Getting Ready for “Smart” Learning

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What happens in teaching, learning and training settings as artificial intelligence-enabled content generation tools begin performing many of the same cognitive and creative tasks as people do? 

What happens as “smart technologies” appear on the horizon that can actually perform many of the cognitive tasks that, in the past, people have been hired to do?

Educational professionals and ed tech user communities alike are simultaneously transfixed, inspired and terrified by the growing wave of generative tools and solutions, fueled by AI, that increase in number every day.  The growing interest around the rapid deployment of “smart tools” for teaching and learning is setting off simultaneous waves of feeling the “Shock of the New” (per Udell & Woodill, 2019) interspersed with plenty of ed tech “FOMO” – Fear Of Missing Out.

In the face of all these developments, many knowledge industry professionals and members of creative communities are finding themselves wondering what it is going to take to remain successful and competitive in environments where bots can write copy and words produce illustrations with just a few prompts.

Navigating Digital Transformation: What Does AI mean for Learning Professionals?

It’s time to take stock of what we can do to take immediate steps to prepare for this next wave of Digital Transformation coming our way. 

First, let’s get grounded. These tools ARE going to change the way we work; that’s what Digital Transformation is all about. It is an ongoing evolutionary process that affects everyone in an enterprise.  It starts with relatively minor modifications to how we do some of the ways we do things in our daily operations, and eventually introduces entirely different ways of doing and thinking about every single thing we do. 

Digital Transformation isn’t an initiative, per se; it’s better imagined as a result, the conclusion of significant efforts to do things better. It occurs as a result of research, evaluation, application, revision and dedication to continuous positive improvement. Every time there is a spike of interest in new tools, such as the kind being produced to demonstrate how AI can be used to transform current digital activity, there is an accompanying swirl of effort to explore more deeply, evaluate more rigorously, test and revise to continuously improve.

Similarly, AI is not a monolithic “thing” – Neural Networks are different from Recommendation engines are different from Robotics are different from Large Language Models are different from Deep Learning. Rather, AI is it an aggregation of documented human learning, expressed in code and formulae and digital representations at a scale that can mimic reasoning of the human brain. It gets smarter from ingesting all of the digital information assets these platforms can consume, metaphorically speaking. Individuals will need stop thinking they can solve complex problems, on their own, without taking advantage of smart tools to achieve better results. 

To successfully navigate the coming waves of digital transformation coming from AI tools, the first thing we can do is to consider some un-learning. We will need to revisit the approach we currently use and see where we may be able to flex. We will need to let go, make some room, and start discovering new ways to take advantage of the intelligent tools that are beginning to emerge. It may be helpful to start considering some of the things that we can each be doing to can be done to take full advantage of the power and capacity that different kinds of AI apps and tools bring to the current conversations about learning and work. 

To harness the full potential of AI, individuals must be willing to avoid rigid, siloed thinking patterns that are no longer valid or relevant. We will need to develop a more fluid understanding of diverse fields and their interconnections.  We will need to adopt a “growth mindset” that embraces continuous learning and being open to change. This mindset may necessitate unlearning fixed beliefs about abilities and reevaluating long-held assumptions about qualifications of competence, or the ways that things must be done. We’re going to need to use data to support decisions.

The rapidly evolving nature of AI technology requires individuals and organizations to be highly adaptable. Again, unlearning old ways of thinking and doing things and being definitive-yet-flexible about what really matters enables teams to be more agile and responsive to change.  Embracing AI technologies will expose us to the unanticipated, helping us uncover novel solutions to challenges that we may not have considered before, leading to groundbreaking discoveries and advancements. In many cases, AI systems will augment human capabilities rather than replace them. Unlearning our traditional human-centric approach to problem-solving and decision-making will allow us to collaborate more effectively with AI systems, creating a powerful synergy that drives innovation and productivity.

These are early days for appreciating the opportunities and challenges that AI will bring to learning and instructional design. One should be mindful of the adage making its way across the industry: even though Artificial Intelligence may not take your job, it could be that somebody with good AI skills will. 

References

Stanford University Center for Human-Centered Artificial Intelligence (2023)

The Artificial Intelligence Index Report, 2023. Palo Alto, CA: Stanford University

Udell, C. & Woodill, G. (2019) Shock of the New: The Challenge and Promise of Emerging Technology. Alexandria, VA: ATD Press

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