Latest / Azadeh and Guy on AI / AI Skeptics or Cautious Critics?

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AI Skeptics or Cautious Critics? · Azadeh and Guy on AI

• 0:01
  SPEAKER_01: Hi and welcome to Azaveh and Guy on AI.
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  And today we're talking about AI resistance mostly because it's
• 0:09
  been in the news.
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  We've had students booing people talking about AI in graduation
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  ceremonies.
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  We know for a fact from surveys around the United States that
• 0:21
  Pew is starting to
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  conduct some surveys and other outfits that are really looking
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  at the resistance to
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  SPEAKER_01: AI across the US population.
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  SPEAKER_01: And it seems like resistance is fairly high,
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  SPEAKER_01: about 70% are resisting AI and more specifically resisting data centers.
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  SPEAKER_01: So it seems like there's a lot of emphasis there.
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  SPEAKER_01: Because data centers, I think, is one of those things you can seek.
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  SPEAKER_01: You can see it come up.
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  SPEAKER_01: You can see it built.
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  SPEAKER_01: You can imagine the water and the energy use and other things.
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  And so that's a very physical manifestation of something that
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  is really hard to see.
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  And I was thinking about that Tom Cruise movie where he's
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  fighting AI,
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  right?
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  SPEAKER_01: The fact that in the movie, you have to give AI a face.
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  SPEAKER_01: You got to make it a thing that has a physical reality.
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  SPEAKER_01: Because it's really hard for us to imagine this.
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  SPEAKER_01: It's just like the conversations for years.
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  SPEAKER_01: It's like,
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  SPEAKER_01: It's in the cloud.
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  SPEAKER_01: Well, where is that cloud?
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  SPEAKER_01: It's a data center.
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  And now we call it data centers,
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  but for a long time we called it the cloud.
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  SPEAKER_01: That was very clever marketing,
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  SPEAKER_01: by the way,
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  SPEAKER_01: because the cloud sounds like,
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  SPEAKER_01: oh,
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  SPEAKER_01: it's somewhere else and it's flying above and it's white and fluffy,
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  not it's taking water and energy and it's really a place with a
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  lot of machines and
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  SPEAKER_01: very few workers.
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  SPEAKER_01: They never said that.
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  SPEAKER_01: They should have called it the Cloud AI.
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  SPEAKER_01: We're building clouds in your neighborhood.
• 1:51
  SPEAKER_01: It's fine.
• 1:53
  SPEAKER_00: And we are recording the first episode of Season 3.
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  SPEAKER_01: Yes, welcome to Season 3.
• 1:59
  SPEAKER_01: It is summer here in Nebraska.
• 2:02
  SPEAKER_01: Some days are very hot and humid, and some days are kind of cool.
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  SPEAKER_00: Super cool.
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  SPEAKER_01: Oh, and I have a flooded basement, so everything is perfect.
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  SPEAKER_01: All right.
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  SPEAKER_01: So, we collected some studies and let's talk about some of the findings.
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  SPEAKER_00: So,
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  SPEAKER_00: the title of the first study that you're going to talk about,
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  SPEAKER_00: and this study has been published in 2026,
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  SPEAKER_00: so very,
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  SPEAKER_00: very recent.
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  Understanding Higher Education Students' Reluctance to Adopt
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  Gennarii in Learning
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  SPEAKER_00: in Latvia,
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  SPEAKER_00: and it's been published
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  SPEAKER_00: I'm not sure if I'm very good at pronouncing the author's names.
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  SPEAKER_00: So I'm not going to do that.
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  SPEAKER_00: And we can always put a link in our description.
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  SPEAKER_01: Yeah, we'll put a link with the DOI.
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  SPEAKER_01: So that's a live link that connects you to what it actually says.
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  And it's about the title is Understanding Higher Education
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  Students Reluct
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  SPEAKER_01: And we're talking about hundreds of students,
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  SPEAKER_01: so 200 students in Latvia and 685 in the Ukraine,
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  SPEAKER_01: which just says it's a reasonably large sample.
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  SPEAKER_01: It's not a very small sample.
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  SPEAKER_01: And so from all of them,
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  What we've learned is that about 22% of students reported not
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  using GenAI at all.
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  SPEAKER_01: Of course, this is with a caveat.
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  SPEAKER_01: In 2026,
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  SPEAKER_01: we're all using AI because if you're doing a Google search,
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  SPEAKER_01: the AI is in the background.
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  SPEAKER_01: So you're not...
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  SPEAKER_00: It's incorporated into almost everything.
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  SPEAKER_01: Yeah.
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  And that's something that we have to keep in mind,
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  but they're not at least
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  SPEAKER_01: Stavros Stavros Stavros
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  SPEAKER_01: Guy Trainin guytrainin522619 Guy Trainin guytrainin522619
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  What is interesting is that there doesn't seem to be in the
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  study a lot of ethical
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  SPEAKER_01: resistance to AI.
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  SPEAKER_00: Yeah, that's true.
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  SPEAKER_00: One more thing, the majority of the participants are bachelor's students.
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  SPEAKER_01: Yeah,
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  SPEAKER_01: so it's a nice distribution,
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  but what they're saying really is the top three reason for
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  non-adaption was they
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  SPEAKER_01: don't believe it works.
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  SPEAKER_01: And that's almost half of the people that talked about this said,
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  SPEAKER_01: It doesn't give me what I need.
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  SPEAKER_01: It's not doing the quality work.
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  SPEAKER_01: It has hallucinations.
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  SPEAKER_01: All of these things.
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  SPEAKER_00: I can do it better myself.
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  SPEAKER_00: I believe in my own work.
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  SPEAKER_00: I don't trust AI that much.
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  SPEAKER_00: The second reason was insufficient information about tools.
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  SPEAKER_00: And we have seen that a lot in our classes,
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  SPEAKER_00: in interviews that we conducted,
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  SPEAKER_00: in our research projects.
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  Many people do not have enough idea,
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  enough knowledge about how different AI tools work.
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  SPEAKER_01: And the last one is the lack of knowledge.
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  SPEAKER_01: So the idea is,
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  SPEAKER_01: I know I can chat with it,
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  SPEAKER_01: but I'm not sure how to get out of it,
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  SPEAKER_01: what I need to get out of it,
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  SPEAKER_01: which is,
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  SPEAKER_01: again,
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  SPEAKER_01: very reasonable.
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  SPEAKER_01: We've seen this all along.
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  I'm always surprised,
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  for example,
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  when students say they don't know what Notebook LM is.
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  And then I remember that not everybody is living,
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  breathing AI all the time.
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  SPEAKER_01: So it's got to be okay.
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  SPEAKER_01: Of course,
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  SPEAKER_01: A in all of these studies,
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  they also looked at student majors as a way to see if there's a
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  difference between
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  SPEAKER_01: different populations.
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  SPEAKER_01: And what we always see is that
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  SPEAKER_01: Computer science students are much more likely to use AI,
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  partially because AI starts and is most helpful right now in
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  computer science.
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  SPEAKER_01: Also,
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  SPEAKER_01: and we've talked about this before when we talked about computer science,
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  SPEAKER_01: they've been using it a lot longer.
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  SPEAKER_01: So Gen AI has shown up for us fairly recently,
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  SPEAKER_01: but in computer science for 10 to 10,
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  SPEAKER_01: 15 years,
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  SPEAKER_01: they've been using different versions,
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  SPEAKER_01: earlier versions of AI.
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  So the key implication of that study is that resistance is
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  largely driven by
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  SPEAKER_00: knowledge gaps and perceived irrelevance,
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  SPEAKER_00: not by ethical rejection.
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  SPEAKER_00: That was kind of surprising for me to be honest.
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  SPEAKER_01: Yeah,
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  SPEAKER_01: because we're,
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  SPEAKER_01: again,
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  SPEAKER_01: that's why I wanted to make that split,
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  SPEAKER_01: because in the United States,
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  SPEAKER_01: I think that the ethical considerations are considerably higher.
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  SPEAKER_01: In other countries, that doesn't seem to be as burning.
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  SPEAKER_01: But also,
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  SPEAKER_01: we have to be honest,
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  they are not having the same number of data centers emerging
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  next to them.
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  SPEAKER_01: So they are a little less concerned from that perspective.
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  SPEAKER_01: But it's also very much a different conversation.
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  SPEAKER_01: So I can, for example, talk about Estonia when I was there.
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  SPEAKER_01: about a year and a half ago,
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  SPEAKER_01: they seem to be awfully excited about AI,
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  SPEAKER_01: at least at that point in time.
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  SPEAKER_01: I don't know if it has changed.
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  SPEAKER_01: It's a country that's embracing technology, embracing moving forward.
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  SPEAKER_01: I know that I've been to Singapore, and that's very much the same.
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  SPEAKER_01: It seems like a national goal to be fully embedded in AI,
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  SPEAKER_01: thinking about how we can benefit from that.
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  SPEAKER_01: So different countries react differently, and I think that creates a mood
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  In which it creates and the United States is the place where
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  we're seeing also the
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  most a doom around jobs so if you're thinking about college
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  students they're
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  thinking about I'm going to finish university and I'm going to
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  go out to the job
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  market and AI is going to steal my job obviously you're going
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  to have a negative
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  SPEAKER_01: reaction to that
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  SPEAKER_01: All right, so what's the second study?
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  Second paper,
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  the title of the paper is AI Advocates and Cautious Critics.
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  SPEAKER_00: How AI Attitudes,
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  SPEAKER_00: AI Interest,
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  Use of AI and AI Literacy Builds University Students' AI as
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  Self-Efficacy.
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  It's been published in Computers and Education and Artificial
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  Intelligence Journal.
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  SPEAKER_01: Which is a journal we love.
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  SPEAKER_00: Yeah, yeah, absolutely.
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  SPEAKER_01: Just saying.
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  SPEAKER_00: Yes.
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  SPEAKER_00: And it's again another survey study.
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  They have about 1500 undergraduate and graduate students from
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  the United States,
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  SPEAKER_00: the United Kingdom and also Germany.
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  SPEAKER_01: And they measured a whole variety of things.
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  So they looked at AI self-efficacy,
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  AI literacy,
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  interest in AI and the attitudes and AI use.
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  SPEAKER_01: So they're kind of trying to get a full picture there.
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  SPEAKER_00: So when it comes to the findings of the study,
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  SPEAKER_00: they have figured out that they can have two major student typologies.
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  SPEAKER_00: One of them is AI advocates,
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  SPEAKER_00: those students who have high AI literacy,
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  SPEAKER_00: self-efficacy,
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  SPEAKER_00: interests,
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  SPEAKER_00: and positive attitudes,
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  SPEAKER_00: and obviously high usage of AI,
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  SPEAKER_00: and also cautious critics.
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  which are 21% of the sample with below average AI literacy and
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  self-efficacy,
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  SPEAKER_00: high negative attitudes and low usage.
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  SPEAKER_01: So they're kind of low on everything or except negative attitude,
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  SPEAKER_01: which is again,
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  SPEAKER_01: low on everything.
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  SPEAKER_01: And what's interesting about the cautious critics,
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  SPEAKER_01: just like in the last study,
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  SPEAKER_01: they are not distributed equally across the population.
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  SPEAKER_01: So they tend to be much more from the humanities and social sciences.
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  SPEAKER_01: And they tend to be
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  SPEAKER_01: Guy’s Substack Guy Trainin
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  Another interesting study that adds a different perspective
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  from a different place,
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  SPEAKER_01: although showing the same kind of concern.
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  SPEAKER_00: So let me see.
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  The other study that we went through is to adopt or to ban the
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  student perceptions
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  SPEAKER_00: and use of generative AI in higher education.
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  SPEAKER_00: It's been published in Humanities and Social Sciences Communications.
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  SPEAKER_00: They again, they have done an online survey.
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  SPEAKER_00: Obviously the survey had a bunch of closed and also open-ended questions.
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  SPEAKER_01: And this is in Italy, which is, again, a little bit different.
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  The key findings were that about 82% of students really had
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  reservation about the
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  SPEAKER_01: accuracy of AI outputs.
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  SPEAKER_01: So that really reflects the same study from Latvia and Ukraine,
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  SPEAKER_01: although the numbers are much,
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  SPEAKER_01: much higher.
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  SPEAKER_01: And it may be connected to the language we're working in.
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  So I am guessing that the Ukrainian and Latvian students are
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  mostly interacting in
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  SPEAKER_01: English and that Italian students may be trying to interact in Italian.
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  And we know that the best outputs are still in English because
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  just the learning
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  SPEAKER_01: corpus was much larger.
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  SPEAKER_00: 63.3% read that chat GPT output could be mistaken for human written text.
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  SPEAKER_00: Yeah,
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  SPEAKER_01: so that's really interesting because on one hand,
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  SPEAKER_01: 82% saying that they're worried about accuracy,
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  SPEAKER_01: but they're also saying it's indistinguishable.
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  SPEAKER_01: Maybe it's because people make mistakes too.
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  SPEAKER_01: That's probably it.
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  And we can see a similar result with the other study that we
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  just went through.
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  SPEAKER_00: So there is a gender disparity.
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  Male students were significantly more likely to engage with
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  GenAI tools than female
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  SPEAKER_00: students,
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  SPEAKER_00: just like the other study that we just talked about.
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  SPEAKER_01: So,
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  and the difference here is that ethical concerns were
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  considerably higher than in
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  SPEAKER_01: the other studies.
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  So here we're starting to see some ethical concerns about
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  generative AI,
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  and this is
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  SPEAKER_01: And this is really interesting because this was published in November 25,
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  SPEAKER_01: which means that the data is either from early 25 or even from 2024.
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  SPEAKER_01: And that leads to a different judgment.
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  It seems like the public opinion moves quite fast on this
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  because this is so new.
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  SPEAKER_01: So it could be that this has changed.
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  SPEAKER_01: The second thing is,
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  SPEAKER_01: and I think that's important,
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  SPEAKER_01: that the accuracy,
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  SPEAKER_01: if you think about where we were in 2024 versus where we are now,
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  SPEAKER_01: accuracy has also changed.
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  So it could be that their worries about the accuracy would be
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  somewhat diminished right now.
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  SPEAKER_01: And so do we want to talk about another study?
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  SPEAKER_00: Let me see.
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  SPEAKER_01: Okay, let's talk about another study.
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  So the title of this study is Artificial Intelligence in Higher
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  Education,
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  SPEAKER_00: Student Perspectives on Practices,
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  SPEAKER_00: Challenges,
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  SPEAKER_00: and Policies in a Transitional Context.
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  SPEAKER_00: It's been published in Frontiers in Education.
• 13:51
  SPEAKER_01: And if you,
• 13:52
  SPEAKER_01: just as a word,
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  SPEAKER_01: if you'll notice,
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  SPEAKER_01: a lot of these publications are publications that are open source,
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  SPEAKER_01: so you can find those and there's no paywall.
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  SPEAKER_01: But it's also because many of these open source
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  SPEAKER_01: publications tend to do very fast reviews, which is helpful.
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  So it makes you a little bit question how fast can you get the
• 14:16
  data out there.
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  SPEAKER_01: But in the age of AI,
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  SPEAKER_01: and we've talked about this,
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  SPEAKER_01: we need to get research out there in a quick way.
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  And you don't want to talk about what happened in 2021 right
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  now because it seems
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  SPEAKER_01: highly irrelevant.
• 14:30
  SPEAKER_01: So the ability to publish fast is important.
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  SPEAKER_01: And that's why we went for these studies specifically.
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  SPEAKER_01: So what did they find?
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  SPEAKER_00: So students who have taken a course,
• 14:40
  SPEAKER_00: AI course,
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  SPEAKER_00: they report a significantly higher frequency of AI use,
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  SPEAKER_00: which is not obviously surprising.
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  It seems that there is a positive correlation between AI
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  literacy and also AI usage.
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  And the students who lack AI training reported a very strong
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  correlation with
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  SPEAKER_00: difficulties using AI.
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  SPEAKER_01: And that's interesting.
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  SPEAKER_01: This is something we've seen in my class,
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  SPEAKER_01: for example,
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  SPEAKER_01: when we tried AI,
• 15:08
  and that is initially AI seems very simple because the window
• 15:12
  in the wisdom that
• 15:13
  SPEAKER_01: started with OpenAI,
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  SPEAKER_01: it's a box just like a search.
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  SPEAKER_01: So it invites just write something in and ask for something.
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  Where the difficulty arises is not when you're doing that
• 15:25
  initial contact,
• 15:25
  SPEAKER_01: but when you have something very specific you're asking it to do.
• 15:29
  SPEAKER_01: And then
• 15:31
  Sometimes you get nothing like what you wanted and finding the
• 15:34
  right words and the
• 15:35
  right ways to iterate and get a good product is a little bit of
• 15:40
  a challenge.
• 15:42
  And that's what I seem to be kind of getting the vibe from
• 15:47
  based on these results.
• 15:49
  SPEAKER_00: And it's not only about prompting.
• 15:51
  SPEAKER_00: Sometimes it even goes back to your knowledge gaps.
• 15:55
  I don't understand the concept for this,
• 15:57
  so I cannot formulate a good prompt.
• 15:59
  SPEAKER_01: Yeah, and that's the thing about AI, right?
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  It can give you whatever you ask,
• 16:04
  but you got to know what you're asking for.
• 16:06
  SPEAKER_01: And so the content knowledge and in our case,
• 16:09
  SPEAKER_01: the pedagogical knowledge or the technology knowledge,
• 16:13
  SPEAKER_01: they have to be,
• 16:15
  SPEAKER_01: there's got to be enough there.
• 16:18
  SPEAKER_01: Otherwise, it just doesn't know what you want.
• 16:19
  SPEAKER_01: You don't even know what to ask.
• 16:22
  SPEAKER_01: And obviously you can't evaluate it.
• 16:24
  SPEAKER_01: So there's skepticism out there and I think that that's really important.
• 16:31
  That idea that there is resistance and some of that resistance
• 16:34
  is because we don't
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  know or we're not sure of the policy or we have concerns really
• 16:41
  call for different
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  SPEAKER_01: reactions.
• 16:44
  SPEAKER_01: And that's what we've tried to enact,
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  SPEAKER_01: for example,
• 16:46
  this semester is let's ask you to use it so you can evaluate it
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  and see where it
• 16:51
  SPEAKER_01: works and it doesn't work.
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  SPEAKER_01: But also providing for enough space for resistance.
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  SPEAKER_01: So if somebody is saying, I do not want to use it, this is not for me.
• 17:03
  SPEAKER_01: At least in my classes right now, I'm providing them the space.
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  SPEAKER_01: So if you're in class and that's part of the assignment, you will use it.
• 17:10
  SPEAKER_01: But if you don't want to use it anywhere else, that's perfectly fine.
• 17:13
  SPEAKER_01: And when we talked to Rachel, it was the same thing.
• 17:18
  SPEAKER_01: She talked about, it's perfectly fine to say to students, you can resist.
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  SPEAKER_01: I respect your resistance and we'll find a different way to do the work,
• 17:30
  SPEAKER_01: but we still have to do the work.
• 17:33
  The intellectual work and I'm not I'm not sure even who's right
• 17:36
  and who's wrong.
• 17:37
  SPEAKER_01: I mean, I don't know that we know these things.
• 17:41
  SPEAKER_00: So I think that's it.
• 17:43
  SPEAKER_01: Yeah, that's it.
• 17:44
  SPEAKER_01: So what are we saying?
• 17:45
  SPEAKER_01: We're saying resistance is real.
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  We're saying we have to pay attention because these shift
• 17:51
  between who you're teaching,
• 17:53
  right?
• 17:54
  SPEAKER_01: So women are
• 17:56
  You are more resistant than men or they're more cautious
• 17:59
  critics is that category.
• 18:01
  SPEAKER_01: I love that.
• 18:02
  SPEAKER_01: They're not necessarily wrong.
• 18:05
  SPEAKER_01: We don't have to fix them as students or as individuals.
• 18:08
  SPEAKER_01: It's just this is their disposition.
• 18:10
  SPEAKER_01: They're not right.
• 18:11
  SPEAKER_01: You are not trusting technology as much.
• 18:12
  SPEAKER_01: They are not trusting this technology as much.
• 18:15
  SPEAKER_01: This is perfectly fine.
• 18:16
  SPEAKER_01: They may actually be right about it.
• 18:19
  SPEAKER_01: So we know women resist more.
• 18:21
  SPEAKER_01: We know people in the humanities and the social sciences resist more.
• 18:25
  SPEAKER_01: Computer science are all in.
• 18:27
  SPEAKER_01: Just let us do our thing.
• 18:29
  SPEAKER_01: Which I totally sympathize with.
• 18:31
  SPEAKER_01: But I also know that they might be wrong.
• 18:34
  And we know that there's a difference between Western Europe
• 18:37
  and the United States
• 18:38
  SPEAKER_01: and Canada versus other places.
• 18:41
  SPEAKER_01: And that doesn't go in the direction these things usually go.
• 18:46
  So there's actually more resistance in the classic West than
• 18:49
  there are in other places.
• 18:51
  And I'm sorry,
• 18:52
  Latvia and Ukraine,
• 18:52
  that I'm not calling you the classic West.
• 18:57
  SPEAKER_01: And, you know.
• 18:59
  SPEAKER_00: Yeah.
• 19:01
  SPEAKER_01: Classic in the Soviet era.
• 19:03
  And we're not in the Soviet era,
• 19:04
  but I think some tendencies still remain.
• 19:06
  And in this case,
• 19:08
  it just means you're more enthusiastic about AI,
• 19:10
  so yay for you.
• 19:14
  SPEAKER_01: But direct angry comments at my Substack.
• 19:17
  SPEAKER_01: Bring it on, people.
• 19:19
  SPEAKER_01: Any interaction is good interaction.
• 19:21
  SPEAKER_01: That's what I'm saying.
• 19:23
  SPEAKER_01: So thank you.
• 19:24
  SPEAKER_01: This is our first episode in the new season, season three.
• 19:29
  We're very excited for season three because we have some people
• 19:32
  lined up to talk to
• 19:35
  SPEAKER_01: us in the next few weeks.
• 19:36
  We've got Nikki Schafer coming on the 17th and talking to us
• 19:43
  about her work.
• 19:45
  She just finished a dissertation and she looked at AI and
• 19:47
  there's some interesting
• 19:48
  SPEAKER_01: things about how to communicate about AI.
• 19:51
  SPEAKER_01: And then Jeremy Reel will maybe come in June
• 19:56
  SPEAKER_01: Maybe June 24th.
• 19:58
  SPEAKER_01: He has not answered my email.
• 19:59
  SPEAKER_01: Jeremy, if you're listening, just answer my email, please.
• 20:02
  SPEAKER_01: And he will hopefully be here and talk about the work he's doing.
• 20:06
  And he also has got a podcast to talk about AI so he can talk
• 20:09
  about this podcast
• 20:10
  SPEAKER_01: and get all of our millions of listeners.
• 20:15
  SPEAKER_01: So we'll see you next time.