TL;DR: This article explores the transformative impact of generative AI on education, discussing how it redefines the 'need to know' and 'nice to know' in learning. It delves into practical implications for personalized learning, ethical considerations, and the evolving role of educators in an AI-integrated environment. The piece also reflects on historical shifts in knowledge dissemination and challenges us to prepare students for a future where AI plays a central role in content creation and consumption.

The Groundbreaking Impact of Generative AI in Education

Have you felt the ground shift beneath your feet as generative AI begins to redefine the educational landscape? Or is it just me?

In Democracy and Education , John Dewey wrote, "If we teach today as we taught yesterday, we rob our children of tomorrow." This statement rings truer than ever in the age of generative AI. Yet, lots of people are arguing about who might be committing the robbery. As much as this might be an argument about technology, it is also an argument about what constitutes necessary knowledge for students today. This short post attempts to contribute a small pebble to the mountain needed to understand that intersection, where the line between 'need to know' and 'nice to know' is continuously drawn, erased, and redrawn.

Generative AI: Redefining Curriculum and Creativity

Generative AI can create new content based on inputs or prompts. By content, I'm talking about curriculum, assignment sheets, rubrics, and the kinds of assignments students typically turn in. Tools like GPT will transform the world of work and education as it enables new forms of creativity, innovation, and problem-solving. This will also challenge and redefine what students "need to know" and what is "nice to know" in the age of AI.

Practical Implications in Context-Rich Learning

Let's delve into the practical implications of generative AI in the realm of context-rich learning within the classroom. Envision an AI tool adept at crafting lesson plans that not only cover the required curriculum but also resonate with students' interests, obsessions, or the intersections between the coursework and their identities. This approach could be particularly transformative for disengaged students, those who struggle with traditional educational models, or those who have not yet embraced a growth mindset. Such AI could analyze a range of data to suggest individualized reading materials or activities, thereby bridging gaps in understanding through connections that are personally meaningful to each student. Moreover, in the domain of writing, AI could offer prompts that ignite students' passions, provide constructive feedback on their drafts, or guide them towards resources that deepen their engagement with the subject matter.

The Educator's Role in an AI-Enhanced Classroom

This innovation prompts crucial considerations for educators: How do we ensure that the AI's recommendations align with our educational objectives? How do we strike a balance between automated assistance and the indispensable human touch in teaching? The integration of AI into our educational practices compels us to reevaluate not only our curriculum but also our roles as educators. We must evolve into facilitators who assist students in interpreting and expanding upon the insights and suggestions provided by AI, ensuring that education retains its essential human core.

Understanding the 'Need to Know' vs. 'Nice to Know'

The distinction between "need to know" and "nice to know" information comes from the book Understanding by Design by Grant Wiggins and Jay McTighe , which offers a framework for designing courses and content units called "Backward Design." They put it this way:

"Need to know" information is the core knowledge and skills that students must acquire to achieve the desired results.

"Nice to know" information is supplementary or peripheral information that may be interesting but not essential for the desired results.

The distinction between "need to know" and "nice to know" information is not static or universal. It depends on the context, the purpose, and the audience of the learning. Moreover, it changes as new technologies, discoveries, and demands emerge.

Historical Paradigm Shifts in Knowledge

As we once transitioned from celestial navigation to GPS, we now move from manual content creation to AI-assisted ideation. Yet, the fundamental skills of critical thinking and adaptability remain as 'need to know' essentials. Let's look at two examples:

Navigational Skills: From Shooting an Azimuth to GPS

Today, if you are not an Eagle Scout, in the military, a first responder, or an outdoor enthusiast, how likely are you to possess the skills to shoot an azimuth and navigate across unfamiliar terrain? How many people even know what "shooting an azimuth" is or that it was a thing you could do inside city limits by yourself at any age? Note: The phrase "shooting an azimuth" is a common term in navigation, orienteering, and land surveying. It refers to the process of determining the direction or bearing to a specific distant object, using a compass. I didn't know what an azimuth was until an Eagle Scout named Adam Sirgany gave me a course in orienteering before a major hiking trip. I'd like to be better at orienteering. But most people would file that under "nice to know," not "need to know." And if you tested me on it today, I'd likely be grouped in with the majority.

The Printing Press: A Revolution in Knowledge Access

The invention of the printing press in China by Bi Sheng around 1040 AD and later its reinvention in the West by Johannes Gutenberg in the 1450s fundamentally changed what people needed to know and what was nice to know. These innovations revolutionized the production and structure of knowledge and education. Gutenberg's movable type printing press, introduced in Mainz, Germany, around 1440-1450, allowed for the mass production of books and widespread dissemination of knowledge, which, in turn, necessitated literacy and access to information as 'need to know' for a broader population. Prior to this, such knowledge was often confined to specific classes or professions and considered 'nice to know' by the general populace. Similarly, the advent of the internet in the late 20th century, which began with the development of ARPANET in the 1960s, further shifted the paradigm, making instant access to vast amounts of information a 'need to know' and transforming the global dissemination of knowledge.

Generative AI: The Next Educational Revolution

Generative AI will do the same in the future, as it will reshape the creation and consumption of content. In fact, it already is.

The historical shifts brought about by the invention of the printing press and the advent of the internet offer valuable lessons as we navigate the emergence of generative AI in education. Just as Gutenberg's press democratized access to knowledge and reshaped literacy, generative AI promises to further democratize the creation and consumption of information. It stands to transform not only how we access knowledge but also how we interact with and produce it. This evolution echoes past revolutions in information technology, where each advancement expanded the boundaries of 'need to know' knowledge. However, with AI, the change is not just in the volume or accessibility of information but in the very nature of learning and teaching. It compels us to ask: How can we prepare students for a world where information is not only ubiquitous but also increasingly generated and curated by intelligent algorithms? How do we ensure that our educational systems evolve to help students navigate this new landscape, not just as consumers of information, but as critical thinkers and creators? Reflecting on historical shifts helps us understand that our current challenge is not just about integrating a new tool, but about adapting to a fundamentally different landscape of knowledge and learning.

The Future of Content Creation and Consumption in Education

Generative AI will change what we need to know and what is nice to know, specifically in terms of content creation and consumption. We will need to know how to use generative AI tools to enhance our creativity, innovation, and problem-solving, as well as how to detect and prevent generative AI misuse and abuse. For more about ethical considerations, please see the "Promoting Responsible Use of Generative AI" section of Ctrl+Alt+Innovate: The Radical Shift of Designers in the AI-Driven Classroom by my friends and colleagues Goran Trajkovski and Jill Walker, MSML, M.Ed.

Rethinking Pedagogy in the Age of AI

Generative AI will also change how we design and deliver education, as we will need to rethink the content and the pedagogy of our programs, courses, and lessons. We will need to focus on the "big ideas" and "essential questions" that generative AI can help us explore and answer, as well as the "need to know" information that generative AI can help us acquire and apply.

We will also need to involve students in the process of identifying and exploring the "need to know" and "nice to know" information, as well as the generative AI tools and techniques that can help them learn and create.

Questions for You about the Future of Learning in an AI-Driven World

What do you think high school graduates need to know? What about college students? What do they need to know–or be able to do off the grid–without power, electricity, or an electronic device? What part of that knowledge needs to be collapse-proof? How do those questions become more problematic when we consider learners like me who have ADHD, Dyslexia, or any other disability?

Thanks

While the responsibility for any misstep in this article is mine alone, anything you might have enjoyed likely has roots in conversations, lessons, and readings with and from people cited and linked above, as well as these folks: Lauren Mason Carris ✨ , Jason Gulya , Matt Lewis , Ethan Mollick , Zia Nizami , Peter Saddington , and Ben Thomas .

#AI #education #edtech #collapse-proof #generativeAI #instructionaldesign

Please note:

1. No azimuths were harmed in the making of this article.

2. The comments made herein are the author's own and not necessarily a reflection/an opinion/stance of any employer.