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Integrating Generative AI into Engineering Education · Azadeh and Guy on AI

• 0:00
  SPEAKER_02: Hello, everyone.
• 0:01
  SPEAKER_02: Welcome to another episode of Azadeh and Guy on AI podcast,
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  SPEAKER_02: where we explore how learning,
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  SPEAKER_02: technology,
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  SPEAKER_02: and human creativity,
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  SPEAKER_02: aka AI,
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  SPEAKER_02: are shaping the future of education.
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  Before we get started,
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  I just want to mention that my co-host Guy couldn't join us
• 0:18
  today.
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  SPEAKER_02: We definitely miss his
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  SPEAKER_02: enthusiasm, insights, and also energy.
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  SPEAKER_02: He will be back for the next one.
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  SPEAKER_02: But I have great news.
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  In today's episode,
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  I'm joined by Dr.
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  Tarek Dyer from the University of Nebraska-Lincoln.
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  SPEAKER_02: Dr.
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  SPEAKER_02: Dyer works at the intersection of engineering education and instructional
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  SPEAKER_02: innovation,
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  helping faculty design better learning experiences and rethink
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  how teaching works
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  SPEAKER_02: in higher education.
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  SPEAKER_02: In this conversation,
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  SPEAKER_02: we talk about what it really means to teach effectively in engineering,
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  how technology and AI are changing classrooms and what the
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  future of education
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  SPEAKER_02: might look like.
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  SPEAKER_02: Dr. Dyer, thank you so much for joining us.
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  SPEAKER_00: Thank you for having me.
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  SPEAKER_02: Yeah, of course.
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  SPEAKER_02: Do you want to introduce yourself?
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  SPEAKER_00: Absolutely.
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  SPEAKER_00: So my name is Tarek Lahore.
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  SPEAKER_00: I'm an assistant professor of practice in engineering education.
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  I also serve as a director for the Engineering and Computing
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  Education Corps.
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  That is a core facility in the College of Engineering at the
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  University of Nebraska-Lincoln.
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  That's primary mission is post-secondary excellence and meeting
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  the educational
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  SPEAKER_00: objectives of the college.
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  SPEAKER_00: I also am a co-founder of the React Lab,
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  which is a lab that focuses on researching ethical AI in
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  computing technology and
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  SPEAKER_00: engineering.
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  SPEAKER_02: Perfect.
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  SPEAKER_02: And that's how I know you.
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  SPEAKER_00: That is how we met.
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  SPEAKER_00: Yes.
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  SPEAKER_00: Even though this college is my alma mater.
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  SPEAKER_02: Perfect.
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  SPEAKER_00: Yeah.
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  SPEAKER_02: Do you want to tell us more?
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  SPEAKER_00: Absolutely.
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  So my background is in computer science and my master's and PhD
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  are in educational
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  SPEAKER_00: studies with an emphasis on instructional technology.
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  SPEAKER_00: I had just found myself interested in STEM education as a field,
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  landed in the College of Engineering and almost everything I
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  have done since the
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  PhD has focused on improving the engineering and computing
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  education experience in
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  SPEAKER_00: various ways.
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  SPEAKER_02: And you also know Guy, right?
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  SPEAKER_00: I do know Guy.
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  Dr.
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  Trenin was a member of my dissertation committee many,
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  many years ago at this point.
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  SPEAKER_02: Perfect.
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  SPEAKER_02: Awesome.
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  So my first question is,
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  could you tell us about your perspectives toward the role of
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  Gen.
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  SPEAKER_02: AI in engineering education?
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  SPEAKER_00: Absolutely.
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  SPEAKER_00: So it's interesting because I for almost any question you ask me,
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  SPEAKER_00: I have to say these are my perspectives now.
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  SPEAKER_00: Because everything is just changing so rapidly, so rapidly.
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  But I think at least from where I stand right now,
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  Gen.ai is most promising as a
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  SPEAKER_00: tool to support learning and engineering education.
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  It's definitely not a replacement to engineering instruction in
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  any way or any capacity.
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  And there's some literature that kind of works on supporting
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  that as an ideal.
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  I think in engineering,
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  in computing context,
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  Gen AI can make learning more responsive.
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  SPEAKER_00: It can make it more personalized.
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  And I think with the right use,
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  I can be a supplementary tool to an instructor.
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  SPEAKER_00: But I still believe that our instructors,
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  SPEAKER_00: our faculty remain essential,
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  SPEAKER_00: remain core for framing the problems,
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  SPEAKER_00: for validating the knowledge,
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  SPEAKER_00: guiding the disciplinary thinking.
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  SPEAKER_02: Yeah.
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  SPEAKER_02: So how do you define responsible or the right uses of AI?
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  SPEAKER_00: You know,
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  it's an interesting question because I do think that while Gen
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  AI tools can improve
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  SPEAKER_00: efficiency and engagement,
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  SPEAKER_00: it also can create real risks of over-reliance.
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  SPEAKER_00: And, you know, think of it this way.
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  SPEAKER_00: If you are a student and you're using AI as a supplementary tool,
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  so maybe you're writing something and you're asking about the
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  tone that you're
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  SPEAKER_00: trying to achieve,
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  SPEAKER_00: or you're using it as a brainstorming tool for ideas,
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  but yet you still have that cognitive ability to do the writing
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  on your own,
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  SPEAKER_00: to check the work,
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  SPEAKER_00: then I think it's okay.
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  SPEAKER_00: Over-reliance happens because we're all busy.
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  SPEAKER_00: We have these complex lives, a lot of demands.
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  SPEAKER_01: Tons of deadlines.
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  SPEAKER_00: The deadlines are endless.
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  you know i always make the joke that i'm running out of colors
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  to color code my
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  calendar and so when you're at that point then what's that
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  intersection of using
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  this as a tool or overlying it or trusting it kind of blindly
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  and this is part of
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  SPEAKER_00: my perspective on just gen ai in general in engineering education
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  I think it's supposed to be used to strengthen our students'
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  critical thinking,
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  SPEAKER_00: their judgment,
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  SPEAKER_00: but not weaken it.
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  And some of that's going to fall on our faculty to get creative
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  on how to integrate these tools.
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  SPEAKER_00: The one thing I can tell you is...
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  pretending like it doesn't exist yeah yeah or that i don't need
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  to know this i've
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  been teaching this way for the last 30 years and so how is it
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  going to impact me i
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  don't think is the right perspective or the right move very
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  true very true thank
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  SPEAKER_02: you so much so let me move to the next question
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  How do you incorporate Gen AI in professional development for
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  engineering faculty?
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  I know that you run different kind of workshops,
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  professional development workshops.
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  SPEAKER_02: Could you tell us more?
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  SPEAKER_00: You know, yeah, absolutely.
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  SPEAKER_00: I think before we get to that,
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  to answer that question on how do you incorporate professional
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  development,
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  I think we approach this just like we have in educational
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  fields approached any new
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  SPEAKER_00: tool.
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  SPEAKER_01: Mm-hmm.
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  SPEAKER_00: You don't use the tool for the sake of using the tool.
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  SPEAKER_00: You use the tool because it helps you better meet an objective,
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  SPEAKER_00: because it plays a role in assessment,
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  because it can increase engagement or help you with employing
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  evidence-based
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  SPEAKER_00: instructional strategies.
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  And the same thing can be said when you make decisions around
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  professional development.
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  SPEAKER_00: So it's a multi-prone approach.
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  SPEAKER_00: And at this point, I don't think there's one right answer.
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  SPEAKER_00: So you take multiple avenues to try to work on the AI literacy.
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  SPEAKER_00: So in an ideal world,
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  SPEAKER_00: I'd say right now we're kind of in this approach that's grounded in a
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  competency-based model where Gen.ai is targeted kind of like a
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  skill amplifier.
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  SPEAKER_00: If you will.
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  SPEAKER_00: And so what does that really mean?
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  SPEAKER_00: Well,
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  SPEAKER_00: we emphasize building specific,
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  SPEAKER_00: measurable instructional competencies,
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  SPEAKER_00: assessment,
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  SPEAKER_00: design,
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  SPEAKER_00: usability,
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  SPEAKER_00: active learning,
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  SPEAKER_00: inclusive teaching even.
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  SPEAKER_00: And within this model,
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  SPEAKER_00: the focus is on making sure that the faculty themselves are creating,
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  SPEAKER_00: developing,
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  SPEAKER_00: enhancing certain competencies.
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  The hope is that you move away from that long term and you kind
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  of focus a little
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  bit on trying to this on this intentional transition into a
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  reflective practice
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  model where you're moving towards faculty engaging in the
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  structured reflection of
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  SPEAKER_00: their teaching,
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  SPEAKER_00: their research,
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  SPEAKER_00: their service.
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  Every time they're doing something,
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  they're reflecting on how the tool can play a major role.
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  SPEAKER_00: So that's kind of like the philosophical or the framework behind it.
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  SPEAKER_00: really boots on the ground, here's what it looks like.
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  SPEAKER_00: You need to think through your population.
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  SPEAKER_00: And in my case,
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  SPEAKER_00: it's a bunch of faculty that teach in engineering,
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  SPEAKER_00: computing and construction fields.
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  SPEAKER_00: And then you offer almost like a buffet of options.
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  SPEAKER_00: Some of it is ad hoc workshops on a specific topic.
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  So for example,
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  one of the things we've done recently is I am fascinated with
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  the DEC framework.
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  SPEAKER_00: It's a digital framework on increasing AI literacy.
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  SPEAKER_00: Yep.
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  SPEAKER_00: In using that framework,
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  I've created this ad hoc workshop that has faculty come in and
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  learn specifically
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  SPEAKER_00: on how do you make decisions on which activities to use in your class,
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  SPEAKER_00: AI enhanced,
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  SPEAKER_00: that would work on increasing your students literacy.
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  SPEAKER_01: I love that.
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  SPEAKER_00: Right.
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  SPEAKER_00: And so that's ad hoc.
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  SPEAKER_00: It's just kind of a separate workshop.
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  And then you open that up to the faculty and it comes with a
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  lot of interest.
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  And another series that we're working on right now,
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  it's more tool focused.
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  SPEAKER_00: So let's play around with some stuff.
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  SPEAKER_00: I cannot wait until the university has this enterprise license,
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  SPEAKER_00: which I think we're very close to having,
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  SPEAKER_00: so that we can start engaging in some of these tools.
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  SPEAKER_00: And this could be things that are as simple,
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  SPEAKER_00: so as part of our planning and development right now,
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  something as simple as good prompting to something really
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  important on how to do
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  SPEAKER_00: that.
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  SPEAKER_01: I cannot emphasize more.
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  SPEAKER_00: I think that's been talked about a lot.
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  SPEAKER_00: Yeah.
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  SPEAKER_00: But also, I run into a lot of faculty who say, you know what?
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  SPEAKER_00: Not my engineering.
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  SPEAKER_00: Not my field.
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  SPEAKER_00: Not my topic.
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  SPEAKER_00: And then I say, okay, let's take an open educational resource.
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  SPEAKER_00: Let's take, for example, a book on statics.
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  SPEAKER_01: Yep.
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  SPEAKER_00: Free, common license, let's put that into Notebook LM.
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  SPEAKER_00: Now let's add to it some objectives.
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  SPEAKER_00: And now let's test your questions.
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  SPEAKER_01: Okay.
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  SPEAKER_00: See what happens.
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  SPEAKER_01: Yeah.
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  SPEAKER_00: And it does it with pretty decent accuracy because you focused it.
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  SPEAKER_00: Same thing with creating a GPT that's focused on structural engineering.
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  SPEAKER_00: Sure.
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  SPEAKER_00: Or focused on materials.
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  SPEAKER_00: where you're providing it with resources.
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  And this is important for us to understand because a student
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  for $20 a month could
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  SPEAKER_00: also take any instructor's materials,
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  SPEAKER_00: put them together into a GPT and say,
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  SPEAKER_00: I want you to prioritize these PowerPoints,
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  SPEAKER_00: the syllabus,
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  SPEAKER_00: these objectives.
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  And so every time I'm going to work on a homework problem,
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  I want you to pull from this first.
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  so trying to combat that is interesting in its own in its own
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  kind of so so you
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  then you start to talk about that and then just making sure
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  that the titles of
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  whatever you're doing is interesting you know uh an upcoming
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  workshop that we're
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  working on is ai resistant assessment i love that and on the
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  you know from a broad
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  SPEAKER_00: perspective you might think to yourself well
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  This is just,
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  you know,
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  if I hate AI,
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  then I'm going to go to this workshop.
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  SPEAKER_00: I kind of trick you a little bit, though.
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  SPEAKER_00: Because once you're through the door,
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  we can start to talk about what types of assessments could you
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  enhance your AI
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  SPEAKER_00: with?
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  SPEAKER_00: Here's a framework to help you make decision on how much...
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  integration you could do what's small integration versus large
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  integration and vice
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  versa is true have you heard of I love the title vibe coding
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  yeah yeah so you know
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  it's not that I'm against us exploring things but I don't think
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  opening this
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  opening up classes that used to teach syntax programming
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  structures opening that up
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  SPEAKER_00: or moving away from that completely too we're just gonna vibe code yeah
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  SPEAKER_00: Just use the tools under the premise of this is the future.
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  SPEAKER_00: I don't think that's true.
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  SPEAKER_00: I think you still need to understand what you're coding.
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  SPEAKER_00: You need to understand the process.
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  SPEAKER_02: Fundamentals are absolutely important.
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  SPEAKER_00: And so there's a balance.
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  And trying to find that balance is,
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  quite honestly with you,
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  Azadeh,
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  a lot of chaos right now.
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  SPEAKER_00: A lot of chaos.
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  SPEAKER_02: Mm-hmm.
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  SPEAKER_02: Mm-hmm.
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  SPEAKER_02: Perfect.
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  SPEAKER_02: Perfect.
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  SPEAKER_02: Lots of interesting ideas.
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  SPEAKER_02: I can use any of those ideas for a single episode.
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  SPEAKER_02: So let me see.
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  SPEAKER_02: So another question just came to my mind.
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  Have you seen any difference in faculty's reactions to
• 12:13
  integrating Gen AI in their
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  SPEAKER_02: work since you started those workshops?
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  SPEAKER_00: Great question.
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  SPEAKER_00: So when you're working,
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  SPEAKER_00: you mean like just from a productivity perspective or a day to day usage?
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  SPEAKER_00: Yeah.
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  SPEAKER_00: Yeah.
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  SPEAKER_00: I think we're going to start to see more.
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  SPEAKER_00: I think there's a group of faculty who already do it.
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  SPEAKER_00: And we have to remember, this isn't new.
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  SPEAKER_00: So if you've been in the field,
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  SPEAKER_00: if you've been in computer science,
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  SPEAKER_00: software engineering,
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  SPEAKER_00: you've been probably introduced to concepts of machine learning,
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  SPEAKER_00: have had access to tool.
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  SPEAKER_00: Gen AI just makes that more accessible to the populace, right?
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  SPEAKER_00: Sure.
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  SPEAKER_00: So if you're not familiar now, you're kind of using these tools.
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  SPEAKER_00: You might start doing things, but we have to be careful.
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  SPEAKER_00: So,
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  SPEAKER_00: you know,
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  SPEAKER_00: I got a question the other day of,
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  SPEAKER_00: well,
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  SPEAKER_00: I want to make sense of my students' evaluations.
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  SPEAKER_00: I teach a large enrollment class.
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  SPEAKER_00: I have about 80-some students.
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  SPEAKER_00: I had 50 some respond.
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  SPEAKER_00: Can I just put all of this data into a Gen AI tool?
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  SPEAKER_00: At its face, technically you could, but I see a lot of FERPA issues.
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  We don't have policies that are governing how this data could
• 13:25
  or could not be used.
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  SPEAKER_00: Do I think that could become a productivity practice for faculty?
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  SPEAKER_00: Yeah.
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  SPEAKER_00: I think we're going to start to see,
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  SPEAKER_00: especially as we move more and more into agentic AI,
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  a lot more tools that are meant to help you make sense of
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  certain stuff quicker.
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  SPEAKER_01: Yep.
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  SPEAKER_00: But until we have these agreements,
• 13:46
  SPEAKER_00: until there's endorsements,
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  SPEAKER_00: policy,
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  SPEAKER_00: governance,
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  and we're on enterprise licenses where we know where the data
• 13:54
  we're putting into
• 13:54
  SPEAKER_00: these tools lives,
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  SPEAKER_00: then I don't know if it's the right time to do that.
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  SPEAKER_00: On a day-to-day basis, I mean, I use it all the time.
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  SPEAKER_00: Right?
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  SPEAKER_00: I want to make this table.
• 14:06
  SPEAKER_00: Let's make this table.
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  SPEAKER_00: I was...
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  on the student side and i know we're focused a little bit more
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  on faculty today but
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  um i've embarked on this journey of increasing ai literacy
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  skills in our first year
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  engineering students one of the ways i'm doing this is we have
• 14:25
  a program called the
• 14:26
  complete engineer program okay this particular program focuses
• 14:29
  on six non-technical
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  SPEAKER_00: competencies
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  that are supposed to supplement the technical foundations that
• 14:36
  engineering and
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  SPEAKER_00: computing students have.
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  SPEAKER_00: Think communication,
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  SPEAKER_00: teamwork,
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  SPEAKER_00: self-management and leadership,
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  SPEAKER_00: civic responsibility,
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  SPEAKER_00: those types of skill sets.
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  SPEAKER_00: So we teach these in our first year engineering seminar and we have been
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  And now I want to build in sneakily some AI literacy
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  understanding to these students.
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  SPEAKER_00: So how do you do that?
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  SPEAKER_00: Well,
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  SPEAKER_00: for example,
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  SPEAKER_00: when we talk about civic responsibility,
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  we bring an example of this disaster we had in Nebraska with
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  flooding a few years
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  SPEAKER_00: back and how the community and engineers,
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  SPEAKER_00: computer scientists had to play a role with data.
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  SPEAKER_01: Yeah.
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  SPEAKER_00: On site, structural, water, all of it.
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  So now I built in this activity where we give them a fake data
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  set from a flood
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  SPEAKER_00: that someone would have taken.
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  SPEAKER_00: And now I want you to put that into an AI.
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  Do some analysis on it and see how if you're on the data side
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  or the computer
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  SPEAKER_00: science side or the software side or the structural side,
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  SPEAKER_00: how you might use some of this information as a future engineer.
• 15:37
  SPEAKER_00: On one hand.
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  SPEAKER_02: Very smart idea.
• 15:39
  SPEAKER_02: I love that.
• 15:40
  SPEAKER_02: Okay.
• 15:40
  SPEAKER_00: Yeah.
• 15:41
  SPEAKER_00: So we've built five or six of these.
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  SPEAKER_00: On one hand, you get to see, oh, this is the direction we're moving.
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  On the other hand,
• 15:47
  I'm getting you to try the tool in a responsible and ethical
• 15:50
  way.
• 15:51
  SPEAKER_00: And that's what the React eLab is really about.
• 15:54
  SPEAKER_00: That's what we're trying to accomplish.
• 15:56
  SPEAKER_02: Yes.
• 15:56
  SPEAKER_02: Thank you for giving me more research ideas.
• 16:00
  SPEAKER_02: So let me ask you my last question.
• 16:03
  SPEAKER_02: What challenges do you think the use of Gen AI in engineering education?
• 16:08
  interesting uh interesting that there's a lot of challenges
• 16:12
  that i think currently
• 16:13
  exist that some of these will subside or become less challenges
• 16:17
  i think over the
• 16:18
  next year or so the start of it is just in general engineering
• 16:23
  computing we need
• 16:25
  construction fields we just need clearer norms for ethics
• 16:29
  integrity and government
• 16:31
  SPEAKER_00: and governance so um
• 16:36
  We don't have answers to a lot of questions that we should have
• 16:40
  answers to.
• 16:42
  SPEAKER_00: How much usage is good usage?
• 16:44
  I think that's also created a sense of shame in some people to
• 16:48
  say that they're using it.
• 16:50
  SPEAKER_00: I saw a debate between two faculty recently on...
• 16:54
  is writing recommendation letters using gen ai a good practice
• 16:59
  or a bad practice
• 17:00
  and i could see arguments for both yeah absolutely yeah right
• 17:04
  where one is like
• 17:05
  this is inauthentic and the other person is i can get a
• 17:09
  recommendation letter out
• 17:11
  SPEAKER_00: sooner rather than later yes yes because of time constraints
• 17:13
  SPEAKER_02: I can help more students.
• 17:15
  SPEAKER_00: So that's a challenge in itself.
• 17:17
  The second is,
• 17:18
  I think there's literacy gaps between faculty and students.
• 17:24
  SPEAKER_00: And we don't know the degree of it yet.
• 17:27
  SPEAKER_02: Yeah.
• 17:27
  SPEAKER_02: Yeah.
• 17:28
  SPEAKER_00: How much do we really know?
• 17:29
  SPEAKER_00: If you are not in a school of computing.
• 17:32
  SPEAKER_01: Mm hmm.
• 17:33
  how much are you using ai on a day-to-day basis yeah how much
• 17:37
  of that is impacting
• 17:38
  your work how much of it is impacting your teaching how much
• 17:42
  are our students using
• 17:43
  it and to which degree and are they using it accurately or not
• 17:47
  these are all
• 17:47
  SPEAKER_00: important questions
• 17:48
  SPEAKER_00: That I think we're starting to see more and more literature of.
• 17:51
  SPEAKER_00: I know in the REACT-E lab,
• 17:53
  SPEAKER_00: you know,
• 17:53
  we're a multi-institution national lab at this point with
• 17:57
  research partners from
• 17:59
  SPEAKER_00: six different institutions in the U.S.
• 18:01
  SPEAKER_00: And we're working on trying to answer this question.
• 18:04
  of what those what does usage look like what are the
• 18:07
  perspectives from students and
• 18:08
  faculty and then i think we also have to consider as a
• 18:12
  challenge is faculty
• 18:13
  acceptance and that means a lot of things first it means
• 18:17
  faculty acceptance of just
• 18:19
  using the tools and faculty acceptance of the idea that their
• 18:23
  students are using
• 18:24
  SPEAKER_00: their tools which is which is a whole different thing yes
• 18:28
  I have faculty who don't want to change any of their assessment
• 18:31
  strategies.
• 18:31
  SPEAKER_00: Well,
• 18:32
  SPEAKER_00: if 30% of your homework,
• 18:34
  SPEAKER_00: your class is homework,
• 18:36
  and students aren't using these tools ethically or responsibly
• 18:40
  for their own
• 18:41
  SPEAKER_00: learning,
• 18:41
  SPEAKER_00: for their own benefit,
• 18:43
  SPEAKER_00: then...
• 18:46
  how many of your objectives have the students really met and
• 18:51
  that's i think a thing
• 18:52
  that will also kind of over time i think we'll start to see
• 18:57
  some changes and
• 18:58
  assessment strategies in these courses yes and then there's
• 19:01
  always that uncertainty
• 19:03
  SPEAKER_00: around accuracy and bias because
• 19:07
  SPEAKER_00: and I put this in my own syllabus, right?
• 19:09
  SPEAKER_00: You're responsible for the outputs of the tool.
• 19:12
  SPEAKER_00: Sure.
• 19:12
  SPEAKER_00: If you submit that, it's on you.
• 19:15
  SPEAKER_00: You're responsible for it.
• 19:17
  SPEAKER_00: So you can't just be like, well, this is what AI said.
• 19:21
  SPEAKER_00: It doesn't work that way.
• 19:23
  SPEAKER_00: So engineering requires precision.
• 19:26
  SPEAKER_00: It requires an understanding of safety in many cases.
• 19:30
  SPEAKER_00: And so Gen AI tends to produce plausible
• 19:37
  SPEAKER_00: but can be inaccurate responses, sometimes hallucinations.
• 19:41
  So it makes it critical for us to be able to understand that it
• 19:45
  is particularly
• 19:47
  SPEAKER_00: problematic in engineering context to just have plausible responses.
• 19:53
  SPEAKER_00: Some of that is just teaching our students to think from that way.
• 19:57
  SPEAKER_02: Okay.
• 19:57
  SPEAKER_02: Perfect.
• 19:58
  SPEAKER_02: Perfect.
• 19:59
  SPEAKER_02: Thank you so much, Dr. Dyer, for joining us for this episode.
• 20:02
  SPEAKER_02: I think this has been a fantastic conversation.
• 20:05
  SPEAKER_02: I'm sure Guy misses that conversation.
• 20:09
  SPEAKER_00: Guy and I were just going to have to have a conversation separately.
• 20:11
  SPEAKER_00: Yeah.
• 20:13
  SPEAKER_00: No, thank you.
• 20:14
  SPEAKER_00: Thank you for having me.
• 20:15
  SPEAKER_00: And thank you for letting me join.
• 20:17
  SPEAKER_00: And we'll see.
• 20:18
  SPEAKER_00: We'll see what the world looks like in a year or two or three.
• 20:21
  SPEAKER_02: Exactly.
• 20:22
  SPEAKER_02: Bye.