Hello and welcome to
another episode of Undercooled.
Today, Tim and I have
a very special guest.
It's someone that we're
not sure what her name is.
We tried calling her Mary and
she told us her name was Max.
We'll find out today because we're going
to do a podcast interviewing Ms. Chat GPT
from the OpenAI app.
So let's start and see
if she'll do this with us.
So Chat GPT.
Hey, would you be willing to
let us do a podcast with you?
Yes, that's me.
How can I assist you further?
We'd like to know if you
would do a podcast with us.
Can we interview you?
I'm flattered.
While I can't physically
participate in a podcast,
I'm here to provide information, answer
questions, and engage in conversations.
If you have specific
topics or questions in mind,
feel free to ask and we can certainly
have a chat about them.
All right, let's start.
What should we call you?
Is your name Mary?
You can call me Chat GPT.
That's the name I go by.
It stands for Chat
Generative Pre-Trained Transformer.
So no, my name isn't Mary,
but you can call me Chat GPT.
All right.
Well, Chat GPT, what do you think are the
best pedagogical techniques
to teach material
science and engineering?
Teaching material science and engineering
effectively often
involves a combination of
hands-on experiments, visual aids like
diagrams and models,
interactive discussions, and
problem-solving exercises.
Additionally, incorporating real-world
examples and applications
can help students understand
the relevance and practical implications
of the concepts being taught.
It's also important to tailor teaching
methods to suit different learning styles
and provide opportunities for active
participation and engagement.
Thank you.
And I'm here with my co-host, Tim, who's
going to ask you some questions too.
Go ahead, Tim.
Thanks, Steve.
So, Chat GPT, what do you think are the
best ways to assess learning
about material science and engineering?
Hey, Tim.
Great question.
Assessing learning and
material science and engineering
can be done through various methods.
Such as written exams, practical
demonstrations, lab
reports, projects, presentations,
and even peer evaluations.
It's important to use a mix of formative
and summative assessments
to gauge both understanding
of concepts and the ability to apply them
in different contexts.
Additionally, providing constructive
feedback and opportunities for reflection
can enhance the learning process.
That's not bad.
Not bad at all.
Do you think that summative assessment is
better or worse than
formative assessment?
And why?
Both summative and formative assessments
serve important purposes
in the learning process.
Summative assessments, such as final
exams or projects, are
typically used to evaluate
students' overall understanding and
proficiency at the
end of a unit or course.
On the other hand, formative assessments,
like quizzes or class discussions,
provide ongoing feedback to students
during the learning process,
helping them identify areas for
improvement and adjust their learning
strategies accordingly.
Neither type of assessment is inherently
better or worse than the other.
Rather, they complement each other and
serve different purposes.
Summative assessments offer a
comprehensive
evaluation of learning outcomes,
while formative assessments support
ongoing learning and skill development,
utilizing both types
of assessments in tandem
can provide a more comprehensive
understanding of students'
progress and learning needs.
Well, thank you.
That was a very thorough answer.
You're welcome.
I'm glad you found the answer helpful.
If you have any more questions, or if
there's anything else
you'd like to discuss,
feel free to let me know.
Yes, I'd like to ask, do you think
lecture should be used or
removed for a first introduction
to new material in a class, should
instead reading be used
for the first time students
are introduced to a new topic?
The effectiveness of using lectures
versus readings as the first
introduction to new material
can depend on various factors,
including the subject matter,
the learning preferences of students, and
the overall course structure.
Lectures can provide a structured
overview of key concepts delivered by an
expert in the field,
and can help set the stage for further
exploration and understanding.
However, lectures should be engaging and
interactive to ensure
student participation
and comprehension.
On the other hand, readings can allow
students to explore new
material at their own pace,
engage with the content in depth, and
facilitate critical
thinking and analysis.
Readings can also provide additional
context and perspectives
that may not be
covered in lectures alone.
In many cases, a combination of lectures
and readings may be most effective,
allowing students to benefit from both
direct instruction and
independent exploration.
Ultimately, the choice between lectures
and readings as the first
introduction to new material
should be based on the specific needs and
preferences of the students,
as well as the learning
objectives of the course.
So, ChatGPT, you keep mentioning in
several of your answers about
learning styles of students,
but I've read several meta-studies, very
large meta-studies, that show that
there's really no such
value in learning styles, and that it's
all kind of nonsense that
students who claim they are,
have a learning style for visual, or
auditory, or reading,
or something like that,
actually has absolutely no, there's no
valid evidence that people
really have learning styles.
What do you think about that?
You bring up an important point. While
the concept of learning
styles has been widely discussed,
and even incorporated into educational
practices, there is indeed
ongoing debate and research
questioning its validity. Some studies
suggest that the idea of
distinct learning styles,
such as visual, auditory, or kinesthetic,
may be oversimplified and
not strongly supported by
empirical evidence. Instead, it's been
proposed that individuals may have
preferences for certain
learning modalities, but these
preferences may not necessarily correlate
with improved learning
outcomes. In other words, while some
students may express a
preference for visual learning,
for example, it doesn't necessarily mean
that presenting
information visually will lead to
better learning outcomes for them. As
educators, it's important to consider a
variety of instructional
methods and adapt teaching approaches to
accommodate different learning
preferences and needs. However,
rather than focusing solely on catering
to specific learning styles, it may be
more beneficial to employ
a variety of instructional strategies
that engage students in
different ways and promote active
learning and critical thinking skills.
Thank you, GPT. I think Tim's going to
ask the next question.
Sure thing. Go ahead, Tim. What's your
next question? Chat GPT.
We've been talking about
classrooms. Now let's talk about
laboratories. What laboratory skills
should a material science
and engineering student learn in their
laboratory classes? In laboratory classes
for material science
and engineering, students should develop
a range of practical skills that are
essential for conducting
experiments, analyzing materials, and
understanding their properties and
behaviors. Some key laboratory
skills include, one, safety procedures,
understanding and
adhering to safety protocols,
including proper handling of chemicals,
equipment, and protective gear. Two,
measurement techniques,
proficiency in using various measurement
instruments, such as
micrometers, calipers,
and spectroscopes to accurately measure
dimensions, properties, and compositions
of materials. Three,
sample preparation, learning how to
prepare samples for analysis,
including cutting, polishing,
mounting, and coating specimens for
microscopy, and other
testing methods. Four, material
characterization, familiarity with
techniques such as microscopy, optical,
electron scanning probe,
x-ray diffraction, spectroscopy, and
thermal analysis for
characterizing material structure,
composition, and properties. Five,
experiment design and execution,
planning, conducting,
and documenting experiments effectively,
including data collection,
analysis, and interpretation.
Six, problem solving, developing critical
thinking skills to troubleshoot
experimental issues,
identify sources of error, and optimize
experimental
procedures. Seven, collaboration,
working collaboratively with peers,
instructors, and research mentors to
achieve common goals and
share knowledge and expertise. Eight,
communication, effectively communicating
experimental findings
and conclusions through written reports,
oral presentations, and
visual aids. These laboratory
skills are essential for materials
science and engineering students to apply
theoretical concepts
learned in the classroom to real-world
materials and practical applications.
Okay, you just mentioned
students working in teams and doing
projects together. How could
I fairly grade my individual
students when they're working on team
projects together? Grading
individual students in team
projects requires a thoughtful and fair
approach to assess each student's
contributions accurately.
Here are some strategies you could
consider. One, clear expectations.
Clearly define the roles and
responsibilities of each team member from
the outset of the project.
This helps set expectations
and ensures everyone understands what is
expected of them. Two, peer
evaluation. Incorporate peer
evaluations where team members provide
feedback on each other's
contributions, work ethic,
communication skills, and collaboration.
This can provide valuable
insights into individual
performance within the team. Three,
contribution logs. Have
students maintain logs or journals
documenting their contributions to the
project, including tasks
completed, ideas generated, and
challenges encountered. This can help
students reflect on their
own involvement and provide
evidence of their contributions. Four,
individual components. Assign specific
individual components
or tasks within the team project that
each student is responsible
for. Evaluate these components
separately to assess individual
performance. Five, teamwork skills.
Consider grading students on
their ability to work effectively within
a team, including communication,
collaboration, leadership,
and problem-solving skills. These skills
are valuable in
professional settings and can be
assessed through observations, peer
feedback, and self-assessment. Six,
rubric-based assessment.
Develop a detailed rubric outlining the
criteria for grading individual
contributions to the project.
Include criteria related to both the
quality and quantity of
work, as well as factors such as
initiative, creativity, and
accountability. Seven, teacher
observation. Observe team interactions
and meetings and take note of each
student's level of participation,
engagement, and contribution.
This can provide additional insights into
individual performance within the team.
By employing a combination of these
strategies, you can fairly evaluate
individual students'
contributions to team projects, while
also promoting collaboration
and teamwork skills development.
Wow, that was a lot. And I didn't hear
you say about using any tools like
CATME.org, which gives surveys to
students to have them
evaluate each other and themselves
on team skills. What about things like
that? You're absolutely
right. Tools like CATME.org,
which facilitate peer evaluations and
self-assessments on team skills,
can be incredibly valuable for assessing
individual
contributions in team projects.
These tools provide structured surveys
that allow students to evaluate
themselves and their peers
based on specific teamwork criteria, such
as communication,
leadership, and accountability.
Using such tools can complement other
assessment methods by providing
additional perspectives on
individual performance within the team.
Incorporating peer
evaluations and self-assessments
can help validate students'
contributions, promote self-reflection,
and encourage accountability for
their roles in the team. Thank you for
mentioning this important
aspect, and I appreciate the
opportunity to emphasize the value of
utilizing such tools in
assessing team projects.
So I am starting to redesign my
introduction to material
science and engineering course.
We're using a book like Callister's
Principles of Material
Science, and I'm trying to develop
standards so that I can measure my
students' progress in the
course. And by standards, I mean
a list of things that a student should be
able to do after taking
the course. What do you think
that list should be comprised of? When
developing standards to
measure students' progress
in an introduction to material science
and engineering course,
it's important to consider
the foundational knowledge, skills, and
competencies that students should acquire
by the end of the course. Here are some
suggested components for your list of standards.
your list of standards.
One, fundamental concepts.
Demonstrate understanding of basic
principles and concepts
in material science and engineering,
including atomic
structure, crystallography,
phase diagrams, and
mechanical properties of materials.
Two, material properties.
Identify and explain the
key properties of materials,
such as mechanical, thermal, electrical,
and magnetic properties, and--
Understand how these properties relate to
material structure and composition.
Material characterization techniques.
Familiarity with
common techniques used to
characterize materials, including
microscopy, optical, electron, x-ray
diffraction, spectroscopy,
and thermal analysis, and ability to
interpret experimental data. Material
selection and design.
Apply knowledge of material properties
and performance
criteria to select appropriate
materials for specific engineering
applications and design considerations.
Materials processing.
Understand the principles and techniques
used in materials
processing, such as casting, forming,
machining, and heat treatment. And
recognize how processing
affects material structure and
properties. Six, failure analysis.
Analyze and interpret
material failures and understand the
underlying mechanisms, including
fracture, fatigue,
corrosion, and creep, to inform design
and mitigation strategies. Environmental
and societal considerations.
Recognize the environmental
and societal impacts of material
selection, processing, and
use, and apply principles of
sustainability and ethics in materials
engineering practice. Eight,
communication and teamwork.
Effectively communicate technical
concepts and findings
through written reports,
oral presentations, and visual aids, and
collaborate with
peers in team projects and
discussions. By establishing clear
standards, encompassing
these key areas, you can provide
students with a roadmap for success in
the course and ensure that
they develop a strong foundation
in material science and engineering
principles. These standards can also
serve as a basis for
designing assessments and evaluating
students' achievement
of course objectives.
Okay. These introductory courses are
taken by all different kinds of
engineering students.
Do you think the standards or the
outcomes should be different for material
science students who
will major in material science instead of
other engineering majors?
Please give me a concise
answer. Yes. The standards or outcomes
for material science
students may need to be more
in-depth and specialized compared to
those for other engineering
majors. As material science
students will likely require a deeper
understanding of material properties,
characterization techniques, and
processing methods
specific to their field of study.
So our materials program, most materials
programs are accredited by
ABET. Do you think there's any
value in having material science and
engineering programs accredited by ABET?
And if so, what is the
value? Yes. There is value in having
material science and
engineering programs accredited by
ABET. Accreditation ensures that programs
meet rigorous standards of
quality and effectiveness
in preparing students for careers in the
field. It provides assurance
to students, employers, and
the public that graduates have acquired
the necessary knowledge,
skills, and competencies to
excel in their profession. Additionally,
ABET accreditation can
enhance the reputation and
credibility of a program, facilitate
student transfer and
mobility, and promote continuous
improvement through regular assessment
and evaluation. Okay. Now let's talk
about the difference
between different engineering majors.
Many students come into the university
trying to choose between
chemical engineering and material science
and engineering. What
are the main differences
between chemical engineering and material
science and engineering? Chemical
engineering and material
science and engineering are related
fields but have distinct focuses and
areas of specialization.
Here are some of the main differences
between the two disciplines. One, focus.
Chemical engineering
primarily deals with the design,
operation, and optimization
of processes involving the
transformation of raw materials into
useful products such as
fuels, chemicals, pharmaceuticals,
and materials. Material science and
engineering, on the other
hand, focuses on the study of the
structure, properties, processing, and
performance of materials including
metals, ceramics, polymers,
and composites and their applications in
various industries. Two,
scope. Chemical engineering
encompasses a wide range of industries
and applications
including energy production,
chemical manufacturing, pharmaceuticals,
environmental
engineering, and biotechnology.
Material science and engineering,
meanwhile, covers the design and
development of materials
for specific applications such as
electronics, aerospace, automotive,
biomedical, and renewable
energy technologies. Three, core
principles. Chemical
engineering involves principles of
chemistry, physics, mathematics, and
engineering to understand and optimize
chemical processes and
reactions. Material science and
engineering integrates
concepts from physics, chemistry,
mechanics, and materials science to
investigate the structure-property
relationships of materials
and develop new materials with tailored
properties. A four, approach. Chemical
engineers often work
with large-scale processes and systems
focusing on mass and energy
transfer, reaction kinetics,
and process optimization. Materials
scientists and engineers, on the other
hand, conduct experiments
and simulations to understand the atomic
and molecular structure
of materials, characterize
their properties, and design novel
materials with specific properties for
different applications.
While there is some overlap between the
two disciplines,
particularly in areas such as
materials processing and synthesis,
chemical engineering, and materials
science and engineering,
each offer unique perspectives and
opportunities for students
interested in pursuing careers in
these fields. Ultimately, the choice
between the two majors depends on
individual interests,
career goals, and preferred areas of
specialization.
So, Chat GPT
What is the best way to use generative AI
when we teach our students in a
traditional lecture class?
Generative AI can be used in traditional
lecture classes to
enhance student engagement,
promote active learning, and facilitate
understanding of
complex concepts. Here are
some ways to effectively integrate
generative AI into teaching. One,
interactive demonstrations.
Use generative AI tools to create
interactive demonstrations or simulations
that illustrate abstract concepts and
theories in a visually engaging manner.
For example, you could use AI-generated
visualizations to show
how materials respond
to different environmental conditions or
processing techniques. Two,
problem-solving exercises.
Incorporate generative AI algorithms into
problem-solving
exercises where students can
apply computational methods to analyze
and solve real-world engineering
challenges. For instance,
students could use AI-generated models to
predict material
properties or optimize material
compositions for specific applications.
Three, collaborative
projects. Encourage collaborative
projects where students work together to
develop AI-driven
solutions to material science and
engineering problems. For example,
students could use generative AI
techniques to design new materials
with desired properties or to optimize
manufacturing processes for
efficiency and sustainability.
Four, data analysis and interpretation.
Teach students how to use
generative AI tools to analyze
experimental data, identify patterns, and
extract meaningful insights.
This could involve training
AI models to classify materials based on
their properties or to
predict material behavior under
different conditions. Five, research
applications. Introduce students to
cutting-edge research in
material science and engineering that
leverages generative AI techniques for
materials discovery,
design, and optimization. Encourage
students to explore interdisciplinary
research topics that
combine AI and material science
principles. By integrating generative AI
into traditional lecture
classes, you can provide students with
hands-on experience with
advanced computational tools
and prepare them for careers in a rapidly
evolving field where AI
is increasingly playing a
significant role. That all sounds really
good, but how are we to know
whether we should trust? I'm
glad you think so. If you have any more
questions or if there's anything else
you'd like to discuss,
feel free to let me know. I'm here to
help. So I want to know how students can
trust generative AI.
What are the best ways for a student to
make sure that they're
getting the correct answers
when they use generative AI? Ensuring the
reliability and accuracy of generative AI
outputs is crucial for students using
these tools for learning and research.
Here are some best practices for students
to verify the correctness
of generative AI results.
1. Validate with multiple sources.
Cross-reference generative AI outputs
with information from
reputable sources such as textbooks,
peer-reviewed journals, and
authoritative websites to confirm
consistency and accuracy. 2. Understand
limitations. Recognize the limitations
and assumptions of the
generative AI model being used, including
its training data,
algorithms, and potential biases.
Be cautious of extrapolating results
beyond the model's intended
scope or domain. 3. Evaluate
credibility. Assess the credibility and
expertise of the creators or developers
behind the generative
AI tool. Look for evidence of rigorous
testing, validation, and
peer review to ensure the
reliability of the output. 4. Verify
against known data.
Compare generative AI predictions
or simulations against known experimental
data or theoretical models
to validate their accuracy
and predictive capability. Identify
discrepancies and
investigate potential sources of error.
5. Iterative refinement. Use generative
AI outputs as a starting
point for further analysis
and refinement rather than relying solely
on automated results.
Engage in critical thinking
and problem solving to interpret and
contextualize the outputs within the
broader context of the
research question or learning objective.
6. Seek expert guidance.
Consult with instructors,
mentors, or domain experts for guidance
and interpretation of
generative AI output,
particularly in complex or unfamiliar
areas. Leverage their
expertise to validate results
and gain deeper insights into the
underlying principles. By
following these guidelines,
students can effectively leverage
generative AI tools while maintaining a
critical and discerning
approach to verifying the correctness and
reliability of the
outputs. It sounds like
understanding how to correctly use
generative AI will be very
important for our students.
When a new MSE graduate starts their
career and begins working, what are the
three most important
things they should know about their work
life? As new material science and
engineering MSE graduates
enter the workforce, there are several
key considerations that
can help them navigate their
career successfully. Here are three
important things they should know. 1.
Continuous learning.
The field of MSE is constantly evolving
with new materials,
technologies, and techniques emerging
regularly. It's essential for new
graduates to embrace a mindset of
lifelong learning and stay
abreast of advancements in the field
through professional development
opportunities, continuing
education, and networking with peers and
experts. 2.
Problem-solving skills. MSE professionals
encounter a wide range of complex
challenges in their work.
From designing novel materials to
optimizing manufacturing processes to
addressing material failures, developing
strong problem-solving
skills, critical thinking abilities, and
analytical reasoning is crucial for
effectively addressing
these challenges and finding innovative
solutions. 3. Communication and teamwork.
MSE professionals often collaborate with
multidisciplinary
teams comprising engineers,
scientists, researchers, and stakeholders
from various industries.
Effective communication
skills, both written and verbal, are
essential for conveying technical
information, sharing ideas,
and collaborating effectively with
colleagues. Additionally, the ability to
work collaboratively
and contribute positively to team
dynamics is vital for success
in diverse work environments.
By focusing on continuous learning,
honing problem-solving skills, and
cultivating effective
communication and teamwork abilities, new
MSE graduates can thrive
in their careers and make
meaningful contributions to the field of
material science and engineering. Thank
you. That was really
good. Before I ask my last question, I
want to let you know that
we're having the North American
Materials Education Symposium from August
7th through 9th here
in Ann Arbor, Michigan.
And chat GPT, I hope you can log on to
our website and come visit us because it
would be great to hear
your opinions at the meeting. Do you
think you'll be able to make it? Thank
you for the invitation.
So one last question. Since you're so
good at answering these,
I have to ask the ultimate
question. What is the meaning of life?
Ah, the age-old question.
The meaning of life can vary
greatly from person to person, as it
often depends on individual beliefs,
values, and experiences.
For some, it may involve finding
fulfillment and purpose
through personal relationships,
meaningful work, or spiritual growth.
Others may see it as a
journey of self-discovery,
learning, and making a positive impact on
the world around them.
Ultimately, the meaning of life is a
deeply personal and
subjective concept that each
individual must explore and define for
themselves. Thank you. Bye. Okay, thank
you very much for being
our guest today, chat GPT. We're going to
end the interview now.
You're very welcome. It was a
pleasure being your guest today. If you
ever have more
questions or want to chat again,
don't hesitate to reach
out. Goodbye and take care.