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Can education systems anticipate the challenges of AI? (IIEP-UNESCO strategic debate)

I was honoured to be a discussant in the IIEP-UNESCO strategic debate in Paris on the question Can education systems anticipate the challenges of AI?

Stuart Elliot, author of the OECD report ‘Computers and the Future of Skill Demand, was the main presenter with an exciting framework to help understand the impact of AI on skills and education using the OECD’s PIAAC data.

Stuart’s slides are here. My slides and the full video recording.

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Voice for Development: Your Weekend Long Reads

While ICT4D innovates from the ground up, most tech we use comes from the top. Yes, it takes a little time for the prices of commercial services in Silicon Valley to drop sufficiently, and the tech to diffuse to the audiences we work with, but the internet and mobile have made that wait short indeed.

Next Big Wave After the Keyboard and Touch: Voice

One such innovation is natural language processing, which draws on AI and machine learning to attempt to understand human language communication and to react and respond appropriately.

While this is not a new field, the quality of understanding and speaking has improved dramatically in recent years. The Economist predicts that voice computing, which enables hands-off communication with machines, is the next and fundamental wave of human-machine interaction, after the keyboard and then touch.

The prediction is driven by tech advances as well as increasing uptake in the consumer market (note: in developed markets): last year Apple’s Siri was handling over 2bn commands a week, and 20% of Google searches on Android-powered handsets in America were input by voice.

Alexa Everywhere

Alexa is Amazon’s voice assistant that lives in Amazon devices like Echo and Dot. Well, actually, Alexa lives in the cloud and provides speech recognition and machine learning services to all Alexa-enabled devices.

Unlike Google and Apple, Amazon is wanting to open up Alexa and have it (her?) embedded into any products, not just those from Amazon. If you’re into manufacturer, you can now buy one of a range of Alexa Development Kits for a few hundred dollars to construct your own voice-controlled products.

Skills Skills Skills

While Amazon works hard to get Alexa into every home, car and device, you can in the meantime start creating Alexa skills. There’s a short Codecademy course on how to do this. It explains that Alexa provides a set of built-in capabilities, referred to as skills, that define how you can interact with the device. For example, Alexa’s built-in skills include playing music, reading the news, getting a weather forecast, and querying Wikipedia. So, you could say things like: Alexa, what’s the weather in Timbuktu.

Anyone can develop their own custom skills by using the Alexa Skills Kit (ASK). (The skills can only be used in the UK, US and Germany, presumably for now.) An Amazon user “enables” the skill after which it works on any of her Alexa-enabled devices. Et voilà, she simply says the wake phrase to access the skill. This is pretty cool.

What Does This Mean for ICT4D?

Is the day coming, not long from now, when machine-based voice assistants are ICT4D’s greatest helpers? Will it open doors of convenience for all and doors of inclusion for people with low digital skills or literacy? Hmmm. There’s a lot of ground to cover before that happens.

While natural language processing has come a looooong way, it’s far from perfect either. Comments about this abound — this one concerning Question of the Day, a popular Alexa skill:

Alexa sometimes does not hear the answer correctly, even though I try very hard to enunciate. It’s frustrating when I’ve gotten the answer right — not even by guessing, but actually knew it — and Alexa comes back and tells me I’ve gotten it wrong!

In ICT4D, there’s isn’t always room for error. What about sensitive content and interactions that can easily go awry? Is it likely that soon someone will say Alexa, send 100 dollars to my mother in the Philippines? What if she sends the money to the brother in New Orleans?

Other challenges include Alexa’s language range, cost, the need for online connectivity and, big one, privacy. There is a risk in being tied to one provider, one tech giant. This stuff should be based on open standards.

Still, it is interesting and exciting to see this move from Amazon and contemplate how it could affect ICT4D. What are your thoughts for how voice for development (V4D) could make a social impact?

Here’s a parting challenge to ICTWorks readers: Try out Amazon Skills and tell us whether it’s got legs for development? An ICT4D skill, if you will. (It can be something simple for now, not Alexa, eliminate world poverty).

Image: CC-BY-NC by Rob Albright

Five Traits of Low-literate Users: Your Weekend Long Reads

We know that the first step to good human-centered design is understanding your user. IDEO calls this having an empathy mindset, the “capacity to step into other people’s shoes, to understand their lives, and start to solve problems from their perspectives.”

Having empathy can be especially challenging in ICT4D since the people we develop solutions for often live in completely different worlds to us, literally and figuratively.

I’m currently drafting a set of guidelines for more inclusive design of digital solutions for low-literate and low-skilled people. (Your expert input on it will be requested soon!) There are many excellent guides to good ICT4D, and the point is not to duplicate efforts here. Rather, it is to focus the lens on the 750 million people who cannot read or write and the 2 billion people who are semi-literate. In other words, likely a significant portion of your target users.

Globally, the offline population is disproportionately rural, poor, elderly and female. They have limited education and low literacy. Of course people who are low-literate and low-skilled do not constitute a homogeneous group, and differences abound across and within communities.

Despite these variances, and while every user is unique, research has revealed certain traits that are common enough to pull out and be useful in developing empathy for this audience. Each has implications for user-centered design processes and the design of digital solutions (the subject of future posts).

Note: much of the research below comes from Indrani Medhi Thies and the teams she has worked with (including Kentaro Toyama) at Microsoft Research India, developing job boards, maps, agri video libraries and more, for low-literates. If you do nothing else, watch her presentation at HCI 2017, an excellent summary of twelve years of research.

Not Just an Inability to Read

Research suggests that low exposure to education means cognitive skills needed for digital interaction can be underdeveloped. For example, low-literate users can struggle with transferring learning from one setting to another, such as from instructional videos to implementation in real life. Secondly, conceptualising and navigating information hierarchies can be more challenging than for well educated users (another paper here).

Low-literate Users Are Scared and Sceptical of Tech

Unsurprisingly, low literate users are not confident in their use of ICTs. What this means is that they are scared of touching the tech for fear of breaking it. (There are many schools in low-income, rural areas where brand new donated computers are locked up so that nobody uses and damages them!)

Further, even if they don’t break it, they might be seen as not knowing how to use it, causing embarrassment. When they do use tech, they can be easily confused by the UI.

Low-literate users can lack awareness of what digital can deliver, mistrust the technology and doubt that it holds information relevant to their lives.

One of Multiple Users

Low-income people often live in close-knit communities. Social norms and hierarchies influence who has access to what technology, how information flows between community members and who is trusted.

Within families, devices are often shared. And when low-literates use the device it may be necessary to involve “infomediaries” to assist, such as read messages, navigate the UI or troubleshoot the tech. Infomediaries can also hinder the experience when their “filtering and funnelling decisions limit the low-literate users’ information-seeking behaviour.”

The implication is that the “target user” is really plural — the node and all the people around him/her. Your digital solution is really for multiple users and used in mediated scenarios.

Divided by Gender

Two thirds of the world’s illiterate population are women. They generally use fewer mobile services than men. In South Asia women are 38% less likely than men to own a mobile phone, and are therefore more likely to be “sharing” users. Cultural, social or religious norms can restrict digital access for women, deepening the gender digital divide. In short, for low-literate and low-income users, gender matters.

Driven by Motivation (Which Can Trump Bad UI)

While we often attribute successful digital usage to good UI, research has shown that motivation is a strong driver for task completion. Despite minimum technical knowledge, urban youth in India hungry for entertainment content traversed as many as 19 steps to Bluetooth music, videos and comedy clips between phones and PCs.

In terms of livelihoods and living, the desire to sell crops for more, have healthier children, access government grants or apply for a visa, are the motivators that we need to tap to engage low-literate users.

If “sufficient user motivation towards a goal turns UI barriers into mere speed bumps,” do we pay enough attention to how much our users want what we’re offering? This can make or break a project.

Image: © CC-BY-NC-ND by Simone D. McCourtie / World Bank

Artificial Intelligence in Education: Your Weekend Long Reads


Continuing the focus on artificial intelligence (AI), this weekend looks at it in education. In general, there are many fanciful AI in Ed possibilities proposed to help people teach and learn, some of which are genuinely exciting and others that just look much like today.

One encouraging consensus from the readings below is that, while there is concern that AI and robots will ultimately take over certain human jobs, teachers are safe. The role relies too much on the skills that AI is not good at, such as creativity and emotional intelligence.

An Argument for AI in Education

A 2016 report (two-page summary) from Pearson and University College London’s Knowledge Lab offers a very readable and coherent argument for AI in education. It describes what is possible today, for example one-on-one digital tutoring to every student, and what is potentially possible in the future, such as lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies – in and beyond school. Or, one day, there could be new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. It also proposes three actions to help us get from here to there.

AI and People, Not AI Instead of People

There is an argument that rather than focusing solely on building more intelligent AI to take humans out of the loop, we should focus just as much on intelligence amplification/augmentation. This is the use of technology – including AI – to provide people with information that helps them make better decisions and learn more effectively. So, for instance, rather than automating the grading of student essays, some researchers are focusing on how they can provide intelligent feedback to students that helps them better assess their own writing.

The “Human Touch” as Value Proposition

At Online Educa Berlin last month, I heard Dr. Tarek R. Besold, lecturer in Data Science at City, University of London, talk about AI in Ed (my rough notes are here). He built on the idea that we need to think more carefully about what AI does well and what humans do well.

For example, AI can provide intelligent tutoring, but only on well-defined, narrow domains for which we have lots of data. Learning analytics can analyse learner behaviour and teacher activities … so as to identify individual needs and preferences to inform human intervention. Humans, while inefficient at searching, sorting and mining data, for example, are good at understanding, empathy and relationships.

In fact, of all the sectors McKinsey & Company examined in a report on where machines could replace humans, the technical feasibility of automation is lowest in education, at least for now. Why? Because the essence of teaching is deep expertise and complex interactions with other people, things that AI are not yet good at. Besold proposed the “human touch” as our value proposition.

Figuring out how humans and AI can bring out the best in each other to improve education, now that is an exciting proposal. Actually creating this teacher-machine symbiosis in the classroom will be a major challenge, though, given the perception of job loss from technology.

The Future of AI Will Be Female

Emotional intelligence is increasingly in demand in the workplace, and will only be more so in the future when AI will have replaced predicable, repetitive jobs. This means that cultivating emotional intelligence and social skills should be critical components of education today. But there’s a fascinating angle here: in general, women score much higher than men in emotional intelligence. Thus, Quartz claims, women are far better prepared for an AI future.

Image: © CC-BY-NC-ND by Ericsson

Artificial Intelligence: Your Weekend Long Reads

Artificial intelligence (AI) was one of the hottest topics of 2017. A Gartner “mega trend,” their research director, Mike J. Walker, proposed that “AI technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data and unprecedented advances in deep neural networks.”

But as much as it is trendy and bursting with promise, it is also controversial, overhyped and misunderstood. In fact, it has yet to enjoy a widely accepted definition.

AI underpins many of Gartner’s emerging technologies on its 2017 hype cycle. However, smart robots, deep learning and machine learning were all cresting the Peak of Inflated Expectations. Of course, after that comes the Trough of Disillusionment. Collectively they will take two to ten years to reach the Plateau of Productivity.

AI is both a long game and already in our lives. Your Amazon or Netflix recommendations are partly AI-based. So is speech recognition and translation,  such as in Google Home and Google Translate. But, as you know from using these services, they are far from perfect. Closer to ICT4D, within monitoring and evaluation we know the opportunities and limitations of AI.

In 2018 we can expect to hear a lot more about AI, along with promises and disappointments. Almost anyone who’s software has an algorithm will claim they’re harnessing AI. There will suddenly be more adaptive, intelligent platforms in edtech, and more talk of smart robots and AI hollowing out the global job market.

While there will be some truth to the AI claims and powerful new platforms, we need to learn to read between the lines. The potential of AI is exciting and will be realised over the coming years and decades, but in varying degrees and unevenly spread. For now, a balanced view is needed to discern between what is hype or on the long horizon, and what can we use today for greater social impact. Only in this way can we fully get to grips with the technological, social and ethical impact of AI. Below are a few articles to get our interest piqued in 2018.

The Next Fifteen Years

To get the big picture, an excellent place to start is the Stanford University report Artificial Intelligence and Life in 2030. A panel of experts focussed the AI lens on eight domains they considered most salient: transportation; service robots; healthcare; education; low-resource communities; public safety and security; employment and workplace; and entertainment. In each of these domains, the report both reflects on progress in the past fifteen years and anticipates developments in the coming fifteen years.

AI for Good

Last year the ITU hosted the AI for Good Global Summit, which brought together a host of international NGOs, UN bodies, academia and the private sector to consider the opportunities and limitations of AI for good. The conference report offers a summary of the key takeaways and applications cited in the event. A number of webcasts are also available.

AI Moves into the Cloud

While most ICT4D tech outfits simply don’t have access to the computing power and expertise to fully utilise AI, this is starting to change. In 2017, AI floated into the cloud. Amazon, Google and Microsoft have introduced large-scale cloud-based AI. This includes open-source AI software as well as AI services for turning speech in audio files into time-stamped text, translating between various languages and tracking people, activities, and objects in video. I’m looking forward to seeing these tools used in ICT4D soon.

Growing Up with Alexa

Considering the interaction between her four-year-old niece and Amazon Echo’s Alexa, a reporter asked the following question: What will it do to kids to have digital butlers they can boss around? What is the impact of growing up with Alexa? Will it make kids better adjusted and educated — or the opposite? This piece offers interesting questions on the social impact of AI on children.

The Ethical Dimension

The World Commission on the Ethics of Scientific Knowledge and Technology of UNESCO (COMEST) last year released a report on the ethical issues surrounding the use of contemporary robotic technologies — underpinned by AI — in society (there is a 2-minute video summary). The bottom line: some decisions always require meaningful human control.

Amidst the growing role of robots in our world there are new responsibilities for humans to ensure that people and machines can live in productive co-existence. As AI impacts our world in greater ways, the ethical dimension will equally become more important, bringing philosophers, technologists and policy-makers around the same table. Being in the ICT4D space, our role as technologists and development agents will be critical here.

Image: © CC-BY-SA by Silver Blue

Fake News – Weekend Long Reads

We live in an era, according to the Economist, that is post-truth. Especially in politics, this time sees “a reliance on assertions that ‘feel true’ but have no basis in fact.” In 2016, post-truth was the Oxford Dictionaries Word of the Year.

Untruths have always been with us, but the internet is the medium that changed everything. The scale with which “alternative facts“, untruths and blatant lies can be created and spread — by people and algorithms — can, for the first time ever, threaten democracy and social cohesion at a global scale.

For those of us who have, for a long time, believed in the power of the internet to break down barriers between people and cultures, foster dialogue, have a sharpening effect on truth through increased transparency and access to information, post-truth’s most dangerous weapon, “fake news“, is a bitter pill to swallow. While fake news has been around since the late 19th century, it is now a headline phenomenon, the Collins’ Word of the Year for 2017. What happened to the grand internet dream of the democratisation of knowledge?

All of us have a duty to engage with these complex issues, to understand them, take a position, and reclaim the dream. Most importantly, we need to constantly question whether the digital tools we built, and continue to build, are part of the problem.

The Birth of a Word

It is useful to go back only a year and a half to remind ourselves how fake news became a household word. WIRED’s article traces the the birth — and, it claims — the death of it. How did it die? It quickly became so diluted in meaning, so claimed by those shouting the loudest, that it has become meaningless in many ways.

Fake News, or Information Disorder?

In an attempt to bring structure to the discussions, the Council of Europe produced a report on what it calls information disorder. The authors refrain from using the term fake news, for two reasons. First, they believe it is “woefully inadequate” to describe a very complex issue, and, secondly, it has been appropriated by politicians to slam any news or organisation they find disagreeable, thus becoming a mechanism for repression — what the New York Times calls “a cudgel for strongmen”.

The authors introduce a new conceptual framework for examining information disorder, identifying three different types:

  • Mis-information is when false information is shared, but no harm is meant. (According to Open University research, misinformation is rife among refugee populations.)
  • Dis-information is when false information is knowingly shared to cause harm.
  • Mal-information is when genuine information is shared to cause harm, often by moving information designed to stay private into the public sphere.

The report concludes with excellent recommendations for technology companies, as well as a range of other stakeholders. If the report is too long for you, be sure just to read the recommendations.

Fight It With Software

Tom Wheeler at the Brookings Institute offers a history of information sharing, control and news curation. He laments that today the “algorithms that decide our news feed are programmed to prioritize user attention over truth to optimize for engagement, which means optimizing for outrage, anger and awe.” But, he proposes: “it was software algorithms that put us in this situation, and it is software algorithms that can get us out of it.”

The idea is “public interest algorithms” that interface with social network platforms to, at an aggregate level, track information sources, spread and influence. Such software could help public interest groups monitor social media in the same way they do for broadcast media.

Fight It With Education

While I believe in the idea of software as the solution, the Wheeler article seems to miss a key point: information spread is a dance between algorithms and people. Every like, share and comment by you and me feeds the beast. Without us, the algorithm starves.

We need to change the way we behave online; media and information literacy are crucial to this. There are many excellent resources for teens, adults and teachers to help us all be more circumspect online. I like the Five Key Questions That Can Change the World (from 2005!)

Want To Understand It Better? Fake Some

Finally, long before fake news become popular, in 2008, Professor T. Mills Kelly got his students at George Mason University to create fake Wikipedia pages to teach them the fallibility of the internet. At Google’s Newsgeist unconference last month, a similar exercise involved the strategising of a fake news campaign aimed at discrediting a certain US politician. Both instances force us to get into the minds of fakesters and how to use the internet to spread the badness. While creating fake Wikipedia pages doesn’t help the internet information pollution problem, the heart of the exercises are useful — perhaps they should be part of media literacy curricula?

Thanks to Guy Berger for suggesting some of these articles.

Image: © CC-BY-NC .jeff.

Online Educa Berlin 2017 – rough notes

I recently attended my first Online Educa Berlin conference and found it to be very interesting. With over 2,000 attendees there are enough sessions for you to really dive into whatever is your particular edtech passion. There are also a large number of exhibitors. The focus of the event is largely US and European, but for me this was a breath of fresh air after almost always attending developing country events.

I presented the key findings from the forthcoming landscape review Digital Inclusion for Low-skilled and Low-literate People.

Below are my rough notes from the event, with key takeaways in highlight.

Learning and Working Alongside AI in Everyday Life

Donald Clark, Plan B Learning, UK

  • Recommended book: Janesville: An American Story.
  • 47% of jobs will be automated in next 20 years — claimed by Frey and Osborne, 2013. He says it’s not true.
  • Top 10 market cap companies in the world: 8 of 10 use AI or tech.
  • AI is already in our lives. We have all watched a Netflix show or bought a book on Amazon because of a software-based recommendation.
  • AI in learning:
    • One of the biggest uses of AI in ed is to check for plagiarism. We can do more.
    • Coursera uses AI for online assessmenta (face recognition).
    • Check out: Wildfire (automated learning content creation), PhotoMath (Scan a maths problem for an
      instant result plus working out), Cogbooks (advanced adaptive learning platform).
    • Opportunity: AI can analyse and assess data without bias (unlike humans).
  • AI affects what we should teach, how we teach it, why we teach it. We need to rethink the education offering in the age of AI.

Tarek R. Besold, City, University of London, UK

Key message: AI is useful, but not everything is AI and AI is not good at all things. We need to think more carefully about what AI does well, what humans do well, and how we can work together.

  • Not all tech is AI, e.g. VR is not AI.
  • Intelligent tutoring only works well on well-defined, narrow domains for which we have lots of data.
  • Learning analytics is best used to track learner and teacher activities so as to identify individual needs and preferences to inform human intervention.
  • He pushes back against the popular call for all young people to learning coding. He says they don’t need to all learn programming, but rather logical thinking, procedural thinking, reasoning.
  • AI will not create equal access to education because of inequality in ICT infrastructure.
  • AI is good at taking over the “declarative knowledge” part of teaching, which can give human teachers/educators more time to focus on skills and the social aspects.
  • See the “human touch” as a value proposition beyond AI.
  • In automation, AI can take over mechanistic and repetitive tasks, giving human workers time to focus on decision-making, creative tasks.
  • Impact of AI on labour market: We need a societal decision: less workers or shorter working week for everyone (we can push back at tech companies)?
  • Must read for the AI-savvy decision maker: Artificial Intelligence and Life in 2030 (Stanford University report).

I asked: If AI should augment teaching and learning, with both humans and AI having strengths, how do we move AI into education (that could perceive it as a threat)?

  • Donald: Practical level: introduce spaced learning, adaptive learning, content creation to demonstrate the benefit.
  • Tarek: Political level: take the market approach out of education. Ensure humans will not lose jobs because of technology, shift societal perspectives on putting humans first.

Exhibitor: 360AI provides Artificial Intelligence building blocks delivered as APIs, aimed at accelerating the development of innovative teaching and learning products. 

LMS

  • Rethinking Learning Management Systems as Next Generation Digital Learning Environments (NGDLE) — see this article.
    Not an LMS but an LMX (Learning Method Experience).
  • It can be difficult to choose the right standards. Below is what Marieke de Wit, SURFnet B.V. shared:

LTI standard apparently rather poor on documentation right now.

Jeff Merriman, Associate Director of MIT’s Office of Educational Innovation and Technology and co-founder of the DXtera Institute:

  • MIT has a growing open-source Educational Infrastructure Service (EIS).
  • No UI, they are “headless”.
  • Integration challenge is huge.
  • How can chatbots interface with a LMS? Use existing software, example Slack, as the LMX. Chatbot integration is then back-ended by, for example, an assessment service.

 

Chatbots in teaching and learning

Donald Clark, Plan B Learning, UK

  • Learning bots:
    • Onboarding bots: chiefonboarding
    • Find stuff: Invisible LMS. Engagement not management
    • Learner engagement: differ.chat
    • Learner support: Deakin’s campus genie student services / IBM and “Jill Watson”
    • Teach courses: Duolingo bots for language learning
    • Practice dfficult learner’ bot for teachers
    • Well-being: Woebot
  • 7 interface benefits:
    • Natural, easy to use interface
    • Frictionless interface
    • Less cognitive overload
    • In line with current online interfaces
    • Suitable for younger audiences
    • Less formal but still structured
    • Presentation separate from AI drivers

 

Emerging Technology to Develop Learner Engagement and Increase Impact on Language Learning Outcomes

Geoff Stead, Cambridge Assessment English, UK

  • Cambridge English Beta
    • “Curious about how to shape our future products? Cambridge English Beta is the place to find out about our latest digital developments and get early access to trial versions of our English language learning products.”
  • Quiz Your English live challenge
    • Free game
    • 2.5m games played
    • 70k players
    • Top players play over 7k games per month
    • 70% of installed users drop off in first week
    • Features:
      • Social clues, people challenging you
      • Leaderboard
      • Next steps
  • The Digital Teacher
    • Resources to help you build your confidence and develop the skills you need to take your next step in digital language teaching.
  • Cambridge English MOOCs
    • 6 Moocs, run 14 times
    • 132,000 active students
    • Partner with FutureLearn (UNESCO is also a partner)
    • Successes:
      • Lots of use of video clips of real teachers, real learners and real lessons
      • Tasks to accompany each video
      • Community of learners

Chris Cavey, British Council

  • British Council MOOCs
    • 13 MOOCs run 50 times
    • Again, using video for teaching
    • Lots of tasks: what do think of? Tell us about your day?
    • Facebook Live sessions. Lots of discussion and interaction during MOOC. At end, send students to FB group. For BC their FB group has 200,000 users. Throughout, lots of social media interaction and sharing.
    • MOOCs do not assess language skills, they help prepare users for traditional assessments. It is a spring board and platform for peer sharing and learning.  MOOCs can measure time on task, engagement level, etc.

 

UNV e-Campus

https://learning.unv.org/

  • Moodle-based.
  • Mandatory training (ethics, volunteering, etc.) as well as supplementary courses, e.g. language learning, life skills, business development (courses are bought by UNV for the users).
  • Engagement: Communities-of-practice, remote coaching, online chat events, webinars.

 

Myths And Facts About the Future of Schooling

Pasi Sahlberg, director general of the Centre for International Mobility in Helsinki, Finland

He compared …

Unsuccessful education policies (Global Education Reform Movement) – England, USA, Australia, Chile:

  • Competition
  • Standardisation
  • De-professionalisation (anyone can become a teacher, as long as you love children)
  • Test-based accountability
  • Market-based privatization

Successful education policies (Global Education Reform Movement) – Japan, Canada, Estonia, Finland:

  • Co-operation
  • Encourage risk-taking and creativity
  • Professionalism
  • Trust-based responsibility
  • Equitable public education for all

Next predicted indicator group:

  • Health and well-being of children, not only equity and excellence

 

Longevity, Learning, Technology

Abigail Trafford, author and leader in the movement to fight ageism, USA:

  • Not years at the end, but healthy decades in the middle.
  • 50s and 60s: second adolescence. What do I want to do?
  • We need infrastructure to help older people for their next career, e.g. training, learnerships, internships.
  • Wide open opportunity for new curriculum development and part-time learning and work.
  • Over 1/3 of US is over 50.
  • Brain is plastic, it keeps learning.
  • Older people are better at analysis and strategy; younger better at quick learning and short term memory.
  • A long way to go to mainstream education and learning across the lifespan.
  • Old think: 9-5 work until retirement.
  • New think: 24/7 production and services.
  • We all need to expose and fight ageism.