Dr. Micah Shippee is the Director of Education Technology Consulting and Solutions at Samsung. Micah and his team design, develop and deliver learning solutions to inspire and empower educators and learners. The team strives to amplify the meaningful work in education by supporting the adoption of Samsung innovation. Micah operates at the intersection of practice and research as a veteran consultant and professor specializing in planned change and innovation, learning theory, project management, and organizational behavior. His efforts focus on the adoption and deployment of new technological innovations in organizations. As an educator, author, consultant, and keynote speaker, he focuses on the adoption of innovation through the development of cultures that embrace change. Micah is the author of WanderlustEDU: An Educator’s Guide to Innovation, Change, and Adventure and co-author of Reality Bytes: Innovative Learning Using Augmented and Virtual Reality.
Artificial Intelligence (AI) is here to stay. The promise of streamlined workflows, a shorter workweek, and overall convenience in our daily lives has solidified AI’s place in our world. AI is the fruit of the 4th Industrial Revolution (4IR) characterized by the convergence of digital, biological, and physical technologies, creating new opportunities and challenges for businesses, society, and education.
AI can be a valuable tool for educators, helping scale good practice and effectively reach more students. While AI can be used to automate tasks, provide personalized feedback, and create immersive learning experiences, our conversation must remain centered on our students’ needs. Educators strive to codify the necessary skills from the future world their students will find themselves in and to backward design curriculum (knowledge) to scale their instruction. The effort of educators to prepare our students for their future is the critical focus of education and has always been their North Star.
Moving Beyond the 4C’s – 4IR Skills Acquisition
Since 2004, the “Four Cs”: Critical Thinking, Communication, Collaboration, and Creativity and Innovation, became a lauded guide for preparing students for the future. Needless to say, our society has changed dramatically since then, now 20 years later we must revisit our understanding of what our students need for the next 20 years and beyond.
One way of framing our reflection is through the context of the Industrial Revolution era. These eras strive to organize the historical processes of change from an agrarian and handicraft economy to one dominated by industry and machine manufacturing and beyond. The changes innovations have brought to us have introduced novel ways of working and living and fundamentally transformed our society. We can define these eras as:
| Era | Century | Realization | |
| First | 18th Century | Mechanization: Steam, Water, Mechanica production equipment
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| Second | 19th Century | Mass Production: Division of Labor, Electricity, Mass Production, Assembly Line
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| Third | 20th Century | Automation: Electronics, Computers, Automated Production | |
| Fourth
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21st Century | IoT and AI: Cyber-physical systems | |
One of the most significant impacts of 4IR is the rise of AI, which is already being used in a variety of ways, from automating tasks to powering self-driving cars. As AI continues to develop, it will have an even greater impact on our lives and work. How then do we begin to reframe our thinking about education?
While our teaching and learning environments are changing, our motivation to prepare students for a successful future remains steadfast. Our perspective on the skills and knowledge required for success will become more clear as we struggle through necessary iterations of our thinking.
What are the skills of the future workplace?
As discussed, we often talk about future-ready skills as related to the 4C’s, yet the 4IR skill demand requires an unpacking and reframing of these skills to appear more focused on:
- Upskilling: becoming an expert in changing with new demands is a critical skill for our future.
- Intuitive Decision Making: retaining the human-ness in decision-making as a form of auditing AI output.
- Rational Decision Making: when leveraging AI support in decision-making, we must be prepared to ask: Does that data really lead to that conclusion logically? Is this how we humans operate?
- Collaborative Innovation: Peer to Peer as well as Human to AI. Our capacity to work with other human beings to solve problems and understand every diverse perspective is critical. Further, it is critical that our learners are prepared to collaborate with AI and future innovation.
- Societal Systems: While an AI might direct us to a more effective and efficient societal structure (from an algorithmic perspective) we must pause and leverage both the intuitive and rational decision-making that is uniquely human, to assess whether these recommendations are truly what is best for our society asking: Is this how people really want to live? Have we retained our sense of community? Belongingness… a trait not computationally logical.
Strategically teaching these skills in the absence of a meaningful curriculum will serve fruitless. Knowledge, as framed by the curriculum, must increasingly be more globalized in its objectives and less hyper-focused on regional needs. The very nature of an educational curriculum is to serve as a dynamic, iterative cultural artifact. Historically, educational curricular shifts have gone from Socratic dialogue to expert-apprentice programs, to humanities enrichment, to STEM focus, each with varying degrees of equitable access. Gaps in equity have led to economic-driven curricular designs (ex. STEM/STEAM). With ever-increasing access to learning, we must refocus our curricular priorities to address the needs of our global society. With this in mind, we need a globalized curriculum that is culturally responsive and iterative in nature seeking the good of humanity over governmental initiatives. Next, we focus on the instructional practice, or pedagogy, of knowledge acquisition for educators powered by AI.
Instruction – Assessment in 4IR Knowledge Acquisition
The growth of AI tools is both challenging the processes by which we engage our students and providing opportunities to effectively and efficiently scale well-established instructional practices (pedagogy). Instructional assessment processes, that is, how we measure skill and knowledge acquisition, have always been a central component of education. Traditionally, educators have worked to balance summative assessment and formative assessment.
The goal of summative assessment is to evaluate student learning at the end of instruction, often leveraging pre-existing benchmarks. This is an end-product, rather than a process-focused, area of assessment. What does this matter for AI? One example that illustrates AI’s relevance is if the summative assessment in a class is a take-home essay, can an educator truly trust who, or what is doing the work? When responses were copied/pasted off of a website educators learned how to flag the work but AI tools are increasingly unique responses that are challenging to identify for authenticity.
The exciting contribution AI brings to assessment is in the formative assessment realm. Here the goal is to monitor ongoing student learning and to provide meaningful feedback. It is difficult for an individual educator to give this type of ongoing feedback consistently for large groups of students. AI can be a powerful tool to personalize learning for students with rapid, in-the-moment feedback to nurture their comprehension during their learning process.
Instruction – Scaling Effective Practice in 4IR Knowledge Acquisition
AI, like all technology, is an amplifier, in an educational setting, AI amplifies both good practice and strategies that need improvement. The Apprenticeship Model, at its core, is arguably the most natural, effective form of teaching and learning, and has remained amongst modern humankind’s greatest scalability challenges. We have filled auditoriums with eager learners, designed self-paced courses, and explored countless feedback systems, all to replicate the painfully obvious: a human touch in learning is incredibly powerful but horribly unscalable. Here an expert works individually with a learner to model, guide, provide feedback, challenge, and validate the learner’s progress.
Although apprenticeships can differ widely from one context to another, they typically have some or all of the following features:
- MODELING – the educator carries out the task, simultaneously thinking aloud about the process, while the learner observes and listens.
- COACHING – as the learner performs the task, the educator gives frequent suggestions, hints, and feedback.
- SCAFFOLDING – the educator provides various forms of support for the learner, perhaps by simplifying the task, breaking it into smaller and more manageable components, or providing less complicated equipment.
- ARTICULATION – the learner explains what they are doing and why, allowing the educator to examine the learner’s knowledge, reasoning, and problem-solving strategies.
- REFLECTION – the educator asks the learner to compare their performance with that of experts, or perhaps with an ideal model of how the task should be done.
- INCREASING COMPLEXITY AND DIVERSITY OF TASKS – as the learner gains greater proficiency, the educator presents more complex, challenging, and varied tasks to complete.
- EXPLORATION – the educator encourages the learner to frame questions and problems on their own, and in doing so to expand and refine acquired skills.
While this tried and true method of learning is effective in a 1:1 scenario, it has historically proven a challenge to scale to a larger student audience. AI has the potential to make the apprenticeship model a highly effective pedagogical practice. The ai-apprenticeship model finds the educator MODELING a task for their students (while explaining their thought process) and then directing their students to a self-guided, AI-powered, series of tasks which are responsively enabled to appropriately challenge the learner to achieve expertise (COACHING, SCAFFOLDING, ARTICULATION, REFLECTION, and INCREASING COMPLEXITY AND DIVERSITY OF TASKS). The educator has been enabled to work 1:1 with students requiring support beyond what the AI is offering. Finally, with developments in AI students now have a new set of tools that they can use to produce AI generative products, via question prompts, that represent their content-area expertise (EXPLORATION). The entire time an authentic discourse is imperative to humanize the process with front-of-classroom check-ins and student-led summary dialogue.
Conclusion
We are hearing of innovative educators modeling AI applications through transparent examples with their students, identifying the pros and cons of the technology as it exists today. The modeling approach, where the educator speaks openly about how they use AI, and even explain their reservations, is a life-skill lesson on having grit, or agency, to persevere in overcoming a new challenge. By demonstrating a level of stick-to-it-tiveness that we all need in life, educators are using AI as a use case that has provided opportunities for their students to have clear access to the skill of agency in action.
Change is uncomfortable and sometimes scary but change whether forced (ex. COVID teaching) or by choice, has the potential to guide us to make the world a better place for our children. The conversation about knowledge and skill development is never settled, nor should it be. AI and 4IR have positioned us to iterate, continually ask questions about the status quo, and challenge ourselves to prepare our learners for a successful future.