Serendipity35

AI - “artificial intelligence” - was introduced at a science conference at Dartmouth University in 1956. Back then it was a theory, but in the past few decade it has become something beyond theoretical. been less theory and more in practice than decades before.

The role of AI in education is still more theory than practice.

A goal in AI is to get machines to learn. I hesitate to say "think" but that is certainly a goal too. I am reading The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution currently and in that history there is a lot of discussion of the people trying to get machines to do more than just compute (calculate) but to learn from its experiences without requiring a human to program those changes. The classic example is the chess playing computer that gets better every time it wins or loses. Is that "learning?"

But has it had an impact on how you teach or how your students learn?

It may have been a mistake in the early days of AI and computers that we viewed the machine as being like the human brain. It is - and it isn't.

But neuroscientists are now finding that they can also discover more about human learning as a result of machine learning. An article on opencolleges.edu.au points to several interesting insights from the machine and human learning research that may play a role in AI in education.

One thing that became clear is that physical environment is something humans learn easier than machines. After a child has started walking or opened a few doors or drawers or climbed a few stairs, she learns how to do it. Show her a different door, drawer, or a spiral staircase and it doesn't make much of a difference. A robot equipped with some AI will have a much steeper learning curve to learn these simple things. It also has a poor sense of its "body." Just watch any videos online of humanoid robots trying to do those things and you'll see how difficult it is for a machine.

Then again, it takes a lot longer for humans to learn how to drive a car on a highway safely. And even when it is learned, our attention, or lack thereof, is a huge problem. AI in vehicles is learning how to drive fairly rapidly, and its attention is superior to human attention. Currently, it is still a fall back human error in mist cases, but that will certainly change in a decade or two. I learned to parallel park a car many years ago and I am still lousy at doing it. A car can do it better than me.

Although computers can do tasks they are programmed to do without any learning curve, for AI to work they need to learn by doing - much like humans. The article points out that AI systems that traced letters with robotic arms had an easier time recognizing diverse styles of handwriting and letters than visual-only systems. 

AI means a machine gets better at a task the more it does it, and it can also apply that learning to similar but not identical situations. You can program a computer to play notes and play a series of notes as a song, but getting it to compose real music requires AI.

Humans also learn from shared experiences. A lot of the learning in a classroom comes from interactions between the teacher and students and student to student. This makes me feel pretty confident in the continued need for teachers in the learning process.

One day, I am sure that machines will communicate with each other and learn from each other. This may be part of the reason that some tech and learning luminaries like Elon Musk have fears about AI. 

I would prefer my smart or autonomous vehicle to "talk" to other vehicles on the roads nearby and share information on traffic, obstructions and vehicles nearby with those quirky human drivers only.

AI built into learning systems, such as an online course, could guide the learning path and even anticipate problems and offer corrections to avoid them. Is that an AI "teacher" or the often-promoted "guide on the side?"

This year on the TV show Humans, one of the human couples goes for marriage counseling with a "synth" (robot). She may be a forerunner of a synth teacher.

 

In "Credentials, Reputation, and the Blockchain" by J. Philipp Schmidt, the use of blockchain in one educational context is examined. I first wrote about this blockchain synergy of technoloy and education earlier this year. This EDUCAUSE article looks at using blockchain and strong cryptography to create certifications and digital degrees with more control. Recipients can share a digital degree with an employer while providing trustworthy proof that the degree was in fact issued to the person presenting it. This raises interesting questions about the nature of recognizing and accrediting achievements.

                        Read the article at educause.edu/articles/2017/4/credentials-reputation-and-the-blockchain  

The new Secretary of Education, Betsy de Vos, was viewed with trepidation by many educators. They see her as an advocate of charter schools and not a champion of K-12 public schools. In higher education, it was unclear what her focus would be because she had no experience in that area.
In her first speeches, community colleges may have felt some relief as she praised community colleges noting their importance to President Trump’s plan of expanding vocational and technical education. While community colleges do provide career and technical education, most also have a mission to provide the foundation for students to transfer to four-year colleges. The views of de Vos and the administration on that are still unclear.
Career and Technical Education (CTE) is designed to equip students with skills to prepare them for viable careers in high-growth industries. According to the association for Career and Technical education (ACTE), the top 10 hardest to fill jobs include skilled trade positions. Healthcare occupations make up 12 of the 20 fastest growing occupations. There are one million jobs open in trade, transportation and utilities sectors and more than 300,000 jobs in manufacturing.
Middle-skill jobs that require education and training beyond high school but less than a bachelor's degree make up a significant part of the economy and workforce. 
But not all of that training requires a college. Career training centers and for-profit groups have taken on many of these skill areas, and that is why college educators fear that de Vos, as with public schools, will be more in favor of that private and for-profit approach rather than colleges.
In her speeches, de Vos did not touch on issues involving transfer students, although many enroll at community colleges planning to eventually transfer to a four-year institution. The themes of her comments match the priorities talked about by the administration and Republican lawmakers (like North Carolina Representative Virginia Foxx, the chairwoman of the House education and the work force committee) which focus on facilitating vocational education, expanding the number of certificates awarded to students, and putting a greater emphasis on alternatives to the traditional model of a four-year college education.
De Vos noted that President Trump's 100-day action plan includes a call to expand vocational and technical education, and that he has called multiple paths for postsecondary education "an absolute priority" for his Administration.
Those multiple paths are unclear right now, and that uncertainty concerns many educators.

What is the difference between artificial intelligence (AI) and machine learning (ML)? AI is an umbrella term to describe a branch of computer science that deals with the simulation of intelligent (human) behavior. Machine learning is a subset and currently the most common type of AI. We encounter it, consciously or unconsciously, in our every the average person will encounter. 

Amber MacArthur posted in one of her recent newsletters some examples of AI & ML. Any phone "assistant" (such as Apple's iPhone assistant Siri, works because it relies on huge amounts of data, but its development is based on machine-learning technology. These machines "learn" over time based on our interactions with them. This happens without being programmed to say or do new things. 

It takes more than data for people and machines to learn. It requires being able to recognize patterns in that data and learning from them, being able to draw inferences or make predictions without being explicitly programmed to do so. It needs to do critical thinking.

Another AI example noted by MacArthur is SnapTravel. It is a chatbot that uses machine learning to run its "half-bot, half-human" service with its users. It uses SMS or Facebook via Messenger to work with a "bot" agent to book your hotel reservation.

During the 1960s and 70s, the technology alarm was that computers will be taking our jobs. It turned out that some jobs disappeared, but many more were created. The new technology alarm is that AI will take away jobs. And that will happen if people are "disinclined to technical skills" because they may not be able to earn a good living in a market economy. One prediction is that "as AI improves and gets cheaper, many of the jobs left for humans will be those so badly paid they are not worth replacing with a machine." Ouch.