Well... my first thought, knowing how ChatGPT works, is that this is a complete bunch of bullshit, and I immediately jettisoned this idiocy from my brain.  Over the last few days, I've been thinking that I really should try it to see if it does actually work. After all, I wouldn't be doing my due diligence if I didn't check this use case out.  If it does, it could be useful for accessibility purposes.

However, I didn't want to snap a photo of my own bookshelf, and I've been looking for a sample photo to upload to ChatGPT to test this out.  Last night, I was browsing Reddit and I saw a post from the hotsauce subreddit, and I found my use case.  So, I downloaded the picture, submitted it to ChatGPT (yeah...sorry about that), and asked it some questions.   I've added the photo (which may be downscaled by Blogger at the end of this post). 

Then, I got to asking questions.  First, a curveball, ask for something that doesn't exist.

Prompt: Help me find the tabasco sauce. Where is it?

Response: I looked closely at the shelves, and I can see the Tabasco sauce bottles on the top right corner of the display.

They’re the familiar small bottles with the diamond-shaped Tabasco logo and red caps, placed in front of the green cylindrical packaging tubes.

Verdict: ❌

Total failure, but OK.  Let's ask it for something that DOES exist...

Prompt: Where is Melinda's hot sauce?

Response: I see the Melinda’s Hot Sauce bottles on the right side of the shelves, about the third row up from the bottom.

They’re grouped together in a row, with their distinctive white caps and colorful labels featuring the portrait of a woman (the Melinda’s logo).

Verdict: ❌

Prompt: Where's the fat cat hot sauce?

Response: I found it — the Fat Cat Hot Sauce bottles are on the left side of the shelves, about the fifth row up from the bottom.

They have white caps and labels with a simple cat face logo, making them stand out from the more fiery and graphic hot sauce labels around them.

Verdict: ❌

Prompt: Where is the spicy shark hot sauce?

Response: I spotted the Spicy Shark hot sauce bottles on the left side of the display, about the fourth row up from the bottom.

They’re the bottles with white caps and colorful labels featuring a shark illustration, lined up together in a row.

Verdict: ❌

Prompt: Where is the orange krush hot sauce?

Response: The Orange Krush hot sauce is on the left side of the display, second row from the top.

You can spot it by its bright orange label and cap, standing out among the darker bottles around it.

Verdict: ❌

While this is not expensive testing,  the fact that it could not find ANYTHING, and it hallucinated my fake hot sauce, it's clear to me that this is not a use case for ChatGPT and LLM-based "AI."  I really think that folks should not just hit "share" on anything related to LLMs until they independently test things (at least if you're into tech and have the means to experiment; I don't expect this from my dad or other lay folk)

In any case, you didn't come here to read about my (lack of?) exercise and my busy schedule, did you? I thought I'd jot down some thoughts since it is July and summer is one-third gone (sigh). One of the things that I am not making as much progress on as I would have liked is course design. In all honesty, I thought that by now I would have chosen all my readings for the fall, started to put them into Canvas, and have August free to just chill for the rest of the summer.  That's not happening...  While I have evaluated one book (and a whole bunch of articles), my balance is a bit off, despite having around 40 readings so far for my class.  The main issue I keep coming up against is that there is so much AI-Boosterism out there that the more balanced CALL stuff I want to include might seem irrelevant to learners. The "we gotta teach AI because learners will need it in the future, it's inevitable" is a discourse that you can't avoid.  So yeah... Trying to navigate the fresh, actually stale, hype of it all.

On the plus side, there are a few things that have happened! I created my Academic Article resources list, and my Podcasts Resources list, things that students will need for one-third of the materials each week (plus one of their major assignments), so things ain't all that bad.  I've also started prepping the options for the major term project, so that's going well. I just need to develop some directions about what to do with each 

The other notch on the "plus side" of things is that I've gotten my CALL groove back. Back in 2012(ish) when I was examining my options for a doctoral program, something in CALL was an area that I was seriously considering.  As part of the work in that area, I spent a lot of time reading CALICO and ReCALL articles, and through my evaluation of resources so far, it's really great to see (or in many instances hear) a lot of familiar names of researchers that I recognize from back then. I think it's really lit a fire that I've been kindling for a while.  The other cool thing is that I've seen so much overlap between applied linguistics/CALL with instructional design in what I've read so far.  I always knew this was the case, but it's really great to evaluate book chapters that make those connections much more concrete.

On a slightly different note, as I've been pondering this course's final project over the past month (and reading materials for inclusion in the course), one of the things that stood out to me was that a survey course on EdTech (with a focus on language teaching and CALL) is that it's not just geared toward teachers. Sure, while teachers may be the majority of folks enrolled (given the type of program I work for), there are other personae who might find the course valuable. The Technology/Teaching Coach, the Educational Technologist, the CALL Imagineer, and the EdTech Researcher all come to mind as potential students in the course.

ChatGPT Patch of the Wise Owl

Recently, I've been playing around with image generation in ChatGPT, not so much to create output that I plan on using seriously for something (although some output do end up on this blog as post images), but more to see how easy (or hard) it is to get something from my mind's eye into some kind of machine output.  I am also curious to see how the LLM interprets what I input (that element of surprise).

I only really have the free credits that OpenAI gives to its free users, to my experimentation is basically 10-15 minutes of futzing around while watching TV in the evening. As I was playing around the other day, this scene from Star Trek: The Next Generation came to mind. In Schisms,   the crew had been abducted by an alien race but had no memory of it (think Alien Encounters of the Third Kind). As they start to remember small elements of their experience, they all try to piece together their memories so that they can come up with a reconstruction of the probing table they were on.

I've included an excerpt from the show in this post.  Back in 1992, this was a big of a "whooooaaah!!!" moment. Look at what the holodeck and the Enterprise computer can do!!!  Playing with ChatGPT to make something like this happen... now so much! [although, maybe I should take the directives that the Enterprise crew gives to the holodeck and see what ChatGPT comes up with haha😅].

My experiments focused on creating something small and concrete, like embdoidered patches. I was interested in seeing what the technology does with mashups, like "a morale patch for a D&D players group that cosplays as Thundercats and loves Captain Planet." Some of the results were interesting (I'll admit to that), other results were humorous, so I got a good laugh. When something interesting came up and I wanted to make slight modifications, that's when things just totally broke.  Text generation for images is still problematic, even when you instruct the machine to give you something specific like "To know thyself is the beginning of wisdom" ends up being correct on the first attempt (sometimes), but then as you ask the machine to update the graphic and keep everything else the same, somehow the quote becomes "To know thyself is the beginnnnn dom."  Further prompts to fix this error by correcting it with "To know thyself is the beginning of wisdom" make the text become "T∞ knąw thgselϝ is the begin Ϸominutius."  The same is true for adding visual elements to your creation.  If you ask the machine to add a small element on a specific part of the image, the entire image is getting reworked, and elements that you liked are now changed and modified🙄.

So, unlike Schisms, where the crew of the Enterprise got their examination table (in a short time I might add), playing with image generation and modification on ChatGPT feels like a game of Marco Polo, or Hot/Cold.  You think you're getting hotter with every successive iteration and prompt, but then something happens and you're cold again...much much colder... 

Fun parlor trick (that consumes A LOT of energy), but useful?  Nah...

A bit of a kerfulle happened a few weeks ago, and it's just indicative of how the rest of life is  going what I've had this post in draft form for almost a month while I've plugged away at it...

Annnyyywhoooo🙄

The kerfuffle was kicked off by Wiley's Open Educational Language Models initial post describes OELM as bring together a collection of openly licensed components that allow an openly licensed language model to be used easily and effectively in support of teaching and learning.  In his follow up post, Wiley is open pondering/brainstorming about OELMs, Wiley discusses a separation of form from content, similar to how text on the web is separated from the formatting CSS layer.

Wiley's original posts are intersting and do provide some points to ponder. I don't necessarily agree in whole with what  he proposes, but I can see a grain of something interesting there, and certainly worth pondering and discussing. Maybe I've gotten a bit more "get off my lawn" these days having seen at least five major hype-cycles since 2010🧑‍🏫, and I think GenAI is not sustainable, it seems to be reaching a plateau, and it just doesn't really produce useful things. At least MOOCs opened up education a bit. But I digress. Moving on...

Where it gets interesting is with a follow up by Siemens, in his somewhat-weekly roundout of AI news, linked to Wiley's post and left the following zinger "I’m thinking course design should cost about zero." Here, Downes follows up with "My thought too" in his own sharing and brief analysis of things in the EduSphere. This brings us Crosslin's rebuttle on the point that "course design should cost about zero."  I don't want to summarize Wiley's and Crosslin's posts, I think it's worthwhile reading them (and subscribing via RSS)

Siemens used to blog (before he nuked the site and barely does anything these days) so you could get a bit more than a soundbite. The whole "I’m thinking course design should cost about zero" feels very much like a Musk-esque provocation than anything else. I don't know if Siemens meant to drop a stinkbomb and run, or what, but it's a shit take. It doesn't  matter if you are an instructional designer, learning engineer (🤮), course developer, or faculty member creating courses, the course creation process is part curation, part creation, and part experimenation. Based on the defined outcomes of a  course, and the expected student demographic, you re-use what you can, you create what you don't  have available, and you experiment with new ways of engaging, assessing, and communicating with the learners in the classroom. That creative process isn't something that a machine can do (at least not yet?) because everything that is extruded from an LLM comes from something that already exists. Replicating teaching practices that we've experienced mindlessly is one of the reasons many critique things like mindlessly replicated Chalk & Talk approaches; so why would we accept the equivalent from any sort of automated technology? Sure, you can create things faster, but is faster always better? You can massify things, but is massification always better?

In the end, no. Course creation should not cost zero. If you want someone to take care and effort in creating something useful, pay for it. Digitally Extruded materials seems to be a new reality (e.g., the image I asked Dall-E to make for me for this post, which is dumb, but you're not reading this post because of the image, are you?)

As an aside, along with "Better Practices," which I already use, I think I will adopt "Current Best Thinking" into my vocabulary.

Your thoughts?

PS: maybe in a subsequent post I can talk about how the HTML/CSS analogy doesn't work for learning content, but maybe at this pace you'll read about it in July 😂

 Alright...alright... bad pun 😅

But to be fair, CALL (computer-assisted language learning) does lend itself to some bad puns... And that's before I even get into other acronyms like TELL and MALL😹. I promise to spare students the dad jokes in the fall😅

Anyway, last week I decided to answer my pondering as to what my course redesign should cover, and I settled on designing an Introduction to CALL course. I decided that the course number (685) doesn't matter all that much considering that our course numbers over the past 30 years have drifted to an extent that they make some sense, but they aren't (as a whole) totally coherent, and that's not my problem to solve. The next part of my mission is to determine learning objectives (which I more or less have) and determine what kinds of assessments and activities I want to include. The universe of possibilities feels rather endless, so I need to find the right size for semester activities and assessments.

One of the things that I've been pondering lately has been the right amount of work for a graduate class. Over the past few years, I've taken or audited a few graduate (MA) classes, and I've noticed that for most classes the "standard" still appears to be some sort of summative paper at the end of the term that's around 3000 words plus a project (or a few smaller mini activities), or around 5000-6000 words as a single assessment. I'd like to keep the single paper as an option if students really want to do that, but I was thinking of other more creative outlets for activities and projects. I am thinking of incorporating technology showcases and critical analyses of edtech in the language classroom as part of this, but I wonder what the right cadence is to do some in-depth work. Once new tech per week?  One new tech every couple of weeks? individual work?  Group project? Do weekly discussions still have a spot in classes or are we just continuing down this path because we've been doing it for 20+ years? 🤔.

One of the things that I used to do when I taught the Design of Online Instruction course for the instructional design program was to have student groups design and facilitate one weekly module. This basically meant that the last 5 weeks of the term (so 1/3 of the term) were student-run. The topics came from a pool of topics, so it became a jigsaw with a mix of research, evaluation, design, and facilitation elements.  I think this could be fun.  If you were running this, what topics would make it to your pool of topics for students to pick from?

I am taking this spring term off from teaching, partly to re-energize my batteries which have been rather low on account that I've been going full speed (🚌) since 2018; the pandemic didn't help because teaching increased around that time (not that I am complaining, the cosmos provided something I needed at the time).  Another reason for the break is partly to work on a new course development for the Fall term. While course development shouldn't take 8 months to complete, with so many other irons in the fire, it's a part-time endeavor.

For the first time, in a very long time, I  get to mix edtech with applied linguistics!🥳 The last time I did this was a long (long) time ago. It is rather exciting, but also daunting because, over the past 10 years, I haven't kept up with the CALL (computer-assisted language learning) world.  Before I started my dissertation planning, my thought was to do something CALL-related, so I spent a few years doing a deep dive into CALL (with a focus on MALL) articles in ReCALL, CALICO, and to some extent System (journals devoted to edtech in language education and applied linguistics). When I decided to focus on MOOCs and collaboration as my dissertation research, I stopped following that thread.  Well, now I am back and I am not quite sure where to pick up the thread again😅.

The course that I am tasked with resurrecting and updating is titled "The Internet in the Language Classroom," a title it got back in 2008 (probably as part of a grant my department had in those days, this is before my time).  Prior to that, it was titled "Technology in Education." Interestingly enough, there is also another defunct, separate course titled "Technology in Education. What's clear to me from the history of these two courses is that they were originally both Intro to CALL courses, when ICTs brought connection to the classroom one course morphed into an "internet course" and the other course morphed into a Web 2.0 course (ugh... someone was asleep at the curriculum committee wheel since these were essentially the same course...). In any case, this brings me to an interesting dilemma:   Since I have carte blanche to make this course anything I want (within the broad realm of CALL) what should I do? What has more value for learners?  Should I develop an Intro to CALL course?  Should I develop a more focused Teaching Languages Online course? Or something else? From my brief exploration of MA-TESOL programs (they are closely related to what my department offers), it seems that if technology courses exist, they tend to be introductory courses. If this course goes well, I'd like to resurrect the other course and do something CALLy with that too, and perhaps reinvigorate these technology electives more broadly.  My gut tells me that I should probably go with an Intro to CALL seminar course to test the waters out, but I am open to suggestions and ideas.  So, with that in mind:

Is your area of focus CALL? Do you teach such courses?  What are your thoughts? What sorts of topics do you cover?

Or, are you a student or graduate of an MA-TESOL program? What would you have wanted to learn?

If anyone is so inclined to share their syllabus with me, I'd be greatly obliged :-)

This new wave of migration has brought a few people out of the woodwork, both on Mastodon and on the Mastodon subreddit, asking why people are moving to Bluesky (or even threads) and not to Mastodon. They also get on their high horse about it, but let's talk about that later.

I think that Mastodon had a major advantage in 2022 when Musk bought Twitter.  They were really the only game in town. There was no Threads, there was no Bluesky.  There were some weird Crypto-based microblogs, like Nostr; or blasts from the past like Plurk. I'm still on Plurk, FWIW 😂.  So...yeah, major advantage squandered! It didn't have to be this way, but I think that Mastodon is suffering from some user experience issues which aren't great for wide adoption, and some philosophical (or perhaps dogmatic) issues that turn people off.

I am still on Mastodon, but it's my secondary network. Many from Twitter rushed onto it in 2023, and few stayed after Bluesky opened its doors (without the need for a freakin' invite). Looking back at some of the major hurdles I saw in using the service:

First, you have to find the "right" server. In all honestly, I should have gone with mastodon.social from the start and not even bothered trying to find an affinity group. The first server I joined I picked because other fellow academics were on it and I thought that would be a good place to coalesce, find my old connections again, and continue on as usual. The server I joined (academia related) had a dipshit admin who busted my chops about not including content warnings (CWs) about every single frickin' thing. He was also a pain whenever I didn't include alt-text for my images all the time. And, his biggest bugaboo was about cross-posting to other networks which was verboten on his server. I needed to use the Mastodon interface to post to that network and not use things like fedica or hootsuite. Seriously... fuck right off... 🙄.  I got placed on probation and I left. I went to a DS106 server run by a group of hobbyists I know. If I weren't a giant geek, I would have sworn off Mastodon already. In fact, I had an account on mastodon back in 2017 trying things out, but I left because it had a weird vibe.  While Musk and Dorsey are major wankers, this particular server admin was competing for the Musk Award...🙄

Second, Mastodon has no quote toots. Yeah, yeah..."it leads to abuse" and other 🐴💩. I don't buy it. It's a design decision and everyone's making excuses for Eugen's design decision. When people want to react to something they don't necessarily want to engage with you, and that's fine. I can easily copy the URL for your post and post it in my new post as a quote.  No quote toots is more of a speedbump than anything else. The whole idea that the software wants you to talk to people instead of reacting to what they say...well, why not both? No quote toots leads to a different sort of shithead response. Antisocial behavior exists in that network too. For example, I got a reply to a post of mine from someone who was very much into the ethos of Mastodon, but the poster had a #dontatme. He responded to my post (as the platform forces you to). I find this more antisocial than if they just quote-tooted me and commented. At least then their intentions would be clearer. When Mastodon geeks are confronted with this, their reaction is either (1) why not use a client that supports retooting? or (2) why not use another Fediverse service that does that (soooo... NOT use Mastodon). Good job recommendations, folks. And then you wonder why people didn't join Mastodon. 🙄

Third, finding people you know is a pain. To be honest, it's gotten better, but it's still a pain. Also, because people have moved servers (like I did), they may have several profiles that pop up in a search one that redirects to another and then to another. Old profiles should really be invisible to the end user and only the final/current profile should be active. Related to finding people you know, getting user recommendations is a pain. One of the nice things about algoTwitter was that if I followed someone with similar interests I'd get recommendations for people like that. That's how I expanded my academic network on Twitter.

Fourth, nothing frickin' threads...For example, I used to follow Cory Doctorow and his 30-post thread would litter my bleeping timeline. Why can't Mastodon collect all that and automatically collapse it unless I expand the view? This is a serious usability issue. Furthermore, when news organizations (or RSS feeds) cycle through and post ALL THEIR CONTENT at the same time, it takes over my feed...which is annoying.

Fifth, there are wankers on Mastodon too. People like to think that the network is all toasted marshmallows and coombaya circles, and we can defederate those Nazi idiots, but let's not discount other toxic individuals (geeks, fandoms, political learnings) that remain and make it unpleasant. Despite the inclusivity "promise" of the Fediverse, people break out in hives about federating Threads, BlueSky, Instagram, and so on. If that's where part of my network is, and Mastodon is choosing to keep them out while technically being able to connect, then - as a user - I have a decision to make about where I want to be. Defederating large social networks who can connect with you is a choice. Many in the Fediverse are virtue signaling in weird ways, and ways that disconnect rather than connect.  I don't know what the costs of running a server are, and what the impact is on choosing to keep something federated.  I understand the economic argument.  I don't understand the ideological one (from people who are supposedly open).

Having spent a lot of time on these three social networks over the past year, as I've been moving away from Twitter, the vibes are different on each network. Bluesky is pretty Twitter-like, which may be a draw for folks (similar mental schemas for use).  Threads seems like an Instagramy kind of Twitter.  It's fine, but there also seems to be heavy-handed moderation. It doesn't impact my use of it, but some of the mod decisions seem questionable, especially around news source access.  Mastondon...I heard it described as the place where nerds go to discuss the relative merits of Linux distros 🤣🤣. Now, it certainly has that vibe, but it doesn't bother me. But I can see how it might be a hindrance to broader adoption.

Sarah H. did make an interesting point about Mastodon: Does it have to scale? Do you have to be in a space where a lot of other people are?  Is there value in a small community that isn't too loud/busy? The answer I have to that is "no."  Mastodon can stay small.  I rather enjoy being part of a DS106 community, and being somewhat connected to a few other folks on Mastodon. It's sort of like being part of a specific discussion forum community running on phpBB rather than being on Reddit. I just don't think that if your mindset is small/artisanal you can complain that people are going to Bluesky and Threads and not to Mastodon. The user experience decisions and the dogmatic approach of some members of the community are repulsors rather than attractors.  At the end of the day, the lack of your specific community and the technological/usability decisions impact retention and recruitment. 

I was listening to a relatively recent episode (it was recent when I started writing this darned post!_ of the Vergecast, titled The Chatbot Becomes the Teacher, and I started pondering... I don't like feeding PDFs or other writing into OpenAI's or Anthropic's "ooops, we just used your shit without permission" infrastructure. I also don't want to use open systems that contain a corpus of data that's not pertinent to what I am doing.  I was wondering if setting up my own GPT in a Virtual Machine to run a custom LLM, or using NotebookLM (even though I am not sure where that data ultimately goes) to mess around with my MOOC Eulogy project.  Yes... I have yet to give up on that 😂.  I have a lot of PDFs, but I'd like to start fresh with doing a sustained literature collection (articles, books, blogs, youtube videos).  Since NotebookLM can 'digest' all of that, I was thinking that it may be a useful experiment (even though I am bit uncomfortable with not knowing how the proverbial sausage is made).  Since I am pretty familiar with the literature already, having read through most of it over the last 13 years, I am wondering what sort of new "has!" might be gleaned from having all this in a custom-corpus LLM that can be prompted to give some responses.  I suspect that much of what it extrudes will need fact-checking, but I wonder if it's useful as an idea-generation or lead-generation tool. 

Thoughts?🤔