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

Method.

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.

1 INTRODUCTION

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

https://orcid.org/0000-0002-4851-5711

https://orcid.org/0000-0001-5966-992X

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,

286

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

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 RELATED RESEARCH

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

Education

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

287

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 RESEARCH QUESTIONS AND

METHODS

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

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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.

4 RESULTS

Table 1 displays the project topics selected by the

three groups.

Table 1: Topics the groups selected for their project.

Project

Name

Teams Requirement

Instant

Course

Feedback

R2, R6, R9

(AITU)

• A web application

enabling students to

submit anonymous

feedback after a lecture.

• It must include reporting,

admin tool, and barcode

eneration functionalit

Health

and

Fitness

Tracker

R1, R4, R7

(PAIU)

• A mobile application for

tracking sports activities.

• The application records

daily sports activities,

saves them, and allows

users to make

ueries.

Language

Learning

App

R3, R5, R8

(NAIA)

• 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

tasks.

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

289

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).

5 DISCUSSION

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

engineering.

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

techniques.

Sprint 1

23%

Sprint 2

18%

Sprint 3

23%

Sprint 4

18%

Postponed

18%

Sprint 1

Sprint 2

Sprint 3

Sprint 4

Postponed

Sprint 1

18%

Sprint 2

31%

Sprint 3

39%

Sprint 4

Postponed

Sprint 1

Sprint 2

Sprint 3

Sprint 4

Postponed

Sprint 1

14%

Sprint 2

Sprint 3

13%

Sprint 4

47%

Postponed

23%

Sprint 1

Sprint 2

Sprint 3

Sprint 4

Postponed

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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

discussions.

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.

ACKNOWLEDGEMENTS

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

(NRF-2023S1A5C2A02095195).

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