INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 847
www.rsisinternational.org


Aduo Frank*, Frank Blessed Amenyeke, Rockson Ofori Amanfo, Charles Wussah
Department of Integrated Science Education, University of Education, Winneba.
DOI: https://doi.org/10.51584/IJRIAS.2025.1010000068
28042025
ABSTRACT
This study investigated the impact of generative AI usage on academic engagement among students at a selected
College of Education in Ghana. The study aimed to examine the kinds of generative AI tools, identify the
different ways students utilize these GenAI in their learning, and assess the overall influence on their academic
engagement by employing a sequential explanatory mixed-methods design. This design was chosen to provide
a comprehensive understanding through both quantitative and qualitative data.
Ninety-four students participated in the quantitative phase via purposive sampling and completed a survey
examining the types of generative AI tools they use and the effects on their academic engagement. Additionally,
twelve students were interviewed to gather in-depth qualitative insights that could not be captured by the survey.
Findings
Revealed that generative AI positively influences students’ academic engagement and improves their learning
environment. It serves as an effective tool to enhance learning and engagement. However, findings from some
respondents via qualitative interview reveal that, excessive reliance on generative AI also poses risks by
encouraging laziness and overdependence, less creativity and immersive engagement due to easy access to the
AI tools, which may affect academic integrity.
The implication for this study is that generative AI tools like ChatGPT spark curiosity by offering instant
feedback, tailored learning journeys, and interactive experiences that turn complex concepts into manageable
insights.
The study highlights generative AI as a double-edged tool: while it empowers students with efficiency, creativity,
and deeper engagement, it also risks encouraging shortcuts, dependency, and ethical breaches. Ensuring
responsible and ethical integration of AI is therefore vital, with academic integrity anchored in fairness, honesty,
and originality remaining at the heart of scholarly practice.
Keywords: Academic Engagement, Enhanced Learning, Generative AI, Laziness, Assistive learning
INTRODUCTION
Background
The use of Artificial Intelligence (AI) in education has attracted considerable attention in recent years, with
researchers highlighting its transformative effects on teaching and learning (Okoye& Mante, 2024). AI-powered
educational tools have been shown to increase student engagement, support personalized learning, and improve
performance assessment (Okoye& Mante, 2024). Generative AI (GenAI), a branch of artificial intelligence that
focuses on machine-generated content, has significant potential for delivering personalized and context-aware
learning experiences, particularly in out-of-classroom settings (Nagender & Patil, 2017).
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 848
www.rsisinternational.org
Generative AI applies transformer-based deep learning architectures to produce novel content such as text,
images, and audio, exemplified by tools like ChatGPT, Microsoft Copilot, and Canva (Lim et al., 2023). This
technology enhances learning by providing immediate feedback, personalized learning pathways, and real-time
practice, making the learning process more interactive and engaging (Ai, 2017). Moreover, GenAI systems are
capable of producing human-like content across different modalities, from text and music to computer code,
through deep learning and pattern recognition techniques (Baidoo-Anu & Owusu Ansah, 2023; Farrelly & Baker,
2023; Halaweh, 2023)
Globally, generative AI tools have been adopted in higher education, reshaping learning behaviors. Students
perceive GenAI as an accessible tutor, available anytime, while institutions continue to grapple with challenges
such as academic dishonesty (Liu et. al., 2024; Oravec, 2023). In some contexts, authorities have even banned
ChatGPT, citing fears of plagiarism and reduced critical thinking (Estrellado, 2023; Johnson, 2023). Meanwhile,
other studies have revealed the benefits of responsible use, such as improved writing, learning engagement, and
personalized learning opportunities (Bulawan et.al., 2023; Lim et.al., 2023; Sok & Heng, 2023).
Statement of the Problem
AI has been demonstrated in numerous earlier researches to impact college teaching and learning significantly.
Despite its growing influence, the use of Generative AI in education remains a double-edged sword (Hagendorff,
2020). While it enhances efficiency, engagement, and creativity, it also poses risks of overdependence,
plagiarism, and reduced student initiative (Halaweh, 2023; Rodrigues et al., 2024). Within Ghana, particularly
in Colleges of Education, students increasingly rely on tools like ChatGPT, Quillbot, and Grammarly for
academic work (Bulawan et al., 2023). However, there is limited empirical evidence on how these tools affect
student engagement in this context. The combination of student engagement and teacher readiness will drive
more innovative and relevant Education in the digital age (Sanusi, Ayanwale, & Chiu, 2024).
This research seeks to address this gap by exploring the kinds of Generative AI tools students use, the ways they
integrate them into their studies, and their overall effect on academic engagement. Understanding this dynamic
is crucial, as it informs educators, policymakers, and curriculum designers on how to integrate AI responsibly
while safeguarding academic integrity and promoting student-centered learning.
Rationale for the Study
The rapid integration of generative artificial intelligence (AI) into education has transformed the ways students
access information, complete assignments, and engage with learning tasks. In the Ghanaian context, particularly
within Colleges of Education, students are increasingly exposed to AI tools such as ChatGPT, Quillbot, and
Grammarly. While these tools promise enhanced efficiency, personalized learning, and academic engagement,
concerns also arise about overdependence, plagiarism, and reduced critical thinking. Despite the global discourse
on generative AI, limited empirical research exists within Ghana’s Colleges of Education regarding how students
utilize these tools and their impact on academic engagement. This study therefore provides timely insights into
both the opportunities and risks of generative AI in higher education. The findings will inform educators,
policymakers, and curriculum designers on strategies for integrating AI responsibly while safeguarding
academic integrity and sustaining student-centered learning.
Objectives of the Study
The objective of this research specifically aims to:
1. examine the kinds of Generative AI tools employed by students in the selected College of Education.
2. identify the different ways students utilize Generative AI in their learning.
3. assess the effect of Generative AI use on students’ academic engagement.
Theoretical Framework
The theoretical framework for this research is grounded in constructivist learning theory and the technology
acceptance model with engagement theory.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 849
www.rsisinternational.org
The Constructivist Learning Theory
The theoretical framework for this research is grounded in Constructivist Learning Theory, particularly as
articulated by theorists such as Jean Piaget and Lev Vygotsky. Constructivism posits that learners construct
knowledge through their experiences and interactions with the world, emphasizing the active engagement of
students in the learning process (Piaget, 1973; Vygotsky, 1978). In the context of this study, AI tools can be
viewed as facilitators of constructivist learning, enabling personalized learning experiences that align with
individual students’ needs, preferences, and prior knowledge. By utilizing adaptive learning systems and AI-
driven feedback mechanisms, students engage in self-directed learning, thereby constructing their understanding
through exploration and inquiry. The choice of Constructivist Learning Theory as the theoretical foundation is
particularly relevant for several reasons. First, as AI technologies evolve, they increasingly support
individualized learning pathways that are central to constructivist principles. For example, platforms such as
Smart Sparrow and DreamBox Learning apply adaptive learning algorithms that adjust content and assessments
based on real-time analytics of student performance (Lai et al., 2020). This adaptability cultivates a learner-
centered environment where students can progress at their own pace, explore topics of interest, and receive
immediate feedback, ultimately fostering deeper understanding and retention of knowledge. Additionally,
Vygotsky’s concept of the Zone of Proximal Development (ZPD) emphasizes the importance of social
interactions in learning, suggesting that students benefit from collaboration and guidance from teachers or more
knowledgeable peers (Vygotsky, 1978). AI can enhance collaborative learning experiences by providing tools
that facilitate peer interaction and feedback. Collaborative platforms like Google Classroom or AI-based peer
review systems allow students to engage in group projects and discussions that enhance social learning. Such
applications underscore AI’s role in creating supportive educational ecosystems that align with constructivist
principles, making this theoretical framework a fitting choice for this research.
The Technology Acceptance Model
Even the best learning applications cannot promote learning if the learners use it minimally or fail to utilize all
its helpful features. A theory frequently employed to explain how new software or information technologies are
adopted by learners is the Technology Acceptance Model (TAM; Davis et al., 1989, Venkatesh and Davis, 1996).
The TAM was developed to explain and predict how users accept and implement new technological tools. Over
the years, TAM has been frequently studied and extended (for an overview, see Chuttur, 2009; Yousafzai et al.,
2007a). It focuses on four central constructs: Perceived Ease of Use, Perceived Usefulness, Intention to Use, and
Actual System Use. Perceived Ease of Use evaluates whether potential users perceive the technology as easy to
operate. Technologies considered easy to use are more likely to be adopted, as they reduce the effort required
for learning and using the system (Venkatesh and Davis, 2000). Perceived Usefulness refers to the belief that
the technology will improve one’s performance (Opoku and Enu-Kwesi, 2019). Intention to Use describes the
extent to which a person has the behavioral Intention to Use the technology. Actual System Use is the actual
behavior of users, indicating how often and to what extent the technology is used. According to the extended
TAM (Venkatesh and Davis, 1996), both Perceived Ease of Use and Perceived Usefulness directly influence
Intention to Use, which in turn is a strong predictor of Actual System Use. External factors such as system
experience, educational level, digital Self-Efficacy and age can influence Perceived Ease of Use and Perceived
Usefulness. However, there is no consensus on these external factors, as different studies have identified varying
influencing variables (Chuttur, 2009; Yousafzai et al., 2007a). Davis et al. (1989) observed that the influence of
Perceived Ease of Use on behavioral Intention to Use tends to diminish as users become more familiar with a
technology. Further research supports this, showing that the effect is more pronounced in the early stages of
technology adoption, but becomes less evident over time (Adams et al., 1992; Chau, 1996; Gefen and Straub,
2000; Igbaria et al., 1996).
In this study, perceived usefulness refers to students' belief that generative AI tools like ChatGPT, Gemini, or
Copilot improve their learning by making it more effective, efficient, and enhancing their academic performance.
Perceived ease of use indicates how much students think these tools are simple to use and require little effort to
interact with. When students have positive perceptions of both usefulness and ease of use, they are more inclined
to accept and regularly use generative AI tools in their academic work.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 850
www.rsisinternational.org
The Technology Acceptance Model (TAM) offers a technological basis for understanding how students' beliefs
about the usefulness and ease of use of generative AI tools impact their decision to adopt and integrate these
tools into their learning activities. This adoption then influences how engaged they become in their academic
work.
Engagement Theory
Engagement Theory, developed by Kearsley and Shneiderman (1998), provides a pedagogical framework
emphasizing how technology can enhance learning engagement. It suggests that meaningful learning happens
when students are actively involved, collaborate with others, and produce knowledge that has value beyond the
classroom. The theory revolves around three main principles:
Relate: This principle stresses the importance of collaboration and communication among learners.
Create: It highlights active learning through the development of meaningful projects.
Donate: This emphasizes the significance of contributing knowledge or products to an authentic, real-world
audience.
Generative AI tools align well with these principles. They support Relate by promoting collaborative learning
through teamwork and AI-assisted problem-solving. They foster Create by enabling students to invent essays,
lesson plans, or research ideas in creative ways. Finally, they facilitate Donate by allowing learners to refine and
share their outputs, providing a sense of academic contribution.
From this viewpoint, Engagement Theory explains how generative AI encourages behavioral, cognitive, and
emotional engagement by making students more active, curious, and involved in their learning process.
METHODOLOGY
Research design
The study adopted a mixed-method research approach, which involves gathering and analyzing both quantitative
and qualitative data to explain a phenomenon, address research gaps, or provide deeper answers to the research
questions. This approach was selected because it offers a more comprehensive understanding of the relationship
between generative AI use and students’ academic engagement. By encouraging active respondent participation,
it allows for richer data collection and more in-depth insights.
Specifically, the study employed a sequential-explanatory design. As Creswell 2017 explains, this design begins
with the collection of quantitative data, followed by qualitative data that builds on the themes identified. In line
with this, the researcher first gathered quantitative data to assess the impact of generative AI on student learning
and academic engagement, primarily through survey questionnaires administered to participants. The analysis
of this data using percentages and mean provided the basis for the next stage, where qualitative data was collected
through semi-structured interviews. The qualitative responses were then examined using thematic analysis of
the transcribed interview coded into themes to explore and clarify the relationship between the use of generative
AI and students’ academic engagement.
Population
Population encompasses the whole group of individuals or items that possess shared characteristics pertinent to
a research study (Norman & Fraenkel, 2000). For this study, the population comprised students from a selected
College of Education in Ghana’s central region, with a target population totaling 298 students, 163 female and
135 males.
Sample and sampling techniques
The study employed purposive sampling to select ninety-four (94) Level 400 students. These students were
intentionally chosen because they were in their final year who were back from their mandatory internship
program. They were selected to be part of the study because they happened to be students who would be requiring
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 851
www.rsisinternational.org
the use of Gen AI tools most because of their project work. and the researchers aimed to investigate how they
utilize generative AI and how it influences their academic engagement.
Validity and Reliability of the Study
The validity of the study was ensured by using a pilot study to test the questionnaire and interview schedule
before administering them to the sampled students. The reliability of the study was also ensured by using a test-
retest reliability method to determine the consistency of the responses of the sampled students.
Ethical Considerations
In the quest to integrate AI into education, ethical considerations must be carefully evaluated. Concerns
surrounding data privacy, algorithmic bias, and the potential for AI to replace human instruction raise critical
questions about the implications of technology in teaching and learning (O’Neil, 2016). In Ghana, where
educational disparities are pronounced, the equitable distribution of AI resources becomes essential to prevent
widening the educational gap between privileged and underserved communities (Kalantzis & Cope, 2012).
Additionally, fostering a responsible AI usage framework that prioritizes the rights of students and educators is
imperative. Educators must be trained not only in employing AI tools but also in understanding their ethical
implications. A balanced approach to AI integration that considers both opportunities and risks is vital to
ensuring that the deployment of technology enhances rather than undermines educational equity and quality.
Participation was voluntary, and informed consent was obtained from all respondents and participants also had
the right to withdraw at any point without any penalty. Data collected was used solely for academic purposes
and securely stored to prevent unauthorized access.
Limitations of the Study
The study focuses only on the kinds of generative AI tools used, identify the different ways students utilize these
GenAI in their learning, and overall influence on their academic engagement among students in the selected
college of education in Ghana. The findings of this study may not be generalizable to other institutions of higher
education outside the selected college of education.
RESULTS AND DISCUSSION
This section presents a detailed discussion of the findings derived from the collected data. The analysis follows
a two-part structure based on the sequential-explanatory design, where quantitative data is gathered and analyzed
first, followed by qualitative data. The quantitative data uses descriptive statistics such as mean, frequency, and
percentage to analyze the results, aimed at addressing the research problem. Subsequently, qualitative data is
collected and analyzed thematically to provide deeper insights and reinforce the quantitative findings. In
designing the qualitative phase, the researchers formulated their interview questions around the highest and
lowest mean scores in each table to clarify and further elaborate on the quantitative results.
Table 1: Kinds of Generative AI used by the students
kinds of Generative AI
Frequency (F)
Percentage (%)
ChatGPT
58
30.5
Gemini
18
9.5
Microsoft Copilot
10
5.3
Chatbot
28
14.7
Quillbot
45
23.7
Grammarly
23
12.1
Perplexity
4
2.1
Canva
2
1.1
Gencraft
1
0.5
Brainly
1
0.5
Total
190
100
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 852
www.rsisinternational.org
Table 1 revealed that most respondents reported using ChatGPT, accounting for 58 responses (30.5%). This was
followed by Quillbot with 45 responses (23.7%). In third place was Chatbot with 28 responses (14.7%), followed
by Grammarly with 23 responses (12.1%). Gemini recorded 18 responses (9.5%), while Microsoft Copilot had
9 responses (5.5%). Perplexity and Canva were used less frequently, with 4 (2.1%) and 2 (1.1%) responses,
respectively, and the least used tools were Gencraft and Brainly, each with only 1 response (0.5%). This indicates
that students in the selected College of Education primarily rely on ChatGPT, QuillBot, and Chatbot. As Hu
(2023) notes, ChatGPT has become the most widely used AI globally. Thus, it is evident from Table 1 that the
generative AI tools most commonly used by these students are text-based AI applications.
In the qualitative phase, students provided rich insights into their frequent use of text-based AI tools, particularly
ChatGPT and QuillBot, on campus. From the data, following themes emerged.
The first recurring theme, Helpful in schoolwork, highlighted why students rely heavily on generative AI like
ChatGPT. “Participants shared their views in the following ways.”
Respondent 1 noted, “For me, accessing ChatGPT is the easiest among all the AI generators because once you
ask a question, the answer is quickly generated.”
Respondent 7 added, “I mostly use text-generated AI like ChatGPT because they are user-friendly, give accurate
information, and are free for everyone.”
These responses suggest that students turn to ChatGPT primarily because of its simplicity, accessibility, and
usefulness in supporting their academic tasks more efficiently. This aligns with the [27] study, which emphasized
that ChatGPT not only assists in academic writing, essay development, and translation but also enables users to
summarize texts, ask and answer questions, and interact in a peer-like manner.
The second theme identified for why students often use text-based AI tools like ChatGPT is Laziness. Several
respondents admitted that they turn to ChatGPT because they feel too lazy to complete their schoolwork
independently.
Respondent 2 shared, “I have been using ChatGPT because I became lazy, and I feel that ChatGPT can provide
all the answers that I need.
Respondent 4 added, “I become lazier in doing schoolwork because I’ve become too reliant on using AI.
These accounts suggest that while ChatGPT’s support features are useful, some students resort to it out of
laziness, making them overly reliant on the AI tool. As Duggal, (2023) argues, integrating AI in education can
foster indolence by automating tasks that would otherwise require analytical thinking and deeper cognitive
engagement. The findings from this study align with [8] assertion, showing that heavy reliance on ChatGPT has
contributed to students’ laziness.
The researcher also sought to explore why only a few respondents utilized generative AI tools such as Gencraft.
The third theme, Unfamiliar, highlights the reasons behind students’ limited use of these types of AI.
For instance, Respondent 7 explained, “Because I’m not familiar with that type of AI, and I have never used an
image generator AI since I don’t have any idea of the purpose of that type of AI.”
Respondent 9 argued, “For me, I didn’t know that there is already a type of generative AI tool like Gencraft and
how it is used, and for what purpose.
Respondent 10 also shared, “I mostly don’t use image-generated AI like Gencraft because they are not as
versatile or as widely applicable as text-based AI.”
These responses suggest that students’ limited exposure to and knowledge of image generator AI, particularly
Gencraft, explains why they rarely use it. Lee, (2023) emphasizes that image generator AI is a rapidly advancing
field with complex algorithms and datasets that are not always easy to understand, leaving many people
unfamiliar with or hesitant toward it.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 853
www.rsisinternational.org
Table 2: The Usage of Generative AI on Learning
Usage of Generative AI
Verbal interpretation
1. I believe generative AI technologies can improve my digital competence
Moderate
2. Generative AI makes it easier for students to find study material
Moderate
3. Generative AI has increased my academic engagement
Moderate
4. Generative AI makes it easier for students to understand the lessons given
by the teacher
Moderate
5. Using generative AI tools has expanded the scope of my ideas.
Moderate
6. Generative AI tools have contributed to improving the overall academic
rigor of my work
High
Overall
Moderate
NB: 1-1.49=very low/strongly disagree; 1.50-2.49= low/disagree; 2.50-3.49=moderate/neutral; 3.50-
4.49=high/agree; 4.50-5.49=very high/strongly agree
Table 2 presents the results on the use of generative AI in students’ academic and learning practices. The table
shows an overall mean score of 3.23, interpreted as moderate, indicating that generative AI has been moderately
useful to students. Among the items, statement 6 recorded the highest mean (3.56), followed by statements 1 and
3 (3.34 each). Statement 4 and 5 obtained a mean of 2.97 and 3.08 respectively while statement 2 had the lowest
mean of 2.94, which is still within the moderate range. This suggests that students’ responses generally fall in
the middle, neither strongly agreeing nor strongly disagreeing. Since statement 6 had the highest mean, it
suggests those students consider generative AI tools to have contributed to improving the overall academic rigor
of my work.
In the qualitative phase, the study further explored whether students’ use of generative AI supported them in
finding study resources. Most respondents affirmed that AI tools significantly assisted them in accessing
lessons and study materials. This theme, referred to as Searching Up, highlights their experiences.
Respondent 6 stated, “Based on my experience in using AI tools, it’s really useful and effective for searching
lessons or study materials that I want to study. I can just type in the lesson, and it appears instantly.”
Respondent 8 added, “Yes, because generative AI like ChatGPT is efficient in finding lessons, especially when
I need more details about topics I struggle to understand. With one click, I can easily access all the information
I need and paraphrase using tools like Quillbot.”
These findings show that generative AI has become a convenient and efficient way for students to access lessons
and materials, especially when they miss classroom notes. AI’s ability to generate study resources quickly has
been a key benefit.
The study also examined whether generative AI supports   . The theme
Synthesizing the Lessons captures this aspect. Most respondents noted that AI simplified lessons by summarizing
or organizing large amounts of content, making comprehension easier.
Respondent 9 explained, “For me, AI has been helpful in making lessons easier since it can summarize or create
bullet points from long texts.
Respondent 10 added, “Generative AI has made difficult lessons and questions from the teacher easier by
researching challenging part of the lessons.”
Overall, these responses indicate that generative AI has become an effective tool for supporting students in better
understanding classroom lessons.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 854
www.rsisinternational.org
Table 3: The Effect of Generative AI on Students' Academic Engagement
Generative AI Effect on Academic Engagement
Mean
Verbal Interpretation
AI's way of linking what students like with the subject matter makes students
understand more and are enthusiastic about learning.
2.89
Moderate
Generative AI increases my productivity in lessons.
3.24
Moderate
By using generative AI tools in this field, I was able to improve my educational
journey.
3.07
Moderate
Generative AI increases my creativity and engagement in school activities.
3.61
High
I recommend the use of AI tools to other students as it increases academic
engagement.
2.98
Moderate
Generative AI tools have improved my ability to present complex data
effectively.
3.12
Moderate
AI helps students improve their achievement in learning, thus improving
academic engagement.
3.52
High
Overall
3.20
Moderate
NB: 1-1.49=very low/strongly disagree; 1.50-2.49= low/disagree; 2.50-3.49=moderate/neutral; 3.50-
4.49=high/agree; 4.50-5.49=very high/strongly agree
Table 3 presents the results on the effect of generative AI on students’ academic engagement. The overall
findings show that generative AI has had a moderate impact (3.20) on students’ engagement. Among the
indicators, statement 4 recorded the highest mean (3.61), suggesting that generative AI highly enhanced students’
creativity and academic engagement. This was closely followed by statement 7 (3.52), also interpreted as high,
and statement 2 (3.24), which shows that Generative AI increases their productivity in lessons. Meanwhile,
statement had the mean score (3.12), indicating Generative AI tools have improved their ability to present
complex data effectively. Statement 3 and 5 with mean values (3.07) and (2.98) suggested that Generative AI
improved their educational journey and recommend the use of AI tools to other students as it increases academic
engagement respectively. Finally, statement 1 (2.89) indicated, AI's way of linking what students like with the
subject matter makes students understand more and demonstrate enthusiasm in learning.
The qualitative results further explored how generative AI has influenced students’ creativity and school
engagement. The first theme, Generating Ideas, revealed that some respondents acknowledged AI as a tool for
boosting creativity by providing them with diverse ideas they could adapt for academic tasks.
Respondent 9 shared, “Generative AI has helped me become more creative, especially in writing research.”
Similarly, Respondent 6 expressed, GenAI is useful in tasks like poster making, where I feel I can create
innovative outputs with AI’s suggestions.
These insights suggest that generative AI fosters creativity by serving as a source of inspiration and
guidance, offering formats, explanations, and novel ideas that aid students in completing academic tasks. This
supports Ali and Eshaq (2023), who noted that AI technologies can analyze vast amounts of data to reveal hidden
patterns, leading to fresh design concepts and perspectives that may otherwise be overlooked.
However, not all respondents perceived AI’s role positively. A second theme, Plagiarism and Less Engagement,
emerged, with three respondents expressing concerns that AI-generated ideas could be seen as plagiarism.
Respondent 2 explained, “No, because I’ve become too reliant on AI. For instance, if I am called during
recitation and I don’t know the answer, I still turn to AI for help instead of actively engaging.
Similarly, Respondent 5 noted that “while AI helps me complete academic tasks easier, it does not foster
creativity and active participation in class.”
“Respondent 4 echoed “AI has been useful, but the creativity is credited to the tool, not to me. I also don’t recite
more because of it.”
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 855
www.rsisinternational.org
These reflections highlight the double-edged role of generative AI in academic life: while it can guide students
and boost participation through interactivity, it also raises risks such as plagiarism and overreliance. Previous
studies also emphasize that AI tools, by generating text without proper citation, pose challenges to academic
integrity (Rodrigues et al., 2024).
Another finding relates to  in classroom lessons. Students have mixed
influence of GenAI usage. whilst some respondents preferred traditional teaching methods over AI usage, other
respondents found AI’s interactive features motivating.
Some expressed their reasons behind their preference for traditional teaching methods over AI usage.
Respondent 8 stated, “I become more interested when the teacher discusses in class. AI alone is not enough to
keep me engaged.”
Similarly, Respondent 3 said, “The explanations from ChatGPT don’t stick in my mind. I learn better when
teachers explain directly.”
Respondent 10 added, “AI didn’t really increase my interest in lessons. What engages me more is the teacher’s
style, especially when they capture my attention.”
These perspectives show that AI, while supportive in completing tasks, does not necessarily foster deeper interest
in lessons. Educators need training to effectively use AI tools and interpret data outputs to guide instruction
(Haleem et al., 2022).
These respondents, on the other hand found motivating.
Respondent 7 commented, “Yes, AI made school more interesting and interactive; it feels like I’m talking to
someone.”
Respondent 3 agreed, saying that AI simplified learning and made lessons easier to understand, while
Respondent 6 highlighted that AI’s assistance during difficult discussions helped sustain their interest.
These accounts suggest that generative AI’s interactive features create more engaging learning experiences for
some students, empowering them to prepare for classroom discussions. This aligns with Kadaruddin (2023), who
found that AI-driven content delivery promotes problem-solving, critical thinking, self-directed learning and
adaptability, empowering students to shape their educational journey.
CONCLUSIONS
From the findings, the following conclusions were drawn:
Students in the College of Education in the selected school in Ghana rely heavily on text-based generative
AI tools, especially ChatGPT, which they use as valuable companions in completing and navigating their
academic tasks.
These tools have become instrumental in enhancing language skills, offering instant feedback on grammar,
clarifying difficult vocabulary, and helping learners better grasp complex terms encountered in digital
spaces.
Beyond language support, generative AI has stimulated creativity and encouraged academic participation
by providing fresh ideas and guidance. Yet, challenges such as plagiarism and overdependence on AI limit
authentic engagement and diminish originality in students’ work.
Implications for educational practice
Generative AI tools like ChatGPT spark curiosity by offering instant feedback, tailored learning journeys,
and interactive experiences that turn complex concepts into manageable insights. They empower students
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 856
www.rsisinternational.org
to explore lessons in advance, paving the way for a more individualized and dynamic approach to
education.
AI-powered platforms refine students’ writing by polishing grammar, enriching vocabulary, and guiding
structure. Acting as intelligent research companions, these tools transform academic writing into a more
approachable and empowering process.
Generative AI serves as a collaborative muse, inspiring fresh perspectives for research, essays, and creative
projects. By expanding the horizon of possible ideas, it amplifies students’ creativity, helping them push
beyond conventional thinking and unlock new levels of productivity.
Recommendations for Educational Practice
Teachers should harness generative AI as a supportive companion in learning, guiding students to see it as
a tool that strengthens their own effort rather than a shortcut.
Curriculum developers ought to craft forward-thinking frameworks that uphold academic honesty, blend
AI-powered assistance with human-centered instruction, and set clear boundaries for ethical use.
Both educators and learners require purposeful training to unlock AI’s potential training that cultivates
critical inquiry, originality, and problem-solving instead of encouraging passive dependence and fostering
laziness.
REFERENCES
1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of
information technology: A replication. MIS Quarterly, 16(2), 227–247. https://doi.org/10.2307/249577
2. Ai, H. (2017). Providing graduated corrective feedback in an intelligent computer-assisted language
learning environment. ReCALL, 29(3), 313–334.
3. Ali, A., & Eshaq, M. (2023). Using Artificial Intelligence for enhancing Human Creativity. Journal of
Art, Design and Music, 2(2). https://doi.org/10.55554/2785-9649.1017
4. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative Artificial Intelligence
(AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN
Electronic Journal. https://doi.org/10.2139/ssrn.4337484
5. Bulawan, A. A., Tilos, F. G., Bulawan, A., Samonte, J., Alejo, K., & Carasicas, M. (2023). The lived
experiences of students in learning with technology: A descriptive phenomenological research study.
International Journal of Advanced Multidisciplinary Research and Studies, 3(4), 438–441.
https://www.multiresearchjournal.com/arclist/list3.4/id-1445
6. Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of
Management Information Systems, 13(2), 185–204. https://doi.org/10.1080/07421222.1996.11518128
7. Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, developments, and future
directions. Sprouts: Working Papers on Information Systems, 9(37), 290.
https://aisel.aisnet.org/sprouts_all/290
8. Creswell, J. W. (2017). Research design: Qualitative, quantitative, and mixed-methods approaches.
SAGE Publications.
9. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A
comparison of two theoretical models. Management Science, 35(8), 982–1003.
https://doi.org/10.1287/mnsc.35.8.982
10. Duggal, P. (2023). AI in education: Boon or bane for learning motivation? International Journal of
Education and Learning, 12(2), 101–115.
11. Estrellado, C. J. (2023). Artificial Intelligence in the Philippine Educational Context: Circumspection
and future inquiries. International Journal of Scientific and Research Publications, 13(4), 16.
https://doi.org/10.29322/IJSRP.13.04.2023.p13704
12. Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for
higher education practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109
13. Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study
of e-commerce adoption. Journal of the Association for Information Systems, 1(8), 1–30.
https://doi.org/10.17705/1jais.00008
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 857
www.rsisinternational.org
14. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1),
99–120. https://doi.org/10.1007/s11023-020-09517-8
15. Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary
Educational Technology, 15(2), Article 421. https://doi.org/10.30935/cedtech/13036
16. Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial Intelligence (AI)
applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 58–
70. https://doi.org/10.1016/j.ijin.2022.08.005
17. Igbaria, M., Parasuraman, S., & Baroudi, J. (1996). A motivational model of microcomputer usage.
Journal of Management Information Systems, 13(1), 127–143.
https://doi.org/10.1080/07421222.1996.11518115
18. Johnson, B. (2023, January 5). NYC schools ban ChatGPT over academic dishonesty fears. The New
York Times.
19. Kadaruddin, K. (2023). Empowering education through generative AI: Innovative instructional strategies
for tomorrow’s learners. International Journal of Business, Law, and Education, 4(2), 618–625.
https://doi.org/10.56442/ijble.v4i2.215
20. Kalantzis, M., & Cope, S. (2012). Learning by design: A cultural history of learning and technology.
Cambridge University Press.
21. Lai, M., Hwang, G., & Chen, C. (2020). The effect of learning analytics and AI-enhanced learning
environments on student engagement: A systematic review. Computers & Education, 157, 103911.
22. Lee, J. (2023). Image generator AI in higher education: Emerging opportunities and challenges. Journal
of Emerging Technologies in Education, 11(2), 55–67.
23. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the
future of education: Ragnarök or reformation? A paradoxical perspective from management educators.
International Journal of Management in Education, 21(2), 100790.
https://doi.org/10.1016/j.ijme.2023.100790
24. Liu, G. L., Darvin, R., & Ma, C. (2024). Exploring AI-mediated informal digital learning of English (AI-
IDLE): A mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer
Assisted Language Learning, 1–29.
25. Nagender, Y., & Patil, K. H. (2017). WhatsApp auto responder using natural language processing and
AI. International Journal of Computer Engineering & Technology, 8(5), 15–22.
26. Norman, E. W., & Fraenkel, J. R. (2000). How to design and evaluate research in education. McGraw-
Hill.
27. Okoye, M., & Mante, D. (2024). The nexus between artificial intelligence and STEM education:
Research on AI applications in higher education. Educational Technology Research and Development,
68(4), 1851–1861.
28. Opoku, M. O., & Enu-Kwesi, F. (2019). Relevance of the technology acceptance model (TAM) in
information management research: A review of selected empirical evidence. Research Journal of
Business and Management, 6(1), 55–62. https://doi.org/10.17261/Pressacademia.2019.1028
29. Oravec, J. (2023). Artificial intelligence implications for academic cheating: Expanding the dimensions
of responsible human-AI collaboration with ChatGPT and Bard. Journal of Interactive Learning
Research, 34(2), 213–237.
30. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens
democracy. Crown Publishing Group.
31. Piaget, J. (1973). The child and reality: Problems of genetic psychology. Basic Books.
32. Rodrigues, M., Silva, R., Franco, M. A. P. B., & Oliveira, C. (2024). Artificial intelligence: Threat or
asset to academic integrity? A bibliometric analysis. Kybernetes. https://doi.org/10.1108/k-09-2023-
1666
33. Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. F. (2024). Investigating the moderating effects of social
good and confidence on teachers’ intention to prepare school students for artificial intelligence education.
Education and Information Technologies. https://doi.org/10.1007/s10639-023-12250-1
34. Sok, P., & Heng, C. (2023). ChatGPT and academic writing: Student perceptions in Cambodian
universities. Asian Journal of Education, 14(3), 245–259.
35. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development
and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025
Page 858
www.rsisinternational.org
36. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four
longitudinal field studies. Management Science, 46(2), 186–204.
https://doi.org/10.1287/mnsc.46.2.186.11926
37. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard
University Press.
38. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007a). Technology acceptance: A meta-analysis of
the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280.
https://doi.org/10.1108/17465660710834453