Examining the Utilization of Artificial Intelligence Tools by Students
in Software Engineering Projects
Amir Dirin
and Teemu H. Laine
2,* b
Metropolia University of Applied Sciences, Helsinki, Finland
Department of Digital Media, Ajou University, Suwon, Republic of Korea
Keywords: Software Engineering, Higher Education, Project, Artificial Intelligence, GitHub Copilot, ChatGPT, Agile
Abstract: With the popularity of AI-based tools, the landscape of learning and teaching software engineering has shifted
to a new era, which has left both educators and students confused regarding the extent to which these tools
are reliable, secure, and, most importantly, result in efficient student competence development. In this study,
we explored how the use of AI tools such as ChatGPT and GitHub Copilot affect the performance of 36
students in nine teams in a software engineering project course. We also explore the perceptions of the
students regarding the use of AI tools in software engineering. We divided the project teams into three groups
based on their use of AI tools: group 1 used AI tools freely, group 2 used AI tools in a restricted manner, and
group 3 did not use any AI tools. The results indicated that while all groups successfully finished their projects,
AI tools were of great help in user story creation and completing a high number of features and tasks. However,
groups 1 and 2 also require time to learn the AI tools and the resulting software quality was lower than that
of group 3. In conclusion, AI tools like Copilot and ChatGPT can become powerful companions to software
engineering students in their educational activities.
The field of computer science is constantly evolving
with the advancement of new technologies. Even for
professionals, it can be a challenge to keep up with
the pace of all the developments. The emergence of
ChatGPT has sparked discussions about its potential
and the opportunities it may bring about
(Taecharungroj, 2023). It has fostered a positive
attitude toward both learning and teaching (Ali et al.,
2023; Rospigliosi, 2023). However, over time its
capabilities and performance have been evaluated by
researchers. For example, Thorp (2023) explored the
writing capabilities of ChatGPT and found that it
produces amusing text. In another study, Gilson et al.
(2023) demonstrated that ChatGPT provides logical
and informational responses for a majority of
questions. Barenkamp et al. (2020) showed that AI
can be effectively applied across various phases of
software engineering to accelerate development
Corresponding author: teemu@ubilife.net
processes cost-effectively. Georgievski (2023)
proposed an AI-based software development
lifecycle for AI-based system architecture, aiming to
replace traditional software engineering
methodologies. Neumann et al. (2023) suggested
practical guidelines for effectively utilizing ChatGPT
in educational settings. Even though ChatGPT has
been in existence for almost two years, uncertainty
persists regarding where artificial intelligence (AI)
development will have the most significant impact.
Engineering fields have been grappling with a
significant concern: whether teaching programming
is still necessary, given that AI tools like ChatGPT
and GitHub Copilot can write programs and devise
diverse solutions to given problems. Even though
software engineering encompasses more than just
coding, the process involves multiple phases to
ultimately deliver a finalized product.
AI tools have a significant impact on software
engineering and other engineering fields,
Dirin, A. and Laine, T.
Examining the Utilization of Artificial Intelligence Tools by Students in Software Engineering Projects.
DOI: 10.5220/0012729400003693
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2, pages 286-293
ISBN: 978-989-758-697-2; ISSN: 2184-5026
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
encompassing various aspects such as project
management, requirements engineering (Dalpiaz &
Niu, 2020), concept design (Verganti et al., 2020),
and implementation. While there are challenges
involved in different phases of software engineering,
it is plausible to address them using AI tools. These
tools have the potential to assist in project risk
analysis, budget calculation, resource allocation, cost
prediction, and change anticipation, among other
tasks. Many commercial AI-based tools support the
management of software projects, such as Asana,
Notion, and Monday.
Undoubtedly, the impact of software engineering
on both industrialization and societal adaptation to
digitalization has been significant (Borg et al., 2018).
With the emergence of AI, this development has now
entered a new phase. The implementation of weak AI
solutions, such as chatbots, has already demonstrated
the potential for transformational change in support
teams and teaching (Chiu et al., 2023). More
recently, the introduction of strong AI solutions, such
as ChatGPT, heralds a new era of development that
promises to take both industries and society to
unprecedented levels (F.-Y. Wang et al., 2023).
Despite the remarkable advancements made in AI,
there remain several unresolved questions that require
further research and development, particularly
regarding the cognitive (Guerrero et al., 2023) and
ethical aspects of AI (Maciel, 2023), such as
ownership of AI (Kim & Song, 2023), decision-
making and trust (Kaplan et al., 2023), data security
(Dirin, et al. 2023) and data integrity (Khan et al.,
2023). While these issues are directly related to the
software development aspect of AI, this research plan
will focus primarily on the software engineering
discipline perspective rather than the philosophical,
security, or user experience perspectives. One of the
primary rationales behind prioritizing software
processes and solutions over AI is that while AI is
seen as an enabler, it is the software engineering
process that plays a pivotal role in tackling the
challenges facing both industries and society.
This paper aims to evaluate the impacts of AI
tools, specifically ChatGPT and GitHub Copilot, on
outcomes, student motivation, and perceived
potential in software engineering student projects.
2.1 Artificial Intelligence
The field of AI was established by John McCarthy
and other colleagues at the Dartmouth Conference in
1956 (Rajaraman, 2014).
They defined AI as the
simulation of human-like intelligence, including
learning and related features, by machines. The
related features are reasoning, problem-solving,
perception, and understanding. Therefore, machines
equipped with capabilities such as visual perception
(Esteva et al., 2021), speech recognition (Jung et al.,
2020), decision-making (Leyer & Schneider, 2021),
and language transactions (Mieczkowski et al., 2021)
are considered to be under the umbrella of AI.
2.2 AI-Based Tools in Education
Education institutes and educational strategists
continuously pursue to improve the effectiveness of
teaching and learning by embracing novel
technologies and methodologies, such as AI-based
applications and tools. Among these activities, the
personalization of learning (Dirin & Laine, 2018; Tan
et al., 2023) has received significant attention.
Intelligent tutoring systems have been designed and
developed to provide customized instructions,
tutoring, and feedback to students to improve their
learning experience. Recently, efforts have been
made to facilitate learning and teaching with AI. For
example, AI has been employed for automated
grading of short answers (Süzen et al., 2020). Further
attempts to personalize student learning through the
adaptation of the learning environments have been
ongoing for some time (How & Hung, 2019;
Walkington, 2013). Moreover, AI has enabled the
expansion of learning and teaching beyond
educational environments, as seen in language
learning platforms (Rebolledo Font De La Vall &
González Araya, 2023). Furthermore, educational
content creation has been improved through the
advancement of generative AI tools, natural language
processing, and machine learning algorithms (Du et
al., 2023).
2.3 AI Tools in Software Engineering
AI has already made an impact in almost every
domain of contemporary life, and the field of software
engineering is no exception. Daun and Brings (2023)
have recommended that it is essential to adapt
teaching software engineering with the latest AI
development, specifically by providing guidelines to
students on the extent to which they need to utilize
AI. ChatGPT has already been utilized for various
purposes in software engineering courses, such as in
the system analysis course (Albonico & Varela, 2023)
where ChatGPT is used to answer student inquiries.
Examining the Utilization of Artificial Intelligence Tools by Students in Software Engineering Projects
Furthermore, as articulated by Ozkaya (2023), AI is
being applied to various tasks in software
engineering, including code generation as
demonstrated by tools like Copilot by GitHub.
Puryear and Sprint (2022) demonstrated that Copilot
generates unique code for introductory assignments
with code accuracy scores ranging from 68% to 95%
which means that the code Copilot generates is
largely aligned with human expectations.
3.1 Research Questions
We pursue to answer the following research questions
in this study:
1. How does using AI tools affect the
implemented functions in software
engineering student projects?
2. How does using AI tools affect the completed
tasks per sprint in software engineering
student projects?
3. How do software engineering students
perceive using AI tools in school projects?
3.2 Research Method
A mixed-method approach was utilized, including the
use of a questionnaire, a brief discussion with the
students during project presentations, and an analysis
of the project implementation and performance. The
mixed-method approach was chosen to acquire
diverse data from different viewpoints, which would
provide a holistic understanding of the implications
of using or not using AI tools in student projects. The
brief discussion aimed to understand the students'
perceptions of the use of AI in software development.
This information complements the questionnaire data
that gathers the students' insights about how AI
helped or did not help them in their project
implementation. This discussion was more like an
exchange of ideas than a semi-structured interview.
Therefore, the mixed-method approach enabled us to
assess the project implementation from various
perspectives, including the developer perspective,
project outcomes, and project management. The
students were required to assess their contributions to
the project at each sprint, as well as reflect on how the
project impacted their competency development.
A total of 36 students (27 males, 9 females, age
range: 20-35) of a software engineering course were
divided into nine teams of four students. Students
were in their fourth semester and had completed full-
stack development courses at the university. The
students already knew each other and formed the
teams based on their preferences. The only
requirement enforced was the size of the team, which
was four people.
We assigned the teams into three groups. In the
first group (AITU), the teams were allowed to use
ChatGPT and Copilot in development. The second
group (PAIU) was allowed to use AI tools but they
first had to receive approval from the first author
(instructor). Lastly, the third group (NAIA) was
tasked with developing their software solely based on
their knowledge and skills without using any AI tools.
During the sprint review meetings, each group shared
their implementation status and applied tools with the
first author. Additionally, in the sprint planning
meeting, the first author, along with the group,
planned the tasks and technology used for the
upcoming sprint. This approach enabled the first
author to closely monitor both the technology utilized
and the progress of implementation.
All teams received the same high-level
requirements for software to be developed over eight
weeks consisting of four two-week sprints. The teams
selected project topics, after which the first author
presented the three AI usage groups (AITU, PAIU,
and NAIA) to the teams. The teams then selected the
AI usage group they wished to belong to.
The teams were required to adhere to the Scrum
methodology, where the first author was the product
owner, and use project management tools (e.g.
Trello). Additionally, they learned about
dependencies and continuous integration and
development. As a result, the difficulties of the
projects were primarily dependent on the
implementation technologies they freely chose. All
projects mandated students to apply three-tier
architectural solutions.
The students in a team took turns acting as the
scrum master, overseeing the project's progress. At
the end of each sprint, the first author conducted a
sprint review with the teams. Before the software
project implementation, the product owner delineated
the technical, functional, and non-functional
requirements of the projects.
While the selection of implementation technology
was free, the teams opted to utilize technologies with
which they were most familiar. However, they were
required to demonstrate proficiency in unit testing
and incorporate various dependencies through Maven
into their codebase. Code was expected to be
systematically cleaned and refactored to ensure
CSEDU 2024 - 16th International Conference on Computer Supported Education
maintainability. Automation tools like Jenkins were
employed for continuous integration. Additionally,
GitHub was used for version control, while Trello
was utilized for progress tracking. During the first
week of Sprint 1, the focus was on conducting a
feasibility study, architectural planning, and database
design, utilizing UML and Entity Relationship
diagrams. Additionally, Figma was employed for
conceptual design.
In the assessment of the results, our main focus
was on the number of task implementations, code
cleanliness, and the applied refactoring approach,
including the number of features implemented.
However, we did not evaluate the technical
performance of the resulting solutions or the
robustness of the algorithms in this study.
Table 1 displays the project topics selected by the
three groups.
Table 1: Topics the groups selected for their project.
Teams Requirement
R2, R6, R9
A web application
enabling students to
submit anonymous
feedback after a lecture.
It must include reporting,
admin tool, and barcode
eneration functionalit
R1, R4, R7
A mobile application for
tracking sports activities.
The application records
daily sports activities,
saves them, and allows
users to make
R3, R5, R8
A game-based web
application for Finnish
language learning.
It enables peers to
compete, tracks their
progress, and generates
reports on the results.
Our analysis of the project presentation and
documentation outcomes indicate that NAIA tended
to exert extensive effort to ensure a high-quality
product. For example, they conducted unit tests for all
functions, applied Jenkins, and more easily
implemented localization. Our analysis of the
resulting software revealed significant differences in
the number of implemented features. The AITU and
PAIU teams implemented almost all or nearly all of
the planned features during the development sprints,
but the NAIA teams postponed more than 30% of the
features to the next sprints. However, all teams
applied the test template and performed the required
At the end of eight weeks, all groups produced a
functional product. However, it was clear that the
groups' applications varied in performance, the
number of tasks in the product backlog implemented
in each sprint, and the features included. Based on the
project’s nature, each group distributed differently the
tasks to the sprints. Sprint one focused on the
feasibility study and technology setup for all groups.
However, for those who used AI (AITU and PAIU),
most of the effort in sprint 1 was dedicated to learning
the AI tools. Students at AITU expressed a strong
belief in the significant impact of AI on their project
success. Only 25% of them believed to some extent
that AI aided in accomplishing tasks. However, 13%
of students held a belief that AI had no impact on the
successful implementation of tasks. Moreover, 88%
of the students at AITU indicated that there is still a
deficiency in AI-based development tools for
devising robust solutions. Conversely, 12% of the
students in the AITU suggested that there are already
sufficient AI-based tools to support complex software
engineering projects.
Interestingly, both AITU and PAIU consulted
ChatGPT in Sprint 1 to develop user stories after
defining tasks in the product backlog.
The answers by PAIU were interesting as 70 % of
the students in this group believed that AI to some
extent helped them complete more tasks. However,
18% of them responded that AI had no impact at all
on the implementation of more tasks in their project.
Almost 6% of those group members who replied
believed that AI helped to implement more tasks.
In NAIA, almost 19% believed that AI has no
impact on implementing more tasks in software
projects. Only 81% believed that AI may impact to
implementation of more tasks to some extent.
Figure 1 presents the proportions of completed
tasks in each sprint by the AITU teams. The
proportions of completed tasks in each sprint are
almost homogeneous. Despite using AI tools such as
Copilot and ChatGPT, the teams in AITU completed
a significant proportion of the tasks and user stories
in sprint 1 (23%), as they learned how to use the AI
tools. Furthermore, the teams in this group completed
a similar proportion of tasks in sprint 3, which
covered database development and merging different
modules to work. The teams in this group postponed
18% of the tasks for the next product iterations.
Examining the Utilization of Artificial Intelligence Tools by Students in Software Engineering Projects
Figure 1: The task completion proportions for each sprint
of AITU (free AI use).
Figure 2 depicts the outcomes of each sprint for
the teams in the PAIU group. The proportions of
completed tasks across the sprints indicate that the
majority of task implementations occurred during
sprints 2 and 3, a notable deviation from AITU. In
sprint 1, tasks amounting to 18% were allocated to
design and planning. Furthermore, only 6% of the
tasks were deferred to subsequent iterations of
product implementation.
Figure 2: The task completion proportions for each sprint
of PAIU (limited AI use).
Figure 3 displays the task completion proportions
in each sprint for NAIA. A majority of the tasks
(47%) were completed in sprint 4, signalling a delay
in the schedule, as the goal was to achieve an almost
functional product by the conclusion of sprint 3.
Furthermore, they completed more tasks in designing,
defining the product visions, and articulating the user
stories in Sprint 1 compared to the other two groups.
During Sprint 1, they dedicated time to technology
selection and design of the database. Unlike the other
two groups, the product owner in the scrum meetings
that this group had to allocate time and effort to
investigate technologies and dependencies. NAIA
had to allocate time to writing code, conducting unit
tests, and implementing dependencies, resulting in
the completion of only 3% of tasks in Sprint 2.
Furthermore, NAIA had the highest proportion of
tasks (23%) that were postponed to subsequent
iterations of product development.
Figure 3: The task completion proportions for each sprint
of NAIA (no AI use).
In this paper, we aimed to explore the effects of the
utilization of AI tools by higher education students in
software engineering projects. Specifically, we
sought to evaluate the quality of the resulting
software, the number of features implemented, and
the impact of AI on task implementation and user
stories in Agile methodology. We also explored the
students’ perceptions of using AI tools in software
5.1 Answering to Research Questions
By answering the three research questions, we aimed
to uncover the impacts of AI tools on students
software projects and their insights on the use of those
tools. AI methods and technologies help in general
engineering assignments. Already in 1986, Goldberg
(1986) demonstrated that AI can construct efficient
software. More recently, Salehi (2018) showed that
AI can assist in solving complex engineering
problems. This is aligned with our study results as
students in groups AITU and PAIU successfully
created software with the help of AI tools. Our results
demonstrate that AI facilitated the implementation of
the tasks and features that were initially defined at the
product backlog level. What we identified is that the
resulting AI-facilitated software quality was not at the
same level as that of a traditional software
development approach. This fact was also highlighted
by Cernau et al. (2022) (who recommended a
pedagogical approach that checks the quality of the
source code developed by students using AI
Sprint 1
Sprint 2
Sprint 3
Sprint 4
Sprint 1
Sprint 2
Sprint 3
Sprint 4
Sprint 1
Sprint 2
Sprint 3
Sprint 4
Sprint 1
Sprint 2
Sprint 3
Sprint 4
Sprint 1
Sprint 2
Sprint 3
Sprint 4
Sprint 1
Sprint 2
Sprint 3
Sprint 4
CSEDU 2024 - 16th International Conference on Computer Supported Education
AI has also been applied to software requirements
engineering for analysing requirements to create
high-quality software as indicated by Dapliaz and Niu
(2020). In our experiment, AI-facilitated teams
implemented more tasks than those who did not use
AI tools. On the other hand, Wang and Xu (2021),
recommended that autonomous software requirement
specifications need to be used for AI programming.
In general, AI-based tools like GitHub Copilot
and ChatGPT can become powerful companions to
software engineering students in their educational
activities. Besides getting help for generating code,
debugging, or generating test cases, ChatGPT was
applied by the participants to come up with tasks and
user stories for their product backlog in Agile
development. Barke et al. (2022) categorized the uses
of Copilot in two ways by students: (i) acceleration to
complete the tasks, and (ii) exploring alternative
options they have for the solution. Pearce et al. (2022)
showed that the code generated by GitHub Copilot
has almost 40 percent higher risk of having
cybersecurity issues. Moreover, Ziegler et al. (2022)
showed that those who apply for Copilot focus more
on the immediate action rather than the long-term
impact of the code. This was also visible in our study:
those who applied AI focused on the completion of
the tasks that had been planned for each sprint rather
than paying attention to the quality of the code.
Therefore, the AI-facilitated teams implemented
more tasks than those who wrote the code by hand
which is aligned with the findings of Imai (2022).
Furthermore, the use of ChatGPT to articulate
user stories for product backlog is yet another way the
students leveraged AI in their projects. From a
product owner perspective, it was clear that AI-
facilitated teams generated more innovative user
stories compared to those who did not utilize AI tools.
The impact of AI and non-AI software product
development, as determined through comparisons of
various development team setups, has yet to be
investigated. This aspect warrants further analysis
and exploration in future research.
5.2 Validity of the Results
The results are valid within the context of our study.
They are supported by the setup of our research,
which incorporates the competence level of the
students (fourth semester) and the capabilities and
features of ChatGPT and Copilot utilized during the
study. However, for broader applicability, further
investigation and evaluation employing a more
systematic approach would be beneficial. Quality
assurance and technical performance measurements
could have enhanced the study, but due to time
constraints, they were not implemented.
Nevertheless, we maintain that our results hold
validity within the scope of our study, as we
employed multiple approaches to assess task
performance, application features, and developer
5.3 Implications and Future Work
This study revealed that students view AI tools like
ChatGPT and Copilot as enhanced learning support
mediums. As educators, we emphasize the
importance of quality assurance and stress the need
for thorough consideration of the software's quality
resulting from the use of AI tools. The development
of software applications with the help of AI tools
requires a redefinition of software quality assurance,
standards, and methods. We aim to extend the study
to gather more systematic data, with a particular focus
on the quality assurance of resultant applications.
The contribution of the second author was supported
by the Ministry of Education of the Republic of Korea
and the National Research Foundation of Korea
Albonico, M., & Varela, P. J. (2023). A Report on the Use
of ChatGPT in Software Engineering and Systems
Analysis Courses. Proceedings of the XXXVII
Brazilian Symposium on Software Engineering, 303–
311. https://doi.org/10.1145/3613372.3614189
Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., &
Mohammed, A. A. Q. (2023). Impact of ChatGPT on
Learning Motivation: Teachers and Students’ Voices.
Journal of English Studies in Arabia Felix, 2(1), 41–49.
Barenkamp, M., Rebstadt, J., & Thomas, O. (2020).
Applications of AI in classical software engineering. AI
Perspectives, 2(1), 1. https://doi.org/10.1186/s42467-
Barke, S., James, M. B., & Polikarpova, N. (2022).
Grounded Copilot: How Programmers Interact with
Code-Generating Models. https://doi.org/10.48550/AR
Borg, M., Olsson, T., Franke, U., & Assar, S. (2018).
Digitalization of swedish government agencies: A
perspective through the lens of a software development
census. Proceedings of the 40th International
Conference on Software Engineering: Software
Examining the Utilization of Artificial Intelligence Tools by Students in Software Engineering Projects
Engineering in Society, 37–46. https://doi.org/10.11
Cernau, L. D., Dioşan, L. S., & Șerban, C. (2022). A
pedagogical approach in interleaving software quality
concerns at an artificial intelligence course.
Proceedings of the 4th International Workshop on
Education through Advanced Software Engineering
and Artificial Intelligence, 18–24. https://doi.org/10.11
Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov,
M. (2023). Teacher support and student motivation to
learn with Artificial Intelligence (AI) based chatbot.
Interactive Learning Environments, 1–17.
Dalpiaz, F., & Niu, N. (2020). Requirements Engineering
in the Days of Artificial Intelligence. IEEE Software,
37(4), 7–10. https://doi.org/10.1109/MS.2020.2986047
Daun, M., & Brings, J. (2023). How ChatGPT Will Change
Software Engineering Education. Proceedings of the
2023 Conference on Innovation and Technology in
Computer Science Education V. 1, 110–116.
Dirin, A., & Laine, T. H. (2018). Towards an Adaptive
Study Management Platform: Freedom Through
Personalization: Proceedings of the 10th International
Conference on Computer Supported Education, 432–
439. https://doi.org/10.5220/0006788104a0439
Dirin, A., Oliver, I., & Laine, T. H. (2023). A Security
Framework for Increasing Data and Device Integrity in
Internet of Things Systems. Sensors, 23(17), 7532.
Dixon-Román, E., Nichols, T. P., & Nyame-Mensah, A.
(2020). The racializing forces of/in AI educational
technologies. Learning, Media and Technology, 45(3),
Du, H., Li, Z., Niyato, D., Kang, J., Xiong, Z., Xuemin,
Shen, & Kim, D. I. (2023). Enabling AI-Generated
Content (AIGC) Services in Wireless Edge Networks.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A.,
Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R.
(2021). Deep learning-enabled medical computer
vision. Npj Digital Medicine, 4(1), 5.
Georgievski, I. (2023). Conceptualising Software
Development Lifecycle for Engineering AI Planning
Systems. 2023 IEEE/ACM 2nd International
Conference on AI Engineering – Software Engineering
for AI (CAIN), 88–89. https://doi.org/10.1109/
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi,
L., Taylor, R. A., & Chartash, D. (2023). How Does
ChatGPT Perform on the United States Medical
Licensing Examination? The Implications of Large
Language Models for Medical Education and
Knowledge Assessment. JMIR Medical Education, 9,
e45312. https://doi.org/10.2196/45312
Goldberg, A. T. (1986). Knowledge-based programming: A
survey of program design and construction techniques.
IEEE Transactions on Software Engineering, SE-12(7),
752–768. https://doi.org/10.1109/TSE.1986.6312977
Guerrero, L. E., Castillo, L. F., Arango-López, J., &
Moreira, F. (2023). A systematic review of integrated
information theory: A perspective from artificial
intelligence and the cognitive sciences. Neural
Computing and Applications. https://doi.org/10.1007/
How, M.-L., & Hung, W. L. D. (2019). Educational
Stakeholders’ Independent Evaluation of an Artificial
Intelligence-Enabled Adaptive Learning System Using
Bayesian Network Predictive Simulations. Education
Sciences, 9(2), 110. https://doi.org/10.3390/educsci902
Imai, S. (2022). Is GitHub copilot a substitute for human
pair-programming?: An empirical study. Proceedings
of the ACM/IEEE 44th International Conference on
Software Engineering: Companion Proceedings, 319–
321. https://doi.org/10.1145/3510454.3522684
Jung, Y. H., Hong, S. K., Wang, H. S., Han, J. H., Pham, T.
X., Park, H., Kim, J., Kang, S., Yoo, C. D., & Lee, K.
J. (2020). Flexible Piezoelectric Acoustic Sensors and
Machine Learning for Speech Processing. Advanced
Materials, 32(35), 1904020. https://doi.org/10.1002/
Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A.
(2023). Trust in Artificial Intelligence: Meta-Analytic
Findings. Human Factors: The Journal of the Human
Factors and Ergonomics Society, 65(2), 337–359.
Khan, A. A., Laghari, A. A., Li, P., Dootio, M. A., & Karim,
S. (2023). The collaborative role of blockchain,
artificial intelligence, and industrial internet of things in
digitalization of small and medium-size enterprises.
Scientific Reports, 13(1), 1656. https://doi.org/10.10
Kim, T., & Song, H. (2023). Communicating the
Limitations of AI: The Effect of Message Framing and
Ownership on Trust in Artificial Intelligence.
International Journal of Human–Computer Interaction,
39(4), 790–800. https://doi.org/10.1080/10447318.20
Leyer, M., & Schneider, S. (2021). Decision augmentation
and automation with artificial intelligence: Threat or
opportunity for managers? Business Horizons, 64(5),
711–724. https://doi.org/10.1016/j.bushor.2021.02.026
Maciel, L. (2023). Editorial: ChatGPT and the ethical
aspects of artificial intelligence. Revista de Gestão,
30(2), 110–112. https://doi.org/10.1108/REGE-04-
Mieczkowski, H., Hancock, J. T., Naaman, M., Jung, M., &
Hohenstein, J. (2021). AI-Mediated Communication:
Language Use and Interpersonal Effects in a Referential
Communication Task. Proceedings of the ACM on
Human-Computer Interaction, 5(CSCW1), 1–14.
Neumann, M., Rauschenberger, M., & Schön, E.-M.
(2023). “We Need To Talk About ChatGPT”: The
Future of AI and Higher Education [Application/pdf].
163 KB. https://doi.org/10.25968/OPUS-2467
CSEDU 2024 - 16th International Conference on Computer Supported Education
Ozkaya, I. (2023). Application of Large Language Models
to Software Engineering Tasks: Opportunities, Risks,
and Implications. IEEE Software, 40(3), 4–8.
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri,
R. (2022). Asleep at the Keyboard? Assessing the
Security of GitHub Copilot’s Code Contributions. 2022
IEEE Symposium on Security and Privacy (SP), 754–
768. https://doi.org/10.1109/SP46214.2022.9833571
Puryear, Ben; Sprint, Gina. (2022). Github copilot in the
classroom: Learning to code with AI assistance. Journal
of Computing Sciences in College, 38(1), 27–47.
Rajaraman, V. (2014). John McCarthy—Father of artificial
intelligence. Resonance, 19(3), 198–207.
Rebolledo Font De La Vall, R., & González Araya, F.
(2023). Exploring the Benefits and Challenges of AI-
Language Learning Tools. International Journal of
Social Sciences and Humanities Invention, 10(01),
7569–7576. https://doi.org/10.18535/ijsshi/v10i01.02
Rospigliosi, P. Asher.’ (2023). Artificial intelligence in
teaching and learning: What questions should we ask of
ChatGPT? Interactive Learning Environments, 31(1),
1–3. https://doi.org/10.1080/10494820.2023.2180191
Salehi, H., & Burgueño, R. (2018). Emerging artificial
intelligence methods in structural engineering.
Engineering Structures, 171, 170–189. https://doi.org/
Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M.
(2020). Automatic short answer grading and feedback
using text mining methods. Procedia Computer
Science, 169, 726–743. https://doi.org/10.1016/
Taecharungroj, V. (2023). “What Can ChatGPT Do?”
Analyzing Early Reactions to the Innovative AI
Chatbot on Twitter. Big Data and Cognitive
Computing, 7(1), 35. https://doi.org/10.3390/bdcc701
Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2023). Towards
Personalized Federated Learning. IEEE Transactions
on Neural Networks and Learning Systems, 34(12),
Thorp, H. H. (2023). ChatGPT is fun, but not an author.
Science, 379(6630), 313–313. https://doi.org/10.1126/
Verganti, R., Vendraminelli, L., & Iansiti, M. (2020).
Innovation and Design in the Age of Artificial
Intelligence. Journal of Product Innovation
Management, 37(3), 212–227. https://doi.org/10.1111/
Walkington, C. A. (2013). Using adaptive learning
technologies to personalize instruction to student
interests: The impact of relevant contexts on
performance and learning outcomes. Journal of
Educational Psychology, 105(4), 932–945.
Wang, F.-Y., Yang, J., Wang, X., Li, J., & Han, Q.-L.
(2023). Chat with ChatGPT on Industry 5.0: Learning
and Decision-Making for Intelligent Industries.
IEEE/CAA Journal of Automatica Sinica, 10(4), 831–
834. https://doi.org/10.1109/JAS.2023.123552
Wang, Y., & Xu, J. Y. (2021). Autonomous Software
Requirement Specifications towards AI Programming.
2021 IEEE 20th International Conference on Cognitive
Informatics & Cognitive Computing (ICCI*CC), 123–
Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin,
D., Simister, S., Sittampalam, G., & Aftandilian, E.
(2022). Productivity assessment of neural code
completion. Proceedings of the 6th ACM SIGPLAN
International Symposium on Machine Programming,
21–29. https://doi.org/10.1145/3520312.3534864
Examining the Utilization of Artificial Intelligence Tools by Students in Software Engineering Projects