Club Admiralty

v7.2 - moving along, a point increase at a time

Multilitteratus Incognitus

Pondering what to learn next 🤔

ETMOOC Session 1 Ponderings

An image generated with AI generator "nightcafe"
Me in a Star Trek-themed anime AI image

Just as session 2 of #etmooc2 is scheduled for this evening, I just caught up with the first session over the last few days. The recording can be found here, and it's funny that it took me 3 days to complete.  Part of it was because I could only really do 20-minute increments (with notes and reactions), and part of it was because I paused to experiment with things mentioned.

Part of the session was really dedicated to identifying ways in which this kind of technology can help with what we do.  Essentially flipping the script and going from "ZOMG! ChatGPT is used for cheating" getting to "how to use ChatGPT to help us with learning?" 

There were a number of examples used in this brainstorming session which present for red flags for me.  I did think of a few examples of my own that may (or may not) be good examples of what you could use tech like this for.  I'll start with my concerns though.

Example 1: Using ChatGPT to grade. This is a use case of having a kind of machine-human collaboration. It was acknowledged that the machine can't really accurately grade everything, and the instructor should have a look-over to correct or supplement the machine output, but this would be a potentially revolutionary use of this tech.  I'm not convinced. First, I have issues with both feeding student submissions into such a system without appropriate guardrails for student submissions. We've seen, from past actors in this space, that they take student submissions and appropriate them for their own use.  Students should not have to consent to having a "smart" machine grade their submission as part of their learning experience.  My second issue here is that such machine grading takes away Teacher Agency to some extent, and it may be taken away as a means to being more "efficient" or "less burned out." Teachers and course facilitators are in a classroom for a reason. If grading submissions is becoming an issue it's important to interrogate why they are becoming an issue instead of throwing some LLM at it.

Example 2: Continuing on with that thread of human-machine collaboration, when working with ChatGPT it's like you're working in a group, but instead of other humans, your team-mate is an AI.  Maybe if AIs were like Mr. Data on Star Trek, I might have a different opinion.  Right now LLMs are like dumb appliances.  They can "learn" but they are essentially machines. Collaboration requires agency, scope definition, goals, and drive, which machines simply do not have.  In Connectivism, you can have interactions between human and non-human/appliance nodes, but I would not go so far as to say that they are collaborating. It's not even a one-sided "collaboration" for the human in that equation.  When you're collaborating in a team you don't have to fact-check your team-mates submissions.  You can have sufficient overlap between areas of expertise so as to have more than one pair of eyes on claims made, and people who are more expert at something can ELI5 things to other team members, but ultimately there is a back and forth.  In a human-machine "collaboration" you have the issue whereby the human needs to be an expert in the subjects to be able to know where the machine goes wrong and correct it.  In a learning context, I think that this is potentially detrimental to the learning process.  It's not the knowledge navigator future we've dreamed of - at least not yet.

The question that came to my mind is this:  why are some folks thinking of LLMs as a "collaborator" and not looking at Google search as a collaborator?

Example 3: OK, final critique here.  One of the things I've heard over the last few weeks is something along the lines of: if you are a good prompt engineer you can get some amazing information, which you have to fact-check."   There are just too many conditionals here to be that kind of study buddy that was mentioned above in example 2. Now, this reminds me of my undergraduate days when I learned about library databases and how to search for resources using Boolean logic. Yes, you needed to play around with your logic and your search terms (and sometimes you needed to learn controlled vocabulary), but you got actual sources that you could read and evaluate (and cite).  I think prompt engineering is less of a sign of things that learners need to learn and more of a sign of a system that is still half-baked ;-). That said, I come back to the fact that you need to know how things work in order to assess whether the output is of any use (or even factual).  An example that comes up is people learning another language.  You would write something in English (assuming English is not your native language) and pop it into an LLM to have it convert that output to something more "native sounding." Amongst other issues, it's useful to know why something sounds more correct than other options when you're learning a language. An LLM could do it for you, but that doesn't help you progress as a learner. We had an example of why it's important to know your stuff (even if machines help) in Star Trek Picard this season. The short version is that the ship's captain is brought to sick bay with some symptoms. The veteran doctor realizes that he has internal bleeding that the younger doctor's medical imaging devices failed to catch. If the veteran doctor didn't know her stuff, the captain would be dead 🤷🏻‍♂️

Anyway, this post is getting too long, so I'll save my ideas for using ChatGPT/AI for another post ;-)


Thoughts? 


~~~~~~

Just for documentation purposes, here are the objectives of the first session:

By participating in the synchronous Zoom session and any additional activities you pursue as part of your own learning experience, you will see how your colleagues are responding to ChatGPT. By the end of the live session and completion of any other activities you pursue, you will be able to:

  • Identify at least three ways ChatGPT might be of benefit to you and those you serve in your section of our lifelong learning environment
  • Anticipate at least three challenges ChatGPT may pose to you and those you serve
  • Describe at least one way you may begin incorporating ChatGPT into your work or describe at least one step you can take to overcome a challenge you face in incorporating ChatGPT into your lifelong learning efforts


 Comments
Stacks Image 20

Archive

 Nov 2023 (1)
 Aug 2023 (1)
 Jul 2023 (1)
 May 2023 (1)
 Apr 2023 (4)
 Mar 2023 (5)
 Feb 2023 (2)
 Dec 2022 (6)
 Nov 2022 (1)
 Sep 2022 (1)
 Aug 2022 (2)
 Jul 2022 (3)
 Jun 2022 (1)
 May 2022 (1)
 Apr 2022 (2)
 Feb 2022 (2)
 Nov 2021 (2)
 Sep 2021 (1)
 Aug 2021 (1)
 Jul 2021 (2)
 Jun 2021 (1)
 May 2021 (1)
 Oct 2020 (1)
 Sep 2020 (1)
 Aug 2020 (1)
 May 2020 (2)
 Apr 2020 (2)
 Feb 2020 (1)
 Dec 2019 (3)
 Oct 2019 (2)
 Aug 2019 (1)
 Jul 2019 (1)
 May 2019 (1)
 Apr 2019 (1)
 Mar 2019 (1)
 Dec 2018 (5)
 Nov 2018 (1)
 Oct 2018 (2)
 Sep 2018 (2)
 Jun 2018 (1)
 Apr 2018 (1)
 Mar 2018 (2)
 Feb 2018 (2)
 Jan 2018 (1)
 Dec 2017 (1)
 Nov 2017 (2)
 Oct 2017 (1)
 Sep 2017 (2)
 Aug 2017 (2)
 Jul 2017 (2)
 Jun 2017 (4)
 May 2017 (7)
 Apr 2017 (3)
 Feb 2017 (4)
 Jan 2017 (5)
 Dec 2016 (5)
 Nov 2016 (9)
 Oct 2016 (1)
 Sep 2016 (6)
 Aug 2016 (4)
 Jul 2016 (7)
 Jun 2016 (8)
 May 2016 (9)
 Apr 2016 (10)
 Mar 2016 (12)
 Feb 2016 (13)
 Jan 2016 (7)
 Dec 2015 (11)
 Nov 2015 (10)
 Oct 2015 (7)
 Sep 2015 (5)
 Aug 2015 (8)
 Jul 2015 (9)
 Jun 2015 (7)
 May 2015 (7)
 Apr 2015 (15)
 Mar 2015 (2)
 Feb 2015 (10)
 Jan 2015 (4)
 Dec 2014 (7)
 Nov 2014 (5)
 Oct 2014 (13)
 Sep 2014 (10)
 Aug 2014 (8)
 Jul 2014 (8)
 Jun 2014 (5)
 May 2014 (5)
 Apr 2014 (3)
 Mar 2014 (4)
 Feb 2014 (8)
 Jan 2014 (10)
 Dec 2013 (10)
 Nov 2013 (4)
 Oct 2013 (8)
 Sep 2013 (6)
 Aug 2013 (10)
 Jul 2013 (6)
 Jun 2013 (4)
 May 2013 (3)
 Apr 2013 (2)
 Mar 2013 (8)
 Feb 2013 (4)
 Jan 2013 (10)
 Dec 2012 (11)
 Nov 2012 (3)
 Oct 2012 (8)
 Sep 2012 (17)
 Aug 2012 (15)
 Jul 2012 (16)
 Jun 2012 (19)
 May 2012 (12)
 Apr 2012 (12)
 Mar 2012 (12)
 Feb 2012 (12)
 Jan 2012 (13)
 Dec 2011 (14)
 Nov 2011 (19)
 Oct 2011 (21)
 Sep 2011 (31)
 Aug 2011 (12)
 Jul 2011 (8)
 Jun 2011 (7)
 May 2011 (3)
 Apr 2011 (2)
 Mar 2011 (8)
 Feb 2011 (5)
 Jan 2011 (6)
 Dec 2010 (6)
 Nov 2010 (3)
 Oct 2010 (2)
 Sep 2010 (2)
 Aug 2010 (4)
 Jul 2010 (9)
 Jun 2010 (8)
 May 2010 (5)
 Apr 2010 (4)
 Mar 2010 (2)
 Feb 2010 (3)
 Jan 2010 (7)
 Dec 2009 (9)
 Nov 2009 (5)
 Oct 2009 (9)
 Sep 2009 (13)
 Aug 2009 (13)
 Jul 2009 (13)
 Jun 2009 (13)
 May 2009 (15)
 Apr 2009 (15)
 Mar 2009 (14)
 Feb 2009 (13)
 Jan 2009 (10)
 Dec 2008 (12)
 Nov 2008 (6)
 Oct 2008 (8)
 Sep 2008 (2)
 Jun 2008 (1)
 May 2008 (6)
 Apr 2008 (1)
Stacks Image 18