Monday, 15 January 2018

In search for #AI for critical thinking in #education #criticalthinking #language

Who knows of Artificial Intelligence (AI) initiatives being developed to support critical thinking in education, or based on data text analysis and cognitive language use? Please drop me a line (or message). To give you an idea of what proceeded this question, I am providing some AI background, including my thoughts. A good read is the paper by Yeomans, Stewart, Mavon, Kindel, Tingley and Reich investigating "the civic mission of MOOCs: engagement across political differencess in online forums", which adds to the idea of using AI as a way to stimulate debate across opposing viewpoints, thus enhancing critical thinking (for those willing). 

AI to help human thinking processes
AI is rapidly expanding its reach: you have initiatives of meaningful curated content generated by AI into elearning (e.g. Wildfire ), you have legal research analysed and organised by AI (e.g. ), you have multiple AI molding social media interactions based on factors such as friends, exchanging ideas, similar content (sometimes opinions) shared… basically, industry is looking at AI as a means to refocus on less-repetitive parts of their business or profit goals ( ).

But, I am wondering whether there is research projects taking into account AI using text analysis but including cognitive language use to enhance critical thinking (for instance: if you have echo chambers, why not use AI to pick up frequently used arguments from ‘the other side’ to generate more in-depth arguments for either side. Or for those looking to become dominating world leaders (devils advocate here): creating something which goes beyond fake news: using arguments that feel right but actually are built using persuasive language construction to trigger a feeling of ‘that is right’ and parallels what a person thinks is morally correct (I said it was a devils advocate example :D )

AI in education
With all the talk on the new citizens needing to be ‘creative’ mindset above anything else, the creativity does not seem to emerge yet in AI, the focus is still more on rehashing what is already there, but with more focus on the norm by using AI in education (I could be wrong, feel free to provide arguments on why creativity is indeed boosted by AI in education).
A couple of examples where AI is used to boost learning, but along the lines of existing norms, nevertheless of interest.
Deep Knowledge Training. One of the interesting strands of AI in education research is Deep Knowledge Training (a good read is the 2015 paper by Piech, Bassen, Huang, Ganguli, Sahami, Guibas and Sohl-Dickstein ) this allows a machine to model the knowledge of a student as they interact with coursework. It can be used to extrapolate student performance for instance. This seems to be good, but you know that this is based on ‘what we expect of students’, which is not necessarily what could be good for humanity or social thinking.
Assessing future scores. Another example is the algorithm built by Google and Stanford which relates to a students learning ability (well more specifically how a student would answer questions) . Here as well, the learning seems to parallel taking exams… which does not seem to promote creative thinking.
IBM Watson for education ( ). Starts from the idea of personalised learning (and passion, so I really love that starting point), but when I looked at the videos, the definition of personalised learning seemed to be limited to personal interests (in educator video), which limits the concept of personalised learning. And though it is good to provide skill-level content, if the content base you pull it from is standard…. The standards will again be the norm, which does not necessarily result in creative ideas or insights.

AI based on language data
One example I found using AI in relation to natural language processing is NexLP ( ) (quoting from their page: “leveraging the latest advances in Natural Language Processing (NLP), Cognitive Analytics, and Machine LearningStory Engine turns disparate, unstructured data - including email communications, business chat messages, contracts and legal documents - into meaningful insight that can be used to act, as well as combined with structured data to create a truly comprehensive view of the entire data universe.) and the people behind NexLP state that they use cognitive analysis to add more context to the actual text analysis”.
But when looking at it, it seems more of an enhanced interactive dashboard at first glance. This means it feels more like a quantifiable AI implementation than a qualitive one. One of the solutions to filter meaningful content is wikification (where you link entities ) which seems to be an effective way to add context to text analytics technology ( )

Past fake news or beyond critical thinking
The term fake news is now a given in many politician’s speech, both in its originally intended definition, as well as in popular debate where it functions as a way to ridicule and diminish the truth or value of an argument by an opposing person. But maybe we can turn this around. Create algorithms that can be used to enhance our debating skills, our critical thinking by generating arguments that are most frequently used by groups gently opposing our views. I mention gently opposing, as persuasive arguments are rarely harsh, completely opposing arguments.
I see this as a possible way to tear down the echo chambers created by filter bubbles, and build bridges. Or at least get a conversation started.  

Feel free to share your thoughts or link to examples.

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