Forum Discussion
The Learning Engineer is DEAD? Long LIVE! the Learning Engineer?
While I'm panicking over what to actually take in the site formally known as Lynda.com - that'll help with the AI'ifcation, specifically in learning analytics.
I guess the job title "Learning Engineer" has gone flop, but the idea that you can still do "data-driven learning outcomes", and I'm hoping Megan's new book "Data and Analytics for Instructional Designers" to figure out if I need to learn Python to participate in meaningful analytics. And then SHE says to also read "Measurement Demystified" (available in O'Reilly, 1/2 way through), and then "Learning Analytics" (not in O'Reilly).
Unless the BIG LXDs are already doing it? (my previous employer got Docebo, but then NOT "Learning Analytics" just "Learning Impact" ggrr). So maybe we don't have to learn generative or deep or machine learning (like learning how to do Python to ply information from training data)..? And I should figure out how to be a "Prompt Engineer" instead?
As anyone seen ANYTHING about AI in T&D or L&D or LxD yet? Have the Support Groups started yet? Maybe they're in Linked Groups...
Alison
It seems like you have a lot of questions and concerns about the role of AI in the field of learning and development (L&D). While it's true that the specific job title "Learning Engineer" may not be widely used, the concept of leveraging data and analytics in learning outcomes is still very much relevant.
Learning analytics, which involves the collection and analysis of learner data to improve learning experiences, is an area that holds great potential for AI integration. By harnessing the power of AI, learning platforms can provide personalized recommendations, adaptive learning paths, and real-time feedback to learners. These advancements can enhance the learning process and help learners achieve their goals more effectively.
Regarding the need to learn Python for meaningful analytics, it depends on the specific requirements of your role and the tools you'll be using. Python is a popular programming language for data analysis and machine learning, and having knowledge of it can certainly be beneficial. However, there are also user-friendly analytics tools available that may not require coding skills. It's important to assess your specific needs and determine if Python or other analytics tools are necessary for your desired level of analysis.
As for resources on AI in T&D or L&D, Megan's book "Data and Analytics for Instructional Designers" sounds like a good starting point. Additionally, exploring publications and online communities dedicated to AI in education, such as research journals or LinkedIn groups, can provide valuable insights and discussions on the topic.
While the field of learning experience design (LXD) is constantly evolving, it's likely that AI will play an increasingly important role in shaping the future of L&D. As AI technology continues to advance, it opens up new possibilities for personalized learning, intelligent tutoring systems, and data-driven decision-making in instructional design.
It's always a good idea to stay informed about the latest developments and trends in AI and how they relate to the field of learning and development. By keeping up with industry publications, attending conferences or webinars, and engaging with professional networks, you can stay connected to the broader community and gain insights into how AI is being implemented in T&D and L&D contexts.
Remember, the field of AI is rapidly evolving, and it's important to approach it with a growth mindset and a willingness to continuously learn and adapt to new technologies and methodologies.
AI brings a lot of benefits. It allows people to bond with each other more. You will find more interesting information. In addition, AI helps advance a number of other engineering disciplines.
It's likely that you could use AI to write whatever python code you need.
I couldn't tell from your post if you had specific things you were hoping to learn from your datasets. I would think that is a more important focus area at the beginning. Once you know what you're trying to learn from the data, then you can search for the best way to mine the data to get those answers.
You'll want to be careful about how you leverage AI. Something like Chat-GPT with Code Interpreter will be a lot more reliable than Chat-GPT without Code Interpreter when it comes to summarizing findings from the data. There is still a serious problem with "hallucination" (the term for when the AI system just makes stuff up and confidently presents it as fact) that you will need to be alert to. Chatbots also have some basic weaknesses (for example, counting) that could also have a big impact on the reliability of their analyses.
