FEATURING
Special Edition
Special Topics
By Dr. Ghalia Nassreddine,
Rafik Hariri University,
Lebanon;
Dr. Obada Al-Khatib,
University of Wollongong
in Dubai, UAE;
Dr. Mohamad Nassereddine,
University of Wollongong
in Dubai, UAE
Dr. Tanujit Chakraborty,
Sorbonne University
Abu Dhabi UAE
Academic Perspectives
Professor Derin Ural,
University of Miami, U.S.
Leadership Spotlight
Professor Khalid Hussain,
Dean of Engineering and
Computing,
AURAK, UAE
Industry Perspectives
Dr. Waddah Ghanem
Al Hashmi, Chairman of the OSH
Federal Committee in the UAE
Student Voice
Nour Mostafa Kamel,
AURAK, UAE
Trends
Anne-Gaelle Colom,
University of Westminster, UK
The Age of
Generative AI
February 2025
Contents
12
Editorial
Welcome to UniNewsletter
Laura Vasquez Bass
04
Special Topics
Unlocking the Role of Generative AI in
Engineering Education: An Overview of its
Opportunities and Challenges
By Dr. Ghalia Nassreddine, Computer and
Information Systems Department, Rafik Hariri
University, Lebanon; Dr. Obada Al-Khatib,
School of Engineering, University of Wollongong
in Dubai, UAE; Dr. Mohamad Nassereddine,
School of Engineering, University of Wollongong
in Dubai, UAE
Special Topics
Turning Data Science
into Action: Case
Studies in Building a
Sustainable Future
with Data Science
Engineering
By Dr. Tanujit
Chakraborty
Associate Professor of
Statistics and Data
08
02 | Special Edition
Science, Sorbonne University
Abu Dhabi, UAE
Industry Perspectives
Student Voice
Trends
36
22
32
Leadership Spotlight
Embracing AI-driven Solutions at
the American University of Ras Al
Khaimah (AURAK):
An Interview with Professor Khalid
Hussain, Dean of Engineering and
Computing
The Role of Engineers in Society
and Industry:
A life skill, and not necessarily a
profession
By Dr. Waddah S Ghanem
Al Hashmi, Chairman of the OSH
Federal Committee in the UAE and
Senior Director in the Energy Sector
18
Academic
Perspectives
Teaching an Engineering Class with
a Chatbot as a Teaching Assistant
By Professor Derin Ural, College of
Engineering, University of Miami, U.S.
From Resistance to
Integration: A Reflection
on my Journey with AI in
Academia
By Nour Mostafa Kamel,
Bachelor of Science in
Computer Engineering,
American University of Ras
Al Khaimah (AURAK), UAE
40
Adapting to the Future:
Preparing for Software
Development in the Age
of Generative AI
By Anne-Gaelle Colom,
Assistant Head of School
and Learning and
Teaching Director, School
of Computer Science and
Engineering, University of
Westminster, London, UK
03
Special Edition |
It has been a
wonderful experience
to work with this
broad set of engineers
and we sincerely hope
you enjoy the
interesting insights
and words of wisdom
they have to offer.
Welcome to
UniNewsletter
While I am incredibly happy for my decision to
pursue advanced degrees in the Humani-
ties—they rigorously equipped me for my
work with UniNewsletter, after all—reading
this fascinating issue on various forms of
engineering pathways and AI caused me to
envy the lucky students who have this learn-
ing trajectory ahead of them. This special
issue, “Engineering in the Age of Generative
AI,” is composed of a truly diverse range of
engineers, those trained in civil, mechanical
or environmental engineering, as well as
computer software engineers. Appreciating
the tremendous impact that AI is having on
education, we thought it apt to highlight the
shifting debates on how a highly technical
discipline like engineering both benefits
hugely from the streamlining capabilities of
AI, but also consider its dangers in engineer-
ing education. This issue’s Student Voice
writer,
Nour
Mostafa
Kamel,
incisively
describes this dialectic as the “promise and
perils” of AI. We wanted to ask how students in
various engineering pathways could achieve
the AI literacy necessary for job placement
these days, while also acquiring the immense
amount of practical and technical aptitude
that is foundational to the skillset of engi-
neers. This issue’s contributors answered our
call thoughtfully, profoundly and offered an
abundance of instructive advice to both insti-
tutions with engineering programs and the
students enrolled or applying to them. They
even posed many questions of their own,
which we are sure will prove enlightening
reading.
Beginning the issue with the first of two
articles in our Special Topics section is a
highly informative co-authored piece by Dr.
Ghalia Nassreddine from Rafik Hariri Universi-
ty, Lebanon, and Drs. Obada Al-Khatib and
Mohamad Nassereddine from the University
of Wollongong in Dubai, UAE. They offer a
Laura Vasquez Bass
“
“
04 | Special Edition
Editorial
detailed, panoramic discussion of the opportuni-
ties and challenges faced by institutions in incor-
porating AI into engineering education. For those
seeking a grounding in how AI technologies can
be used in various capacities to support student
experience, this is a must read. Our second
Special Topics article is by Dr. Tanujit Chakraborty,
Associate Professor of Statistics and Data Science
at Sorbonne University Abu Dhabi, UAE. Dr.
Chakraborty discusses how data science engi-
neering is driving progress on the UN’s Sustainable
Development Goals (SDGs). Techniques like
machine learning, forecasting and generative AI
transform raw data into actionable tools in areas
such as public health, economic stability and
urban planning. The case studies he highlights
show how innovative, data-driven solutions are
bridging the gap between theory and real-world
impact, towards the goal of building a sustainable
future.
In this issue’s Academic Perspectives section, the
University of Miami’s (Florida, U.S.) Professor Derin
Ural explores the integration of an AI chatbot as a
teaching assistant in her engineering course. The
Chatbot, “Kay,” was designed to align with course
goals, offering real-time support, personalized
explanations and summaries of complex topics to
students. Dr. Ural reports that students found the
chatbot to be an accessible and valuable tool,
especially those balancing work or non-tradition-
al schedules. She concludes that while it
didn’t-and couldn’t-replace human instruction,
the chatbot did enhance learning and engage-
ment, which demonstrates the potential of AI to
complement traditional teaching methods.
Next in our distinguished Leadership Spotlight
section is Professor Khalid Hussain, Dean of Engi-
neering and Computing at the American Universi-
ty of Ras Al Khaimah (AURAK), UAE. Professor
Khalid discusses his over three-decade career in
academia, beginning in the UK. He discusses in
depth the ways that AURAK-the first institution in
the UAE to offer a bachelor’s degree in AI-is adapt-
ing to effectively train their students in the age of
AI. He also provides many wise insights on exactly
how AI ought to be used in order to maintain the
integrity of core engineering skills that are essen-
tial for all engineers.
Following Professor Khalid, this issue we are
incredibly excited to introduce to you all a new
section of UniNewsletter. We are so privileged to
present an article from By Dr. Waddah S Ghanem Al
Hashmi, Chairman of the OSH Federal Committee
in the UAE and Senior Director in the Energy Sector,
in our Industry Perspectives section. As its title
suggests, we wanted readers of UniNewsletter to
get the opportunity to hear from seasoned profes-
sionals actively working in industry careers. In
context of this issue, Dr. Waddah holds a PhD in
Environmental Engineering from Cardiff University,
Wales, UK, but has gone on to enjoy a hugely
successful career and is considered a global
authority on Governance and Leadership in Health,
Safety and Environment (HSE) and High Reliability
Organizations. Given the diverse path his career
has taken, Dr. Waddah writes a thought-provoking
article about the unique skillsets of engineers and
suggests that they have much to offer beyond the
immediate territory of their discipline.
As I have already relayed, this issue’s Student Voice
contributor is Nour Mostafa Kamel, BSc student in
Computer Engineering at AURAK. Nour writes about
her experience of the AI boom, which occurred
mid-way through her studies, addressing how her
initial skepticism of AI tools has reduced over time.
She writes passionately about the joys and difficul-
ties of manually learning computer coding before
an AI assistant was available to solve the inevitable
errors. Her article reaches an instructive conclusion.
She suggests that universities must seriously
consider avoiding excessive AI exposure to junior
students because they will miss out on the essen-
tial learning experience of encountering frustration
and learning how to problem solve.
Closing this issue in our Trends section is
Anne-Gaelle Colom from the University of West-
minster, London, UK. Anne-Gaelle’s words are
essential reading for software engineers entering
the field. She expertly outlines how AI has not only
changed the skillset required for developers, but
also how the job market has changed-very helpful-
ly highlighting what developers must do in order to
remain competitive in today’s market. Like Nour,
Anne-Gaelle insists on the importance of struggle—
in the learning experience, arguing that without it
students will bypass the development of crucial
critical thinking and analytical skills that are para-
mount to their long-term success.
It has been a wonderful experience to work with this
broad set of engineers and we sincerely hope you
enjoy the interesting insights and words of wisdom
they have to offer.
05
Special Edition |
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Special Topics
enerative AI is a type of
artificial intelligence (AI)
that produces new and
original content that bears
a
striking
resemblance
to
human-created content. Traditional
AI systems focus on predicting or
classifying values or classes. Howev-
er, generative AI tries to produce
content that is suitable for user
requests. The generated content can
be text, images, graphs, audio or
video. In the early 2010s, generative
AI started gaining attention, espe-
cially with the huge development of
deep learning techniques and trans-
former models such as OpenAI and
ChatGPT. It becomes a powerful tool
for creating realistic and engaging
“
“
Generative AI
integration can
be considered as
the next big step
in digital
evolution. It has
become one of the
most promising
and significant
technologies.
Unlocking the Role of
Generative AI in
Engineering Education:
content that can mimic human
creativity.
Generative AI integration can be
considered as the next big step in
digital evolution. It has become one
of the most promising and significant
technologies. Its higher education
applications, especially in the fields
of electrical and computer engineer-
ing, allow for producing new and
original content that closely mirrors
human-created work. This approach
has the potential to transform the
field. It can help in creating an inter-
active and more engaging learning
environment compared to traditional
tools that make education more
participatory. These tools may
| Special Edition
50
An Overview of its Opportunities and Challenges
08 | Special Edition
Dr. Ghalia Nassreddine, Computer and Information
Systems Department, Rafik Hariri University, Lebanon
Dr. Obada Al-Khatib, School of Engineering, University
of Wollongong in Dubai, UAE
Dr. Mohamad Nassereddine, School of Engineering,
University of Wollongong in Dubai, UAE
enhance cognitive processes and
result in better academic perfor-
mance due to its ability to advance
engagement
rates
among
students. Furthermore, generative
AI tools help in offering more
personalized learning experiences,
like intelligent tutoring systems that
adapt to a student’s unique needs
and preferences. These tools assist
in identifying the weaknesses of
each student and focus more on
improving these fragile areas. Many
organizations,
such
as
United
Nations Educational, Scientific and
Cultural Organization (UNESCO),
demonstrate
the
importance
of
personalized learning and support
approaches
that
meet
diverse
student needs. In addition, intelligent
tutoring systems use generative AI to
monitor students’ performance and
provide real-time feedback. This can
allow for the adjusting of learning
techniques to meet individual learn-
ing needs, which enables professors
from electrical and computer engi-
neering to create more interactive
materials to advance the engage-
ment of students and prepare them
for industrial appointments.
Furthermore, generative AI technolo-
gies
can
support
students
with
special
educational
needs.
For
instance, AI-powered speech-to-text
tools, like Microsoft Translator, assist
hearing-impaired
students,
while
other
AI
applications
provide
real-time sign language translation.
Tools like ECHOES use AI to help
children with autism develop social
Dr. Ghalia Nassreddine
Dr. Obada Al-Khatib
Dr. Mohamad Nassereddine
09
Special Edition |
“Generative AI
can help lecturers
in electrical and
computer
engineering to
design courses by
recommending
structures,
prerequisites and
sequencing
based on
engineering
educational goals
and industry
trends.”
communication skills through inter-
active
simulations,
showing
AI’s
capacity to address various educa-
tional challenges. In addition, gener-
ative AI can use virtual and aug-
mented reality to create simula-
tion-based learning environments,
such
as
game-based
learning.
Simulation-based learning is a type
of experiential learning in which
students are required to address
complex
challenges
inside
controlled settings by engaging in
reproduced “real-life scenarios.” This
program
is
better
than
most
video-based classes because it
helps you remember what you have
learned, the scenarios are the same
so that your responses are also the
same and you are taught the exact
methods needed to complete tasks
in a certain industry. For example, as
depicted in the images below,
generative AI could be used to
create virtual laboratories where
students engage with simulations of
renewable energy systems and
focus
on
optimizing
system
outcomes for a hybrid photovoltaic
(PV), wind and grid network. Here we
see students working on designing a
PV system using a virtual lab gener-
ated by AI tool.
In addition, generative AI can help
lecturers in electrical and computer
engineering to design courses by
recommending structures, prerequi-
sites and sequencing based on
engineering educational goals and
industry trends. Generative AI-pow-
ered technologies can help produce
textbooks, lecture notes and interac-
tive models, conserving instructors’
time
and
ensuring
content
relevance. Additionally, generative
AI-driven assessment and feedback
technologies can streamline the
process and reduce faculty work-
load. The following illustration guides
institutions as to how can integrate
generative AI into its systems.
To integrate generative AI into insti-
10 | Special Edition
Student using virtual lab generated by AI tool
To integrate generative
AI into institutional
frameworks, engineering
departments should first
identify applicable areas
such as student support,
administration or
curriculum development.
Objectives should align
with the engineering
program learning
outcomes, with input
from faculty and staff to
address expectations
and concerns.
“
“
tutional
frameworks,
engineering
departments should first identify appli-
cable areas such as student support,
administration or curriculum develop-
ment. Objectives should align with the
engineering
program
learning
outcomes, with input from faculty and
staff
to
address
expectations
and
concerns.
Choosing
appropriate
AI
tools—like
Chatbots
and
AI-driven
content
recommendations—requires
alignment with budget and institutional
goals. Building robust data infrastruc-
tures will ensure compliance with GDPR
and HIPAA, and training sessions for
faculty and staff are crucial for effective
AI tool utilization. Educating students on
AI’s role and benefits can foster partici-
pation and offer valuable feedback on
its impact on learning and administra-
tion.
Despite all the benefits of incorporating
generative AI in higher engineering
education, it may introduce many chal-
lenges as illustrated below:
1.
Data
Provenance:
Generative
AI
systems analyze massive data that
can
be
subject
to
inadequate
governance, dubious origin, uncon-
sented use or bias. Thus, social influ-
encers or the AI systems themselves
can exaggerate errors.
2.
Copyright and Legal Exposure: Large
databases that may be produced by
different and non-clear sources are
1.
used for training generative AI tools.
Thus, generative AI outputs can
violate intellectual property and
produce
legal
and
reputational
threats.
2.
Data Privacy Violations: The dataset
that is used to train Large Language
Models (LLMs) may include person-
ally identifiable information (PII).
Developers must ensure compliance
with privacy laws by excluding or
removing PII.
3.
Sensitive
Information
Disclosure:
Increased accessibility of AI tools
could lead to accidental sharing of
sensitive information such as patient
data or proprietary strategies. Clear
governance, guidelines and com-
munication are necessary to protect
sensitive information and intellectu-
al property.
To address these challenges and foster
responsible AI unitization within engi-
neering education, it is critical to develop
ethical frameworks that prioritize trans-
parency, fairness and accountability.
Additionally, institutions must strengthen
cybersecurity
measures
to
protect
sensitive data among all stakeholders.
1.
3.
4.
2.
11
Special Edition |
Case Studies in Building a Sustainable Future with
Data Science Engineering
Turning Data
Science into Action:
Dr. Tanujit Chakraborty
Associate Professor of Statistics and Data Science
Sorbonne University Abu Dhabi, UAE
Special Topics
12 | Special Edition
he
United
Nations
Sustainable
Develop-
ment Goals (SDGs)—the
2030
agenda—repre-
sent a global blueprint to address
pressing
challenges
like
public
health, poverty, inequality, sustain-
ability and climate action. Achiev-
ing these ambitious goals requires
more than just ideas; it demands
solutions
that
bridge
the
gap
between theory and real-world
implementation. This is where data
science engineering steps in, com-
bining the power of artificial intelli-
gence (AI) and innovative prob-
lem-solving to design practical
tools that make a difference.
By leveraging techniques such as
machine learning, predictive mod-
eling and time series forecasting,
engineers and data scientists can
transform raw data into actionable
insights. However, the journey from
data to action isn’t without its chal-
lenges. Issues such as accessing
reliable data, scaling solutions and
adapting frameworks to complex
real-world conditions remain key
obstacles.
Recent
collabora-
tions—such as the UAE and French
governments’
agreement
to
advance AI—signal the growing
recognition of data science as an
engineering
discipline
with
the
potential to address these hurdles
and drive global progress.
In this article, let’s explore some
real-world examples where data
science engineering has deliv-
ered impactful solutions aligned
with the SDGs, showcasing how
these tools are shaping a more
sustainable future.
Case Study 1: Designing AI Tools
for
Mobile
Health
(mHealth)
Applications
Imagine receiving a motivational
message on your phone encour-
aging you to take a walk or prac-
tice mindfulness. These small
nudges,
powered
by
data
science engineering, are part of
mHealth interventions designed
to improve well-being. With the
growing reliance on mobile tech-
nologies, mHealth applications
play a vital role in reducing
health disparities and advancing
SDG Goal 3: Good Health and
Well-Being.
From an engineering perspec-
tive, designing mHealth tools
involves creating algorithms that
adapt and optimize in real time.
For
instance,
reinforcement
learning (a type of AI) helps
these systems learn which mes-
sages resonate most with users.
In one of our projects, we devel-
oped a hybrid algorithm using
Thompson sampling (a
“The journey from
data to action isn’t
without its
challenges. Issues
such as accessing
reliable data,
scaling solutions
and adapting
frameworks to
complex real-world
conditions remain
key obstacles.”
13
Special Edition |
reinforcement learning method)
and statistical models to improve
the effectiveness of motivational
messages in mHealth apps. This
approach has been applied in the
“Drink Less” app, which supports
users in reducing hazardous alco-
hol
consumption.
The
same
principles can be extended to
mindfulness and physical activity
apps, demonstrating how AI tools
can be engineered to address
diverse health challenges.
Case Study 2: Forecasting Tools
for Economic Growth and
Epidemic Management
Forecasting is a cornerstone of
science and engineering—wheth-
er predicting the trajectory of a
rocket or the rise of consumer
prices. In the context of SDG Goal
8: Decent Work and Economic
Growth, accurate forecasts help
policymakers
design
effective
economic strategies. For example,
we
engineered
an
ensemble
neural network model, FEWNet, to
forecast inflation rates in emerg-
ing economies such as Brazil,
Russia, India and China. By com-
bining econometric principles with
machine learning, FEWNet delivers
precise
predictions
that
aid
central banks in making informed
decisions.
But forecasting isn’t just about
economics. It’s also crucial for
public health. Epidemic mode-
ling, or “epicasting,” uses data
science tools to predict the
spread of diseases like dengue
or influenza. Our team devel-
oped software that incorporates
key disease characteristics to
provide
reliable
forecasts,
enabling timely interventions in
affected regions. These tools
highlight the engineering inge-
nuity required to tackle diverse
challenges,
from
stabilizing
economies to saving lives.
Case Study 3: Generative AI for
Sustainable Cities
Urbanization
is
accelerating,
especially in developing coun-
tries, leading to challenges like
traffic congestion, pollution and
the loss of green spaces. How
can we design cities that are not
only functional but also sustain-
able?
Generative
AI,
a
cutting-edge engineering tool,
can help urban planners visual-
ize and create future cities.
In a recent project aligned with
SDG Goal 11: Sustainable Cities
and Communities, we combined
statistical modeling with gener-
ative AI to predict road network
density in small and medi-
um-sized Indian cities. This work
answers critical questions, such
as: what will our future cities look
like? How can we plan infrastruc-
ture to meet growing demands?
By using spatial indicators and
human mobility data, our frame-
work offers planners actionable
insights for designing efficient
and sustainable road networks.
Similar
techniques
can
be
adapted globally, showcasing
how engineering solutions can
address urban challenges.
These case studies illustrate the
transformative potential of data
science engineering. From health
apps to economic forecasting
and urban planning, these solu-
tions demonstrate how raw data
can be turned into impactful
tools.
But
success
requires
collaboration. Partnerships with
policymakers, international insti-
tutions and research centers like
Sorbonne University Abu Dhabi
ensure that these tools are not
only innovative but also practical
and scalable.
Looking
ahead,
our
ongoing
research focuses on climate
action and air quality monitoring,
addressing SDG Goal 13: Climate
Action. For instance, we are
developing
geometric
deep
learning models to forecast air
pollution levels in cities like Delhi
and Beijing. These tools, com-
bined with engineering princi-
ples, could help mitigate the
effects of smog and create
healthier urban environments.
Achieving the SDGs is a monu-
mental task, but with innovative
data-driven engineering solu-
tions, collaborative efforts and a
commitment
to
sustainability,
the future looks promising. Data
science engineering is more than
a field; it’s a bridge connecting
today’s challenges with tomor-
row’s solutions.
How can we design cities
that are not only functional
but also sustainable?
Generative AI, a
cutting-edge engineering
tool, can help urban
planners visualize and
create future cities.
“
“
14 | Special Edition
15
Special Edition |
“
Academic Perspectives
Teaching an Engineering
Class with a Chatbot as a
Teaching Assistant
Professor Derin Ural
Professor of Practice, Department of Civil and Architectural Engineering,
College of Engineering, University of Miami, Florida, U.S.
| Special Edition
18
Revolutionizing
Pedagogical
Approaches
Through Artificial Intelligence
As an Engineering faculty member who has
adapted to student centered pedagogies,
including flipped and active learning through-
out my three-decade career, I was curious to
pilot the use of an Artificial Intelligence (AI)
chatbot to enhance my students’ learning
experience. I witnessed that the integration of
AI into educational settings is catalyzing
profound changes in how diverse learners
interact with course content and better
engage in the learning process. With the ability
to build course and topic specific chatbots, AI
is increasingly employed to deliver tailored
learning experiences for students. This article
explores the implementation of a chatbot as a
teaching assistant in an engineering course at
the College of Engineering, University of Miami
(UM). By examining its ability to elucidate com-
plex concepts, respond to student inquiries at
any time of the day and enhance engage-
ment, this pilot contributes to the growing
discourse on AI’s role in higher education. The
results, supported by both student feedback
and scholarly research, underscore its trans-
formative potential.
AI Chatbots: A Paradigm Shift in Education
The deployment of AI chatbots represents a
significant shift in educational support meth-
odologies,
driven
by
a
commitment
to
enhance student learning through technologi-
cal innovation. As an engineering faculty
member, there is a fundamental use for the
chatbots to elaborate and explain concepts,
and not to problem-solve. My decision to pilot
a chatbot was based on research such as that
from Tyton Partners underscoring the potential
of AI to improve academic engagement. Con-
currently, insights from Youth Today highlight
Special Edition | 01
the increasing reliance of learners on AI-driven
solutions for academic and informational
needs. As emphasized by The Chronicle of
Higher Education, equipping educators with
the requisite skills to effectively deploy AI tools
is imperative for sustainable success for the
generation of learners relying on AI driven solu-
tions. Participating in professional develop-
ment workshops at the UM, I was able to create,
test and pilot chatbots for my engineering
classes this year. Within engineering educa-
tion, where mastering intricate theoretical and
practical concepts is paramount, I found chat-
bots offer an adaptive, engaging and most
importantly accessible means of addressing
student inquiries on course content. Having
traditional and non-traditional students in the
class also proved that both groups benefitted
from the chatbot, with students working
full-time benefitting the most. The chatbot was
an effective alternative to faculty office hours,
for those working full-time.
Designing and Implementing the Chatbot
Initiative
The chatbot “Kay” employed in my course was
meticulously configured to align with course
topics and learning objectives outlined in the
syllabus. Naming the bot “Kay” was based on a
living thought leader in the subject area, whom
students were able to meet for one session
during the semester. Chatbot Kay’s functional-
ities included answering technical questions,
summarizing
course
content,
comparing
models, giving engineering best practice
examples and retrieving information from prior
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The deployment of AI
chatbots represents a
significant shift in
educational support
methodologies, driven by
a commitment to enhance
student learning through
technological innovation.
interactions to personalize support. From an
instructional perspective, aligning the chatbot’s
outputs with course objectives necessitated
significant initial investment in prompt design
and customization, through iterative questions
and answers, instructing the chatbot to share its
references. After a period of testing the chatbot,
it was ready to pilot with my students. Upon
surprise to see a link to a chatbot on the syllabus,
students were intrigued as they were introduced
to the chatbot as a supplementary tool posi-
tioned to complement, rather than replace,
direct instructional methods.
Key capabilities of the course specific chatbot
included:
•
Articulating detailed explanations of engi-
neering principles.
•
Offering concise recaps of topic highlights.
•
Providing real-time responses to conceptual
inquiries outside scheduled class hours,
which was the most impactful attribute.
Students were encouraged to utilize the chatbot
consistently through assignments that required
them to first work without access to the chatbot,
and then compare their findings to the summary
provided by interacting with the chatbot.
Students were then able to provide feedback to
assess its efficacy. At the beginning of the
semester, students interacted with Kay by
posing one or two questions, through short
conversations. As the semester progressed, their
conversations began to flow naturally, with
seven to nine questions about various course
topics.
Empirical Insights from Student Feedback
A structured survey administered at the end of
the semester for all students revealed compel-
ling trends:
•
Enhanced Learning Efficacy: 67 percent of
student participants strongly agreed and 33
percent agreed that the chatbot facilitated
a deeper understanding of complex materi-
al and bolstered their overall learning expe-
rience. They enjoyed interacting with the
chatbot.
•
Academic Confidence: 67 percent of partici-
pants strongly agreed and 33 percent
agreed that the chatbot positively influ-
enced their development as more capable
and confident students. The chatbot was
trained to have a growth mindset and polite
tone, which was well-received by students.
•
Universal Endorsement: 100 percent of
respondents to the survey advocated for the
continued integration of chatbots in future
iterations of the course.
Students qualitative feedback further illustrated
the chatbot’s impact and potential for future
classes:
•
“It was really helpful ... There was some infor-
mation I kept on forgetting and the chatbot
could always bring back information from
previous sessions. It’s definitely a tool that
can be beneficial for students, not for cheat-
ing or doing assignments for them, but in
assisting them with portions they may not
understand.”
•
“As a full-time working single mother, the
chatbot allowed me to continue my educa-
tion … I was in a situation where I was falling
behind in my coursework, and was thinking
of dropping my courses. The chatbot inter-
actions at late hours was instrumental in my
success. All classes should have a TA chat-
bot!”
These reflections underscore the AI chatbot’s
role in providing targeted and individualized
academic support for the varying student
needs.
67 percent of student
participants strongly
agreed and 33 percent
agreed that the chatbot
facilitated a deeper
understanding of complex
material and bolstered
their overall learning
experience.
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Special Edition | 23
Benefits of AI Integration in Engineering Educa-
tion
The chatbot’s contributions extended beyond
meeting immediate academic needs, delivering
broader pedagogical advantages:
•
Uninterrupted Accessibility: Its availability 24
hours a day and seven days of the week
empowered students to seek clarification
and reinforcement of topics regardless of
the time and their location.
•
Personalized Learning: Leveraging interac-
tion data, the chatbot offered nuanced
guidance tailored to individual learning
trajectories. As a faculty member who devel-
oped the chatbot, having the ability to anon-
ymously see the questions addressed by
students allowed for reinforcement of topics
during class hours.
•
Class
time
Efficiency:
By
addressing
frequently asked questions, it allows faculty
to dedicate more time to advanced discus-
sions and mentorship.
Envisioning the Future of AI-Enhanced Learning
The deployment of a chatbot as a teaching
assistant in an engineering course yielded unex-
pected valuable insights into the potential of AI
to augment traditional pedagogical approach-
es. While not a substitute for the depth of human
instruction, the chatbot proved to be an invalua-
ble
complement,
enhancing
accessibility,
engagement and efficiency. Student feedback
as well as faculty experience in this pilot attests
to the promise of AI chatbots as a transformative
tool in education. As AI technologies continue to
advance,
their
integration
into
academic
settings in engineering education and beyond
offers a compelling avenue for redefining the
contours of teaching and learning in the 21st
century.
As a full-time working single
mother, the chatbot allowed
me to continue my education
… I was in a situation where I
was falling behind in my
coursework, and was thinking
of dropping my courses. The
chatbot interactions at late
hours was instrumental in my
success. All classes should
have a TA chatbot!
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Special Edition |