How do you train to solve the unknown?
Picture yourself just before the creation of the World Wide Web in 1989. Imagine knowing before anyone else the impact the internet would have. What would you say are the most important skills that need to be taught to the ‘new generation’? This is not an easy question to answer. In most cases, what we saw between the mid-1990s and 2000s was that education systems were focused on helping students learn how to use certain technology tools, not on how to succeed in a society reshaped by the Internet.
Now let’s “back to the future” and jump to where we find ourselves today. An image you know before anyone else in how artificial intelligence (AI) and data-intensive digital services will shape the next decade. What would you say are the most important skills that need to be taught to the generation of students currently in school? This is not an easy question to answer either. In many cases, education systems still focus primarily on helping students learn to use certain technology tools. Maybe we can do better this time.
There are many approaches to how to develop digital skills today. Depending on the accepted framework, digital skills, ’21. may or may not be included in what are called ‘century skills’. Much of the literature emphasizes the relevance of these capacities to the world that is coming (in some cases, a world that is already here). There is no single framework to use as a definitive reference (although some have more visibility than others): different countries/regions have decided to assess and measure these skills using different techniques and approaches.
Perhaps one of the differences between 1989 and 2019 is that today’s societies are more aware of the impact of digital technologies on almost every aspect of our lives. Given that our current use of technology has become more complex, it is reasonable to expect the relevant skills and knowledge required to become more complex. As we will see, this complexity is not related to how difficult it is to interact with certain tools (simplicity is the golden rule in technology), but rather to their capacity to think critically and evaluate contexts.
The current expansion of “intelligent technologies” (adaptive, predictive, personalized) can make our lives easier in some cases (for example, helpful chatbots or robot sitters). However, it is also true that we are much more aware of some undesirable (in some cases negative) consequences of these new technologies than in the recent past.
Some people may be bothered to think that AI or robots could be part of the conversation in education. The unknown tends to produce fear or rejection. On the other hand, perhaps it is time to consider how we can better prepare the next generation to thrive in contexts where data-intensive systems can help or replace a range of skills and capacities already developed in schools.
Learning how to interact (understand, use, cooperate, act, trust, feel) with robots may no longer be science fiction. Joseph Aoun, author of Robot-Proof, argues that reading, writing, and math are essential abilities for modern society. But now there are additional difficulties. In addition, at least three literacy levels are required: data literacy (to read, analyze and use an ever-increasing flow of information); technological literacy (including coding and understanding how machines work); and human literacy (understanding how to function in a human environment).
Before taking a strong position on how AI can play a role in education, it might be a good idea to remember that humans today have all sorts of intelligences and capacities that go far beyond what narrow AI can do. Rosemary Luckin emphasizes that human intelligence is extremely rich and diverse. When considering social intelligence, emotional intelligence and self-efficacy, Professor Luckin argues that one of the potential roles of AI in education is to provide opportunities to increase human intelligence with AI that supports decision-making processes rather than replacing humans through automation.
Neil Selwyn, professor of education at Victoria’s Monash University and author of a new book, Should Robots Replace Teachers? He suggests that “the concern is not that teachers will be replaced, but that they will be displaced or deprofessed.” In this case, research shows that educators may need support and guidance to adopt and teach these new knowledge and languages, and their role during the learning experience of students remains important.
Which would you rather be: passenger or driver?
Expanding policies that support the development of computational thinking capacities is something that has gained visibility and relevance. Countries like the UK have decided to adopt computational thinking as a central component of their national curricula. Today it is possible to find a growing number of countries (and civil society initiatives) that encourage learning not only how to use technologies, but also how to create new ones. Perhaps one of the most interesting questions in this context concerns whether to teach computational thinking. Related to this or incorporating it into different disciplines, it has integrated it as “cross-literacy”. Both approaches have their pros and cons; It is quite possible that this will continue to be an ongoing conversation.
Supporters of computational thinking stress that it’s not about coding, it’s about understanding how technology works as a driver and its impact on today’s society, not from the passenger seat. As recently announced by the OECD, the emphasis in the 2021 PISA assessment was on the processes and mental models (e.g., abstraction, algorithmic thinking, automation, decomposition and generalization) that students need to succeed in an increasingly technological world.
Interestingly, the more social the experience of using technologies, the closer the link between digital skills and so-called social-emotional skills. Learning to code will be just as important as learning to code. Identifying new problems can lead us to change the way we view today’s skills. Here are some examples of cross-competencies that illustrate socio-technical capacities that might be useful to consider:
• Algorithmic thinking: To what extent can the information presented by algorithms influence ideas, feelings or decisions? How, when and for what purpose can automated systems affect people’s lives? How to understand the potential cost of automated decisions? How to develop algorithm awareness to deal with potential bias?
• Intelligent skepticism: How to develop selective confidence to deal with deep fake news or fake news? What techniques, protocols or good practices can help us select reliable information? How to manage trust in data-intensive environments? How to encourage scientific thinking with independent thinking, demanding evidence, and even certain doses of skepticism?
• Ethical fluency: How to instill ethical thinking into the design, implementation and adoption of information technologies? How to integrate privacy and data protection into every phase of technology adoption? How to go from “move fast and break things” to work that benefits your community but doesn’t negatively impact others?
• Self-regulation: How to self-regulate one’s behavior, emotions and thoughts in different digital environments, especially in situations where they may affect others (or yourself) in contexts of hyperarousal and hyperconnection? What are the best strategies for maintaining the focus of attention online?
Some people may want to learn about AI4K12. This North American community of scientists promotes national guidelines (not curriculum) for AI education for K-12 and argues that nearly everyone will need a basic understanding of machine learning and the technologies that support AI. They argue that students should understand and evaluate new AI technologies and critically consider the ethical or societal impact questions posed by them.
The future of education raises a number of challenging questions: If machines are learning, what should we teach non-machines? How should we design future capacities for future generations? Rather than educating students to cope with today’s technologies, how can we better prepare them to make sense of tomorrow’s complex or unknown problems? What are the basic knowledge and capacities that will not get old? And just as important: How can educators (as well as other experts) be involved in this conversation?
We always predict the future, and we’re always wrong about that. While we can’t predict the future, narrowing the time between 1989 and 2019 offers an opportunity to think ahead and design transformative solutions where more people, not just passengers in other people’s vehicles, can become drivers of their destinations.
Writer: Tuncay Bayraktar