necessitating a fundamental overhaul of our educational systems and institutions.  Getting the right answers to predetermined tests is no longer sufficient in an age where AI can access, integrate, and recite knowledge billions if not trillions of times faster than the human mind. So, what are the skills, capabilities, and experiences that our students and citizens will need to prosper in an age where personal and professional success will be based on the application, not the memorization and regurgitation, of knowledge?

In part 1 of this 3 part series on the future of education, I introduced two critical educational requirements:

(1) Transformational Education Must Nurture Empowerment

(2) Transformational Education Must Challenge Thinking…Critical Thinking!

Let’s continue that conversation here in Part II to define the requirements for humans to excel in creating organizational and societal value in a world dominated by AI and Big Data.

(3) Transformational Education Must Encourage and Exploit Conflict

Many organizations engage in a “wear’em down” decision-making process when dealing with wicked hard challenges with multiple opposing views.  This approach ends up compromising on the Least “Worst Option” that offends the fewest senior leaders and HIPPOs (Highest Paid Person’s Opinion). This “lowest common denominator” approach leads to sub-optimal decisions from both organizational and key constituents’ perspectives.

To overcome this dilemma, we must train everyone in how to embrace and exploit an “AND” mentality.  An “AND” mentality seeks to synergize Option A “AND” Option B, versus an “OR” mentality that compromises on either Option A “OR” Option B. Think “improve effectiveness of healthcare” AND “improving economic growth”.

The “AND” mentality seeks to: 1) blend two or more loosely-coupled perspectives into a new, superior perspective, 2) bend an original perspective from a multitude of different dimensions to see what new perspectives it yields, and finally 3) break apart a perspective into its subcomponents to recombine, eliminate, or re-engineer the perspective subcomponents into something greater than before…the Best “Best Option” (Figure 1).


Figure 1: Embrace Diversity and Conflict to Fuel Innovation

Organizations can transition from compromising on the Least “Worst Options” towards synergizing on Best “Best Options” by embracing a Design Thinking or growth mindset that yields an environment in which everyone is empowered to share and leverage their diverse perspectives and experiences to create something superior to the individual components.

“Design Thinking is a human-centered and collaborative approach to problem solving using a design mindset to solve wicked complex problems” – IDEO

Design Thinking is all about people – their points of view and their stories and experiences. Design Thinking is about gaining an intimate understanding and appreciation of your key constituents’ journey; that is, what they are seeking to accomplish, their intent, the decisions that they need to make, the KPIs and metrics against which they will measure success, and the gains (benefits) and pains (impediments) that they encounter on their journey.

The key to Design Thinking is an empowering mindset. Design Thinking seeks to empower and democratize the ideation process by ensuring that all ideas, regardless from where they originated, are worthy of consideration.  Design Thinking understands that if you don’t have enough “might” ideas, you won’t have break-through ideas (Figure 2).


Figure 2: Establishing a Design Thinking (Growth) Mindset

(4) Transformational Education Must Fuel Curiosity-Creativity-Innovation Cycle

The battle to learn faster than machines is over.  Not only can AI models analyze data billions and trillion times faster than humans, but data scientists are constantly developing new and improved ways for AI models to learn (e.g., machine learning, deep learning, reinforcement learning, federated learning, active learning, transfer learning, meta-learning, etc.).  You don’t need to be a data scientist to predict this outcome.

If you want to change the game, change the frame.

But we don’t need to “out-learn” the AI models.  My $1.99 calculator has negated my need to learn how to calculate square roots and allowed me to focus my resources on the application of math to solve important problems.  In the same way, AI will soon negate my need to aggregate and analyze massive volumes of data to uncover meaningful, relevant, and predictable customer, product, service, and operational trends, patterns, and relationships buried in that data.

AI will enable humans to focus on those skills and capabilities that make us more human.  That means that we must encourage and nurture that natural human curiosity that fuels creativity and ultimately leads to innovation necessary to reinvent of today’s outdated products, services, processes, policies, and procedures (Figure 3).


Figure 3: Unleash the Human Curiosity-Creativity-Innovation-Reinvention Pyramid

Unfortunately, society goes to great lengths to crush curiosity and an inquisitive mindset of our students in favor of standardization.  We have standardized classes with standardized curriculums sitting in standardized classrooms with standardized testing. No one is allowed to color outside the lines.  But the curiosity crushing doesn’t stop there because we take jobs in organizations with standardized organizational charts with standardized job descriptions, standardized performance reviews, and standardized pay grades.

Standardization leads to a “lowest common denominator” human development.  And to create students who can flourish in a world dominated by Big Data and AI, education MUST move beyond teaching and measuring standardization and instead focus on unleashing that human unique Curiosity-Creativity-Innovation-Reinvention value creation cycle.

(5) Transformational Education Must Be Grounded on Ethics

Ethics is the moral principles that govern a person’s behavior or actions, where moral principles are the principles of “right and wrong” that are generally accepted by an individual or a social group.

AI ethics focuses on the ethical development, application, and management of AI. AI Ethics involves addressing AI model confirmation bias and identifying and exploring the potential unintended consequences from the application of AI.  AI ethics focuses on ensuring that AI is used in a way that is fair, responsible, and beneficial to both individuals and society.

An important characteristic of ethics is that ethics is proactive, not passive.  Ethics must be proactive in taking appropriate actions in light of society moral standards, versus just abdicating to someone else. And if you don’t know the difference between proactive and passive ethics, then it’s time for a bible lesson refresher – The Parable of the Good Samaritan (clink here for a refresher lesson). See Figure 4.


Figure 4: Proactive versus Passive Ethics

Special educational focus must be given to training stakeholders on identifying and quantifying the potential unintended consequences of deploying new technologies such as Big Data and AI. 

Unintended Consequences are unforeseen or unintended results that can occur as a result of an action or decision.

That means collaborating, brainstorming, and ideating the potential outcomes from AI deployments across a wide range of impacted stakeholders and constituents.  That means investing the time prior to AI development and deployment (duh) to explore and envision the different ways that AI can go wrong.

But even identifying all the potential unintended consequences aren’t enough if we don’t have a way to codify the KPIs and metrics against which we will identify and monitor for those unintended consequences.

And that’s the challenge with the AI ethics conversation – if we can’t measure it, then we can’t monitor it, judge it, or change it.  Consequently, we must find a way to transparently instrument and measure ethics.

I’ve created a collaborative Ethics Design Canvas that organizations can use to fuel that collaborative, brainstorming AI ethics conversation. This canvas is designed to leverage economics concepts around value creation to facilitate the difficult conversations across the organization’s key internal and external stakeholders in identifying how “ethics” will be measured and monitored for a particular initiative or program (Figure 5).


Figure 5: Economics of Ethics Worksheet

I strongly encourage others to test out this canvas and provide feedback so that we can collaborate in creating a pragmatic, easy-to-understand AI ethics canvas.

Future of Education Summary – Part II

AI technologies like ChatGPT are necessitating a fundamental overhaul of our educational systems and institutions.  This blog series seeks to identify where and how our educational systems and institutions need to transform to prepare our leaders, workers, students, and citizens to thrive in an era where Big Data and AI will exert undue influence on many of our personal decisions and our professional endeavors.

In part 1 of a 3 part series on the future of education, I introduced two critical educational requirements:

(1) Transformational Education Must Nurture Empowerment

(2) Transformational Education Must Challenge Thinking…Critical Thinking!

In part II, I introduced three additional educational requirements:

(3) Transformational Education Must Encourage and Exploit Conflict

(4) Transformational Education Must Fuel Curiosity-Creativity-Innovation Cycle!

(5) Transformational Education Must Be Grounded on Ethics!

In Part 3, I’ll introduce the final requirements for overhauling our educational systems and institutions.