Humble Beginnings of AI Literacy in Adult Learning

Hello LX Community Members!
Have you heard enough about AI and GAI yet? Another area of AI hardly ever looked at is the little research in the area of AI literacy. As it grows in use, in time, the research will follow. In the meantime, take a quick review of the knowns and unknowns in the link below. Comments are always welcomed here in this awesome LX Community.

  • Lists recurring thematic foci and issues in AI literacy research.

  • Presents the pedagogical structure of courses intended to foster AI literacy.

  • Calls for more research on AI literacy training and validated assessment scales.

Artificial intelligence literacy in higher and adult education: A scoping literature review

[LINK](Artificial intelligence literacy in higher and adult education: A scoping literature review - ScienceDirect

Follow me on Twitter @roxannriskin

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Sincerely, even tough I heard about AI or read a lot about it, I could been AI illiterate. This is the case I think when I can’t manipulate the computer in a way of using effectively services that could be offered by AI software or application. As example I can waste a lot of time looking for some thing in a webpage that contains a lot of similar products, but using a sophisticated AI service I can economize time and even efforts in finding what I want, to accomplish this task I need to practice specific computing skills or get a minimal knowledge about how to use AI new technologies consistently and efficiently with my objectives.

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Hi @Hamid ,
I feel the same way. I don’t believe most of us have any AI literacy at all. My plan was to try the most familiar AI that was being talked about by academics and then of course the media frenzy began.
The two that I have been using for the past few months have been ChatGPT and Dalle2. I have some basic familiarity of using prompts but was more difficult was writing good prompts. So just by trying and testing out prompts and learning more about prompt engineering was when I felt more confident getting closer to the results that I was looking for from the AI. I am looking forward to posting more in this area especially where it can benefit learning designers in any areas that I find beneficial. What specific areas are you interested in using AI?

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Thank you for your reply @Roxann, I’m interested in using AI to generate analytics about financial risks, especially scoring related with Environmental, Social and Governance (ESG rating). I also intend to use it in filtering data more efficiently, because bibliographic research engines are sometimes not effective in filtering by keywords.

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HI @Hamid

Here is a sample of what ChatGPT returned on a prompt, with some aspects, of how AI can assist with what you mentioned.

Sure, here are two detailed ways on how to use AI to generate financial risks, especially scoring related with Environmental, Social and Governance (ESG rating):

Natural Language Processing (NLP) for ESG Scoring:
One way to generate financial risks related to ESG scoring is by using natural language processing (NLP) techniques. NLP is a subfield of AI that focuses on enabling computers to understand and interpret human language. In this case, NLP can be used to analyze large amounts of text data from various sources such as news articles, social media, and corporate reports to extract ESG-related information. The extracted information can then be used to create ESG scores for companies based on their performance in environmental, social, and governance factors.
To use NLP for ESG scoring, you would first need to gather and preprocess the text data. This involves cleaning and structuring the data in a way that is suitable for analysis. Next, you would use NLP techniques such as sentiment analysis, entity recognition, and topic modeling to extract relevant ESG-related information. Finally, you would use the extracted information to create ESG scores for companies.

Machine Learning for ESG Risk Assessment:
Another way to generate financial risks related to ESG is by using machine learning (ML) techniques for ESG risk assessment. ML is a subfield of AI that involves training algorithms to learn from data and make predictions or decisions based on that data. In this case, ML can be used to analyze data related to a company’s ESG performance and assess the risk associated with that performance.
To use ML for ESG risk assessment, you would first need to gather and preprocess the data. This may include data related to a company’s environmental impact, labor practices, corporate governance, and more. Next, you would use ML techniques such as regression analysis, decision trees, and neural networks to identify patterns and relationships in the data. Finally, you would use the ML model to make predictions about the risk associated with a company’s ESG performance.

Overall, using AI for ESG scoring and risk assessment can help investors make more informed decisions about their investments and encourage companies to improve their ESG performance. However, it’s important to keep in mind that AI models are only as good as the data they are trained on, so it’s important to ensure that the data used is accurate, unbiased, and representative of the companies being assessed.

Regenerate response

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Thank you very much @Roxann.
Sincerely AI will grow up my work efficiency, I experienced Chat GPT 4 in generating content for a conference meeting, before few hours of the deadline, It took less than 2 hours.

I would thank you so much for your kind sharing about the use of NLP.

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