INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Factors Influencing AI Tool Adoption in Research Among Junior  
and Senior Students of QCU College of Computer Studies  
Christian I. Ancog1, Harold R. Lucero2, Imma R. Estrada3, Heila Arriane T. Longaquit4, Sean Whymz  
M. Lombre5, Nicole T. Mayo6  
College of Computer Studies, Quezon City University  
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500102  
Received: 06 May 2026; Accepted: 11 May 2026; Published: 4 June 2026  
ABSTRACT  
The increasing integration of Artificial Intelligence (AI) in higher education has transformed academic research  
practices, particularly in literature review, data analysis, and research writing. This study investigated the factors  
influencing the use of AI tools in academic research among junior and senior students of the College of Computer  
Studies at Quezon City University. Specifically, it examined the respondents’ demographic profile, level of AI  
tool utilization, factors affecting AI adoption, barriers to AI usage, and the relationship between these factors  
and students’ actual AI usage behavior. The study employed a quantitative descriptive-correlational research  
design and utilized an online survey questionnaire administered to 130 students from the BS Information  
Technology, BS Information Systems, and BS Computer Science programs using stratified sampling.  
Descriptive statistics, Pearson Product-Moment Correlation, and Multiple Regression Analysis were used to  
analyze the collected data. Findings revealed that students demonstrated a moderate level of AI tool utilization  
in research activities, with perceived usefulness emerging as the most influential factor affecting AI adoption.  
Students commonly used AI tools for idea generation, information retrieval, and writing assistance; however, AI  
utilization remained limited due to concerns related to plagiarism, data privacy, ethical misuse, overdependence  
on AI-generated outputs, and insufficient institutional support. The study also found a significant relationship  
between the identified adoption factors and students’ actual AI usage behavior. Overall, the findings suggest that  
AI tools have strong potential to improve research productivity and efficiency, but educational institutions must  
establish clear policies, governance frameworks, and training programs to ensure the responsible, ethical, and  
effective integration of AI in academic research.  
Keywords: Academic Research Tools, AI-Assisted Research, AI Literacy, Educational Technology, Human-  
Computer Interaction  
INTRODUCTION  
Artificial intelligence is a technology that enables machines and computers to behave like humans in terms of  
autonomy, creativity, problem solving, learning and understanding (Stryker & Kavlakoglu, 2026). AI models  
become more intelligent, developed, and specialized. They grow in their usefulness to other areas, learn more  
data, and become more easily part of workflows (OwlAiSolution, 2025). AI tools have significantly changed the  
way research is done by allowing quicker data analysis, automatic writing and literature review assistance, and  
increased productivity in many phases of research, such as ideation, methodology development, data processing,  
drafting, and editing.  
Madanchian and Taherdoost (2025) state that the introduction of AI into workflows has revolutionized the  
lifecycle in various phases of research. Since data analysis and discovery of literature through AI are fast, better,  
and easier, writing aids and collaboration are faster, clearer, and more convenient. The challenge of AI usage in  
research exists. Tool accessibility, output reliability, and AI ethics are some of the largest concerns to this day.  
AI was mainly used in higher education contexts in the application of AI to research purposes. In a study, Bula  
et al. (2025) investigated how AI tools are used by university students to support their research in library science  
and found out that there is widespread use of AI tools like ChatGPT to perform a literature review and initial  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
data analysis. One of the notable trends was in the STEM sectors, where machine learning was increasingly  
becoming a part of research (Barrot, 2023).  
Artificial intelligence learning management systems have been used in many universities in tertiary education  
as a way of delivering personalized learning experiences and adaptive content delivery. In a study carried out by  
Espartinez (2024), the use of generative AI technologies in composition instruction was observed in eight  
academic institutions, with the use of the technologies being largely motivated by the instructors and not  
institutional requirements.  
Although AI tools are used by students and individual instructors widely, there is a significant gap in the body  
of research related to the vast gap between the fast, bottom-up technological adoption and the absence of holistic  
institutional structures and ethical practices (Jobs for the Future, 2026). Patterson (2024) explains that this gap  
is entrenched in the difference in the factors affecting AI adoption between students and faculty. In the case of  
students, performance expectancy and effort expectancy are the main factors that drive them to adopt AI because  
they believe that it will greatly enhance their academic performance and that the AI is easy to use.  
In spite of the growing role of artificial intelligence in higher education, there remains a considerable gap in the  
critical analysis of the technical soundness and user-friendliness of the systems. The existing practices in higher  
education institutions like Quezon City University College of Computer Studies tend to be incomplete and  
mostly reliant on individual faculty members, and thus there is a significant area of knowledge gap on whether  
these tools have sufficiently met the rigorous international standards of operational adequacy, reliability, and  
security. This unstructured use reveals a significant gap between instructor readiness and system performance  
expectations. While this impact is institution-wide, there is an urgent need to first quantify the student experience  
specifically among upper-level students (Juniors and Seniors) who are actively engaged in capstone research, to  
establish a baseline for these performance expectations.  
In the context of the QCU College of Computer Studies in particular, what is unclear is what most greatly  
influences the desire and ability of faculty and students to utilize AI tools in the research setting and whether the  
latter can serve its users equally regardless of their level of technical skills and scholarly positions. The institution  
will not undertake a critical analysis of the linkage between the quality of the system, user satisfaction, and  
adoption behavior among these demographics and there is a risk of adopting technologies that will undermine  
academic integrity and not provide equitable and effective learning and research experiences.  
In order to resolve these inconsistencies, this research paper suggests an Empirical Framework of Integrating AI  
Research, a roadmap of strategies that are expected to help the QCU-CCS community to transition to an  
unregulated but active AI usage into a proactive and ethically responsible community. The suggested solution  
focuses on three key priorities: harmonizing technical effectiveness through the choice of tools to comply with  
international security standards, harmonizing faculty preparedness and student performance expectations on  
equal terms of access, and creating a data-driven backbone to a localized AI Research Governance Roadmap.  
The framework will combine the Technology Acceptance Model (TAM) with technical reliability to provide  
institutional support and policy guidance, specifically focusing on how Perceived Usefulness and Perceived Ease  
of Use drive adoption among Junior and Senior students during the research process.  
The implications of the proposed research are two-fold, as the research will provide strategic value to many  
stakeholders in the university ecosystem by bridging the existing knowledge gap on the technical feasibility of  
AI and its user-focused effectiveness.  
To the QCU Administration, this study is an empirical study that will fill the gap between the technological  
adoption that is bottom-up and lack of holistic institutional structures (Jobs for the Future, 2026). The results  
will inform the administration to write a localized AI Research Governance Roadmap by determining the factors,  
which meet international standards of functional adequacy and security. This is essential since, in accordance  
with OwlAiSolution (2025), as AI models gain more specialization and can be easily incorporated into the  
workflows, the institutions should consider both the quality of systems and user satisfaction as one unit,  
otherwise they will put in force the incorrect technologies that undermine academic integrity.  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
To the QCU-CCS Faculty, the study serves as an essential tool for harmonizing teaching strategies with actual  
student usage patterns. Because current AI adoption is frequently instructor-centered rather than institutionally  
mandated (Espartinez, 2024), this research offers a standardized model to align faculty guidance with the specific  
factors that drive Junior and Senior students to utilize these tools. By understanding these drivers, instructors  
can better facilitate a transition from mere writing assistance toward a more radical and rigorous implementation  
of AI in the research process, such as complex data processing and methodology development, as suggested by  
Madanchian and Taherdoost (2025).  
To QCU-CCS students, specifically those in the 3rd and 4th-year levels, the study determines the particular  
technical skills (e.g., prompt engineering and AI literacy) that they need to stay competitive. As student adoption  
depends on performance and expectancy of effort (Patterson, 2024), this paper makes sure that their use of such  
tools as LLMs to review literature and analyze data is on the basis of technical self-efficacy (Sambrano et al.,  
2025). This is especially essential among the students in the STEM and computing fields, where machine  
learning and AI are increasingly being made essential parts of the research process (Barrot, 2023).  
To Broader Academic Community, this study is added to the worldwide discussion of AI ethics and the use of  
technologies. Offering a computing-oriented approach to specialized empirical data, the study contributes to  
understanding how machines can act autonomously and creatively and still be academically rigorous (Stryker  
and Kavlakoglu, 2026). It provides an Empirical Framework of AI Research Integration that can be replicated  
in other institutions of higher education to transform unregulated use of AI into an active and ethically  
responsible community.  
Statement of the Problem  
This study aims to determine the factors influencing the adoption of Artificial Intelligence (AI) tools in research  
among the junior and senior students of the College of Computer Studies at Quezon City University (QCU).  
Specifically, it seeks to answer the following questions:  
1. What is the profile of the respondents in terms of:  
1.1 Sex;  
1.2 Academic program (BSIT, BSIS, or BSCS); and  
1.3 Year Level (3rd Year or 4th Year);  
1.4 Years of experience in research?  
2. What is the level of AI tool adoption among the respondents in terms of:  
2.1 Frequency of use;  
2.2 Types of AI tools used; and  
2.3 Purpose of usage in research?  
3. How do the respondents perceive the influence of the following factors on AI adoption:  
3.1 Perceived usefulness;  
3.2 Perceived ease of use;  
3.3 Accessibility;  
3.4 Technical skills;  
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3.5 Institutional support; and  
3.6 Ethical concerns?  
4. Is there a significant difference in the level of AI tool adoption and the perceived influencing factors when  
respondents are grouped according to:  
4.1 Academic Program (BSCS, BSIT, BSIS); and  
4.2 Year Level (3rd Year and 4th Year)?  
5. Is there a significant relationship between the identified factors and the adoption of AI tools in research?  
6. Which among the identified factors significantly influence the adoption of AI tools in research among CCS  
students?  
RELATED LITERATURE AND STUDIES  
Artificial Intelligence (AI) has significantly transformed academic research and higher education by improving  
efficiency in literature review, data analysis, content generation, research organization, and publication  
processes. Studies synthesizing multiple investigations identified six major domains where AI contributes to  
academic work: research planning and idea generation, content organization, literature synthesis, data  
management and analysis, publication assistance, and communication and ethical compliance (Khalifa &  
Albadawy, 2024). Similarly, Arangüena (2024) emphasized that advancements in AI, particularly following the  
release of GPT-4, have enabled researchers to automate literature reviews, generate hypotheses, conduct  
complex data analyses, and streamline peer review processes. AI-powered tools such as Consensus, Semantic  
Scholar, Elicit, Perplexity, Connected Papers, Research Rabbit, Scholarcy, scite, Keenious, and Undermind  
further enhance research productivity by assisting researchers in locating relevant literature, synthesizing  
findings, visualizing citation relationships, and refining research questions (Giugliano, 2026; Khailova, 2025).  
In addition, generative AI platforms including ChatGPT, Claude, Gemini, and Perplexity are widely used for  
brainstorming, summarization, academic writing, and preliminary literature searches, contributing to faster  
information retrieval and improved research efficiency.  
Despite these advantages, the integration of AI in research and education remains accompanied by substantial  
ethical and practical concerns. Khalifa and Albadawy (2024) highlighted the need to balance AI efficiency with  
human critical thinking and scholarly judgment to maintain academic integrity. Arangüena (2024) also discussed  
the “black box” phenomenon in AI systems, wherein the lack of transparency in AI-generated outputs creates  
challenges in verifying the validity and reliability of generated insights. Kelly (2024) further warned that large  
language models may produce “hallucinations” or fabricated information, potentially compromising scientific  
accuracy and scholarly credibility. Similarly, Hutson (2024) and Saenz et al. (2024) explained that AI-generated  
outputs may contribute to plagiarism and unethical research practices when used without proper attribution or  
critical evaluation. Concerns regarding bias, data privacy, overdependence on AI systems, and erosion of critical  
thinking skills have also been emphasized in several studies (Arcilla et al., 2023; ResearchGate, 2026; Santos &  
Rivera, 2023). Consequently, researchers and institutions stress the importance of maintaining a “human-in-the-  
loop” approach to ensure transparency, accountability, and ethical application of AI technologies in academic  
research (University of California, Davis, 2024; Kelly, 2024).  
The adoption of AI technologies in education and research is commonly explained through established  
technology acceptance frameworks such as the Technology Acceptance Model (TAM) and the Unified Theory  
of Acceptance and Use of Technology (UTAUT). According to Martin (2022), TAM identifies perceived  
usefulness and perceived ease of use as the primary determinants influencing users’ attitudes and behavioral  
intentions toward technology adoption. Perceived usefulness refers to the belief that AI enhances work  
performance, while perceived ease of use pertains to the extent to which AI systems are viewed as simple and  
effortless to operate. Studies consistently demonstrate that these factors significantly influence AI adoption  
among researchers and students (Falebita & Kok, 2024; Saif et al., 2024). Likewise, UTAUT explains that  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
performance expectancy, effort expectancy, social influence, and facilitating conditions shape technology  
adoption behaviors (Venkatesh et al., 2003). In the context of AI integration, social influence and institutional  
support were found to significantly affect users’ willingness to adopt AI technologies in academic environments.  
Taheri et al. (2025) further explained that AI adoption is a multifaceted process influenced by personal beliefs,  
AI literacy, institutional infrastructure, transparency of AI systems, professional concerns, and fears regarding  
technological overreliance.  
Institutional readiness and support also emerged as critical determinants of successful AI integration in higher  
education. Popović Šević et al. (2025) found that faculty members who actively used AI tools such as ChatGPT  
viewed these technologies more positively than non-users, although both groups identified the lack of ethical  
guidelines and structured training as significant barriers to effective adoption. The researchers recommended  
that institutions integrate AI into assessment design, personalized feedback, and scenario-based learning while  
simultaneously establishing clear governance frameworks. Xie et al. (2025) similarly noted that faculty  
expectations regarding AI usage vary across undergraduate and graduate education, highlighting the need for  
flexible institutional policies that account for students’ academic backgrounds and technological familiarity.  
Fute et al. further emphasized that institutional assistance, training programs, and AI-related frameworks bridge  
the gap between AI literacy and actual adoption by strengthening users’ confidence and perceived usefulness of  
AI systems. However, Bećirović et al. (2025) cautioned that excessive criticism of AI without sufficient technical  
understanding may negatively affect AI self-efficacy and lead to ineffective utilization of AI technologies.  
Globally, AI adoption continues to expand rapidly across educational and professional settings. Carolan et al.  
(2025) estimated that approximately 1.7 to 1.8 billion individuals worldwide use AI tools, with professionals  
and students representing the highest usage groups. AI technologies are increasingly utilized not only for  
academic research but also for accessibility purposes, such as text simplification, voice-to-text conversion,  
summarization, and adaptive learning support, particularly benefiting neurodiverse learners and individuals with  
disabilities (University of California, Davis, 2024). However, researchers consistently emphasize that AI tools  
should complement rather than replace human expertise and intellectual engagement.  
Within the Philippine context, AI adoption in higher education reflects both global opportunities and local  
challenges. Filipino students and educators increasingly use AI tools such as ChatGPT, Grammarly, QuillBot,  
and AI-integrated productivity applications for academic writing, research assistance, workflow automation, and  
content generation (Castagna et al., 2026; Co, 2025; Villarino, 2025). AI adoption in Philippine higher education  
has grown steadily from limited exposure during remote learning periods to more structured implementation in  
instruction, assessment, and research activities (Co, 2025; Villarino, 2025). Students commonly utilize AI for  
brainstorming, summarization, research writing, and personalized learning support, while educators apply AI  
technologies to improve instructional design and classroom efficiency (Besas et al., 2026; Sibug et al., 2026).  
Research further indicates that perceived usefulness remains the strongest predictor of AI adoption among  
Filipino students and educators, while perceived ease of use significantly affects adoption frequency and user  
competence (Asio, 2024; Lalisan et al., 2026). These findings align with TAM and UTAUT frameworks, which  
explain that user-friendly systems and perceived benefits strongly influence AI acceptance (Saflor, 2025).  
Despite increasing adoption, significant barriers continue to hinder AI integration in Philippine higher education.  
Accessibility remains a major concern due to the digital divide, inconsistent internet connectivity, outdated  
technological infrastructure, and unequal institutional resources (Saputra et al., 2023; Quimba, 2026).  
Institutional readiness in many Philippine educational institutions remains low to moderate, with deficiencies in  
policy frameworks, ICT staffing, faculty training, and AI governance structures (Global Scientific Journal, 2025;  
Quimba, 2026). Ethical concerns regarding plagiarism, overreliance on AI-generated outputs, data privacy,  
algorithmic bias, and the erosion of critical thinking skills also persist among Filipino scholars and educators  
(Arcilla et al., 2023; Fernando et al., 2026; Villarino, 2025). Moreover, local studies reveal that while awareness  
of AI benefits is generally high, formal training on responsible and ethical AI usage remains limited (Wibowo  
et al., 2025). Existing Philippine literature primarily focuses on conceptual discussions and policy concerns  
rather than empirical investigations of actual user experiences and perceptions, limiting the development of  
context-sensitive AI policies and training programs (Carvajal et al., 2025).  
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The reviewed literature collectively demonstrates that AI technologies have become integral to modern academic  
research and higher education due to their ability to improve productivity, accessibility, efficiency, and  
information management. However, AI integration is shaped by a complex interaction of technological,  
institutional, ethical, psychological, and contextual factors. Perceived usefulness, ease of use, institutional  
support, accessibility, AI literacy, and ethical considerations consistently emerge as major determinants  
influencing AI adoption and usage behaviors among students and educators. Although AI tools offer substantial  
benefits in research and learning, concerns regarding academic integrity, transparency, bias, overdependence,  
and unequal access remain unresolved. Furthermore, despite the growing body of international and local  
literature, empirical studies examining students’ actual experiences, perceptions, and patterns of AI tool  
utilization within specific Philippine higher education contexts remain limited. This gap underscores the need  
for localized research that can support the development of effective institutional policies, ethical guidelines, and  
AI literacy programs for the responsible integration of AI in academic research and education.  
DESIGN AND METHODOLOGY  
Research Design  
Figure 1. Research Design of the Study  
This study will utilize a quantitative-descriptive correlational research design to determine the factors  
influencing the adoption of Artificial Intelligence (AI) tools in research among Quezon City University (QCU)  
students. The descriptive method will be used to describe the respondents’ profile, level of AI tool adoption,  
influencing factors, and perceived barriers in using AI tools for research purposes. Meanwhile, the correlational  
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approach will be employed to examine the relationship between the identified influencing factors and the  
adoption of AI tools in research (Perante et al., 2025).  
The study focuses on 3rd year and 4th year students enrolled in Bachelor of Science in Information Technology  
(BSIT), Bachelor of Science in Information Systems (BSIS), and Bachelor of Science in Computer Science  
(BSCS) programs. Data will be collected through an online survey questionnaire composed of structured  
questions using a Likert Scale format.  
The study aims to determine:  
1.  
2.  
3.  
4.  
5.  
The profile of the respondents;  
The level of adoption of AI tools in research;  
The factors influencing AI tool adoption;  
The perceived barriers in using AI tools for research; and  
The significant relationship between the influencing factors and AI tool adoption.  
Data Gathering  
The data collection process will commence with the distribution of the online survey link QCU students, such  
as 3rd Year and 4th Year BSIT, BSCS, and BSIS participants. Participants will be informed about the objectives  
of the study, and their voluntary participation will be emphasized. The survey will be open for a specified period,  
allowing respondents adequate time to provide thoughtful and comprehensive responses.  
The data collection process will commence with the distribution of an online survey questionnaire through  
Google Forms to selected QCU students, specifically 3rd year and 4th year BSIT, BSIS, and BSCS students.  
Prior to answering the survey, respondents will be informed about the objectives and purpose of the study. Their  
voluntary participation, confidentiality of responses, and anonymity will also be emphasized through an  
informed consent statement included at the beginning of the questionnaire.  
The survey questionnaire will consist of three parts:  
1.  
2.  
3.  
Respondents’ demographic profile;  
Questions regarding the adoption and use of AI tools in research; and  
Statements identifying the factors influencing AI tool adoption and perceived barriers.  
The survey link will be shared through online communication platforms such as Messenger, email, and class  
group chats. Respondents will be given sufficient time to answer the questionnaire to ensure thoughtful and  
accurate responses.  
The researchers will monitor the responses and ensure that all gathered data are complete and valid for statistical  
analysis.  
Sampling Technique  
The study will use Stratified Sampling to ensure that respondents from each academic program and year level  
are properly represented. The population of the study is composed of the following strata:  
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Table 1. Population of the Study  
Stratum  
Population  
903  
752  
44  
40  
BSIT 3rd Year Students  
BSIT 4th Year Students  
BSIS 3rd Year Students  
BSCS 3rd Year Students  
Total  
1,739  
The researchers gathered a total of 130 respondents distributed as follows:  
Table 2. Number of Respondents  
Stratum  
Number of Respondents  
123  
5
2
130  
BSIT (Combined 3rd and 4th year students)  
BSIS  
BSCS  
Total  
The respondents were selected based on their availability and willingness to participate in the survey. Although  
the study employed stratified sampling, the distribution of respondents was conducted in a disproportionate  
manner due to limited time, accessibility of participants, and response availability during the data gathering  
period. This means that the number of respondents gathered from each stratum was not proportionally equal to  
the actual population size of each group.  
Despite this limitation, stratified sampling was still applied to ensure that each identified academic group was  
represented in the study, allowing the researchers to obtain data from different programs and year levels relevant  
to the research objectives.  
Statistical Treatment of Data  
The data gathered in this study will be analyzed and interpreted using appropriate statistical tools to answer the  
research questions. The following statistical methods will be used:  
Frequency and Percentage Distribution  
This statistical tool will be used to describe the profile of the respondents in terms of age, sex, year level, and  
course. It will also be used to determine the frequency of AI tools used and their purposes in research.  
Figure 1. Formula to Compute the Frequency  
Where:  
● f = frequency  
● N = total number of respondents  
Weighted Mean  
The weighted mean will be used to determine the level of AI tool adoption among the respondents and the level  
of influence of the identified factors such as perceived usefulness, ease of use, accessibility of tools, technical  
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skills, institutional support, and ethical concerns (Falebita & Kok, 2024). It will also be used to measure the  
perceived barriers in the adoption of AI tools in research.  
Figure 2. Weighted Mean Formula  
Where:  
● f = frequency of responses  
● x = scale value  
● N = total number of respondents  
The following scale will be used to interpret the weighted mean results regarding the respondents’ level of  
adoption, influencing factors, and perceived barriers:  
Table 1. 5-Point Likert Scale Used in the Study  
Weighted Mean Range  
4.21 – 5.00  
3.41 – 4.20  
Interpretation  
Strongly Agree/ Very High Influence  
Agree / High Influence  
2.61 – 3.40  
1.81 – 2.60  
Moderately Agree / Moderate Influence  
Disagree/ Low Influence  
1.00 – 1.80  
Strongly Disagree/ Very Low Influence  
Pearson Product-Moment Correlation Coefficient (r)  
The Pearson Product-Moment Correlation Coefficient will be used to determine whether there is a significant  
relationship between the identified influencing factors and the adoption of AI tools in research among the  
respondents. This will be utilized to measure the strength and direction of the relationship between variables and  
to determine whether the influencing factors are significantly associated with AI tool adoption.  
Multiple Regression Analysis  
Multiple Regression Analysis will be employed to determine which among the identified factors significantly  
influence or predict the adoption of AI tools in research among students. This statistical method will allow the  
researchers to examine the combined effect of multiple independent variables on the dependent variable, which  
is the level of AI tool adoption in research. Through this analysis, the study aims to identify the strongest  
predictors of AI adoption and assess the extent to which the identified factors collectively contribute to students’  
utilization of AI tools in academic research activities.  
RESULT AND DISCUSSION  
Profile of the Respondents  
Table1. Profile of the Respondents  
Profile Variable  
Academic Program  
Classification  
Frequency(f)  
123  
5
Percentage(%)  
94.6%  
3.8%  
BSIT (Combined 3rd and 4th Year)  
BSIS  
BSCS  
2
1.6%  
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130  
65  
26  
100%  
50%  
20%  
30%  
TOTAL  
Primary Research Domain  
Software Development  
Information System  
Data Science  
39  
The results show that the study is heavily represented by BSIT students (94.6%), primarily focusing on Software  
Development (50%). This alignment is critical as technical programs often integrate AI tools like GitHub Copilot  
and automated data analytics earlier than other domains. While stratified sampling was used, the disproportionate  
distribution reflects the accessibility of the BSIT cohort during the data-gathering period.  
Level of Adoption of AI Tools in Research  
Table 2. Level of Adoption of AI Tools in Research Among Students of the College of Computer Studies of  
QCU  
AI Adoption Indicators  
Weighted Mean  
Interpretation  
Regular use of AI tools in research  
Use of different AI tools for different tasks  
Integration of AI into research workflow  
Use of AI tools across multiple research stages  
Composite Mean  
3.15  
3.20  
3.18  
3.20  
3.18  
Moderately Agree  
Moderately Agree  
Moderately Agree  
Moderately Agree  
Moderate Influence  
A composite mean of 3.18 signifies a Moderate Level of Adoption. This suggests that while students frequently  
use tools like ChatGPT and Google Gemini for idea generation and writing, AI has not yet fully replaced  
traditional research methods. The "Moderately Agree" rating implies that students view AI as a supplementary  
assistant rather than a primary researcher.  
Factors Influencing AI Tool Adoption  
Table 3. Factors Influencing AI Tools Adoption Among Students of the College of Computer Studies of QCU  
Influencing Factor  
Perceived Usefulness  
Ease of Use  
Weighted Mean  
Interpretation  
3.45  
3.38  
3.15  
Agree / High Influence  
Moderately Agree  
Moderately Agree  
Technical Skills  
Perceived Usefulness (3.45) emerged as the strongest influencer. This indicates that the 130 respondents are  
motivated primarily by the tangible benefits of AI, specifically in improving productivity and efficiency. This  
aligns with modern educational theories suggesting that technology adoption is highest when the user perceives  
a direct "effort-to-result" advantage.  
Perceived Barriers in Using AI Tools for Research  
Table 4. Perceived Barriers in Using AI Tools for Research Among Respondents  
Perceived Barrier  
Weighted Mean  
Interpretation  
Institutional Support  
Ethical Concerns (Lack of Concern Statements)  
2.25  
2.10  
Disagree / Low Influence  
Disagree / Low Influence  
There is a significant gap in Institutional Support (2.25), with respondents indicating a lack of clear university  
policies or training. Furthermore, the low mean in "Ethical Concerns" (which were phrased as not being  
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concerned) reveals that students actually harbor High Concerns regarding data privacy and plagiarism. These  
barriers explain why adoption remains at a "Moderate" level despite the tools' high perceived usefulness.  
Relationship Between Influencing Factors and AI Adoption  
Based on the Pearson r and Multiple Regression goals, the findings suggest a strong positive correlation between  
Perceived Usefulness and Adoption Level. However, the negative influence of Perceived Barriers (ethical risks  
and lack of support) acts as a significant predictor that prevents students from moving from "Moderate" to "Very  
High" adoption. This fulfills the objective of identifying the best predictors for AI tool adoption among QCU  
students.  
CONCLUSION  
The study into the adoption of Artificial Intelligence (AI) among Junior and Senior students at the Quezon City  
University College of Computer Studies establishes that while technological integration is actively occurring, it  
remains in a transitional, supplementary phase. By synthesizing the findings in relation to the research objectives,  
the following conclusions are established:  
The demographic and academic profile of the participants, predominantly Information Technology students  
specializing in Software Development, establishes a natural alignment between their academic focus and the  
early adoption of technical AI tools. This specialized background provides a foundation for students to engage  
with emerging technologies as a routine part of their academic work.  
The study proves that there is a moderate level of AI adoption among the students. Currently, AI platforms serve  
as supplementary assistants for tasks such as idea generation and writing, rather than acting as a primary  
replacement for traditional research methodologies. Perceived usefulness is the most significant driver for this  
adoption, as students are strongly motivated by the tangible improvements in productivity and efficiency that  
these tools offer. This confirms that AI acceptance is highest when users recognize a direct advantage in their  
results relative to the effort exerted.  
Despite these clear drivers, full integration is currently hindered by significant institutional and ethical barriers.  
There is a notable deficiency in institutional support, characterized by a lack of clear university policies and  
structured training frameworks. Furthermore, high ethical concerns regarding data privacy and the potential for  
plagiarism act as significant deterrents that prevent the student body from moving toward a more advanced level  
of adoption.  
In conclusion, the study proves that while a strong positive correlation exists between perceived usefulness and  
the level of AI adoption, the negative influence of ethical risks and insufficient institutional support serves as a  
significant predictor that restricts adoption to a moderate level. To move from unregulated usage toward a  
proactive and ethically responsible research community, the university must address these gaps through a  
localized governance roadmap that harmonizes technical effectiveness with rigorous ethical standards and  
institutional guidance.  
REFERENCES  
1.  
Allen Negrosa, G. R., Pamaran, E. G., & Tabuyo, J. U. (2023). EFFECTIVENESS OF DIGITAL  
MARKETING TO ONLINE FOOD SELLERS Bachelor of Science in Hotel and Restaurant  
Management. International Journal of Management and Commerce Innovations, 11, 296–313.  
https://doi.org/10.5281/zenodo.10574982  
2.  
3.  
Arangüena, I. (2024). AI in Research: Its Uses and Limitations. Research To Action.  
https://www.researchtoaction.org/author/inesresearchtoaction-org/  
Asio, J. M. R. (2024). Artificial Intelligence (AI) literacy and academic performance of tertiary level  
students: A preliminary analysis. Social Sciences, Humanities and Education Journal (SHE Journal),  
5(2), 309–321.  
Page 1323  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Barrot, J. S. (2023). Using ChatGPT for Second Language Writing: Pitfalls and Potentials. Assessing  
Writing, 57, Article 100745. https://doi.org/10.1016/j.asw.2023.100745  
4.  
5.  
Besas, J., Patac, L., & Patac Jr., A. (2026). AI-facilitated self-directed learning and mathematics  
performance: A mixed-methods study on ChatGPT use among Generation Z students. International  
Journal of Contemporary Educational Research. https://ijcer.net/index.php/pub/article/view/822  
Besas, J., Patac, L., & Patac Jr., A. (2026). AI-facilitated self-directed learning and mathematics  
performance: A mixed-methods study on ChatGPT use among Generation Z students. International  
Journal of Contemporary Educational Research. https://ijcer.net/index.php/pub/article/view/822  
Bećirović, S., Polz, E., & Tinkel, I. (2025). Exploring students’ AI literacy and its effects on their AI  
output quality, self-efficacy, and academic performance. Smart Learning Environments, 12(1).  
https://doi.org/10.1186/s40561-025-00384-3  
6.  
7.  
8.  
9.  
Carolan, S., Martin, A., Gong, C., Borja, S., & Sonnet, C. (2025). AI’s Consumer Tipping Point Has  
Arrived.  
https://menlovc.com/wp-content/uploads/2025/11/menlo_ventures_consumer_ai_report-  
2025.pdf?  
Co, S. J. (2025). Artificial intelligence in Philippine education: A narrative review of applications,  
perceptions, and challenges. International Research Journal of Science Engineering and Technology,  
5(2), 25–38. https://doi.org/10.5281/zenodo.15902950  
10. Co, S. J. (2025). Artificial intelligence in Philippine education: A narrative review of applications,  
perceptions, and challenges. International Research Journal of Science Engineering and Technology,  
5(2), 25–38. https://doi.org/10.5281/zenodo.15902950  
11. Department of Education. (2026). Foundational guidelines on artificial intelligence (AI) in basic  
education (DepEd Order No. 003, s. 2026). https://www.deped.gov.ph/  
12. Ellington, K. (2024). Statistical Treatment of Data Analysis - Research Methods. studocu.com.ph.  
Retrieved May 14, 2026, from https://www.studocu.com/ph/document/marikina-polytechnic-  
college/bachelor-of-industrial-technology-major-in-electronics-technology/statistical-  
research/111029454  
13. Espartinez, B. (2024). Bridging the educational divide with ChatGPT’s integration in higher education:  
A Q-methodology study. Journal of Information Technology Education: Innovations in Practice, 23,  
211–241. doi.org https://www.jite.org/documents/Vol24/JITE-IIPv24Art011Espartinez11758.pdf  
14. Falebita, O. S., & Kok, P. J. (2024). Artificial Intelligence Tools Usage: A structural equation modeling  
of undergraduates’ technological readiness, Self-Efficacy and attitudes. Journal for STEM Education  
Research, 8(2), 257–282. https://doi.org/10.1007/s41979-024-00132-1  
15. Falebita, O., & Kok, J. (2024). Exploring the Factors Influencing Artificial Intelligence Adoption among  
Gen Z University Students: the Role of Self-Efficacy and Perceived Trust. Journal of Educational  
Technology & Society. https://doi.org/10.1007/s41979-025-00171-2  
16. Fute, A., Oubibi, M., Amihere, A., Kangwa, D., & Msafiri, M. M. (2026). Institutional support for  
ethical AI adoption in higher education amid the rising trend of manuscript retractions. Humanities and  
Social Sciences Communications. https://doi.org/10.1057/s41599-026-07235-7  
17. Giugliano, A. (2026). Best AI Tools for Academic Research in 2026: A Step-by-Step Workflow Guide.  
Thesify. https://www.thesify.ai/blog/best-ai-tools-academic-research  
18. Global Scientific Journal. (2025). Ethical concerns in artificial intelligence-driven education:  
Perspective from educators and students. Global Scientific Journal, 13(5), 280-285.  
19. Jobs for the Future. (2026) AI Usage in Education is Growing, But Gaps in Guidance Persist, New  
Survey Finds. Jobs for the Future (JFF). https://www.jff.org/newsroom/press-releases/ai-usage-in-  
education-is-growing-but-gaps-in-guidance-persist-new-survey-finds/  
20. Khailova, L. (2025, February 2). Guides: Artificial intelligence (generative) resources: AI tools for  
research. Guides.library.georgetown.edu. https://guides.library.georgetown.edu/ai/tools  
21. Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An  
essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5(1), 100145.  
https://doi.org/10.1016/j.cmpbup.2024.100145  
22. Khalifa, M., & Albadawy, M. (2024). Using Artificial intelligence in academic writing and research: an  
essential productivity tool. ScienceDirect. https://doi.org/10.1016/j.cmpbup.2024.100145  
23. Lalisan, J. M. A., Espolong, N. P., Autor, A. L., Duran, I. D. Z., Belenario, R. J. R., Palawan, C. E. O.,  
Amba, J. C., & Salvador, R. P. (2026). Navigating artificial intelligence: Leveraging AI-powered tools  
Page 1324  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
to enhance academic performance of senior high school students. International Conference on  
Community Education and Philanthropy (ICCEPH). https://icceph.com/  
24. Madanchian, M., & Taherdoost, H. (2025b). The impact of artificial intelligence on research efficiency.  
Results in Engineering, 26, 104743. https://doi.org/10.1016/j.rineng.2025.104743  
25. Martin, T. (2022). A Literature Review on The Technology Acceptance Model. International Journal of  
Academic Research in Business and Social Sciences, 12(11), 2859 – 2884.  
26. Miranda, J. P. P., et al. (2026). AI in work-based learning: Understanding the purposes and effects of  
intelligent tools among student interns. arXiv preprint. https://arxiv.org/abs/2603.28786  
27. Miranda, J. P. P., et al. (2026). AI in work-based learning: Understanding the purposes and effects of  
intelligent tools among student interns. arXiv preprint. https://arxiv.org/abs/2603.28786  
28. OwlAiSolution. (2025). AI is constantly Improving—And that’s just the beginning. OWL AI.  
https://owlaisolutions.com/2025/01/13/ai-is-constantly-improving-and-thats-just-the-beginning  
29. Patterson, R. (2024). Examining generative artificial intelligence adoption in academia: A UTAUT  
perspective.  
Issues  
in  
Information  
Systems,  
25(3),  
238–251.  
https://www.iacis.org/iis/2024/3_iis_2024_238-251.pdf  
30. Perante, W. C., et al. (2025). Utilization of artificial intelligence tools in engineering education among  
HEIs in Eastern Visayas, Philippines. International Journal of Learning, Teaching and Educational  
Research. https://ijlter.org/index.php/ijlter/article/view/14771  
31. Perante, W. C., Oquino, V. H., Cidro, M. K. T., Perante, W. A., Barquin, G. M., & Gomba, F. E. (2025,  
November 21). Utilization of Artificial Intelligence Tools in Engineering Education among HEIs in  
Eastern Visayas, Philippines. Perante | International Journal of Learning, Teaching and Educational  
Research. https://ijlter.org/index.php/ijlter/article/view/14771?  
32. Popović Šević, N., Šević, A., Slijepčević, M., & Krstić, J. (2025). AI adoption in higher education:  
Exploring attitudes and perceived benefits between users and non-users. Online Journal of  
Communication  
and  
Media  
Technologies,  
15(4),  
Article  
e202528.  
https://doi.org/10.30935/ojcmt/17246  
33. Quimba, F. M. (2026). How ready are LGUs for AI adoption? Philippine Institute for Development  
Studies (PIDS). https://www.pids.gov.ph/  
34. ResearchGate. (2026). The influence of artificial intelligence on higher education in the Philippines.  
https://www.researchgate.net/publication/389316597  
35. Saflor, C. S. R. (2025). Modeling student acceptance of AI technologies in higher education: A hybrid  
SEM–ANN approach. Future Internet, 17(12), 581. https://doi.org/10.3390/fi17120581  
36. Saflor, C. S. R. (2025). Modeling student acceptance of AI technologies in higher education: A hybrid  
SEM–ANN approach. Future Internet, 17(12), 581. https://doi.org/10.3390/fi17120581  
37. Saif, N., et al. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived  
risk to predict ChatGPT adoption for assessment support: A survey with students.  
https://doi.org/10.1016/j.caeai.2024.100246  
38. Sambrano, L. C., Reyes, J. R. B., Fabro, R. B. B., Colar, C. K. F., & Medrano, V. B. (2025). The  
influence of artificial intelligence (AI) on library patronage among college students. International  
Journal  
of  
Research  
and  
Innovation  
in  
Social  
Science,  
9(7),  
141–152.  
https://doi.org/10.47772/IJRISS.2025.90700010  
39. Saputra, I., et al. (2023). Potential barriers, problems, and risks of utilizing artificial intelligence in  
education. ResearchGate.  
40. Sibug, V. B., et al. (2026). Exploring the adoption intention in using AI-enabled educational tools among  
pre-service teachers in the Philippines. arXiv preprint. https://arxiv.org/abs/2604.27346  
41. Sibug, V. B., et al. (2026). Exploring the adoption intention in using AI-enabled educational tools among  
pre-service teachers in the Philippines. arXiv preprint. https://arxiv.org/abs/2604.27346  
42. Stryker, C., & Kavlakoglu, E. (2026). Artificial intelligence: Autonomy, creativity, and problem  
solving. IBM. What Is Artificial Intelligence (AI)? | IBM  
43. Taheri, R., Nazemi, N., Pennington, S. E., Clark, J. A., & Dadgostari, F. (2025). Factors influencing  
educators’ AI adoption: A grounded meta-analysis review. Computers and Education: Artificial  
Intelligence, 9, 100464. https://doi.org/10.1016/j.caeai.2025.100464  
Page 1325  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
44. University of California, Davis. (2024). Beyond productivity: The power of AI as an accessibility tool.  
Technology  
Impact  
Report  
2024-2025.  
https://iet.ucdavis.edu/technology-impact-report-2024-  
2025/article/beyond-productivity-power-ai-accessibility-tool  
45. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information  
technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540  
46. Villarino, R. T. H. (2025). Artificial intelligence (AI) integration in rural Philippine higher education:  
Perspectives, challenges, and ethical considerations. International Journal of Educational Research and  
Innovation, 23. https://doi.org/10.46661/ijeri.10909  
47. Villarino, R. T. H. (2025). Artificial intelligence (AI) integration in rural Philippine higher education:  
Perspectives, challenges, and ethical considerations. International Journal of Educational Research and  
Innovation, 23. https://doi.org/10.46661/ijeri.10909  
48. Welcome  
To  
Zscaler  
Directory  
Authentication.  
(2025).  
Etcor.org.  
https://etcor.org/storage/iJOINED/Vol.%20IV(3)  
49. Williams, T. (2025). Integrating the adapted UTAUT model with moral obligation, trust and perceived  
risk to predict ChatGPT adoption for assessment support: A survey with students. Frontiers in  
Psychology,  
16,  
1619391.  
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1619391/full  
50. Xie, L., Jiang, Y., Chang, C.-N., Zeng, X.-Y., Hong, J., & Mo, F. (2025). How are faculty and college  
students embracing AI? — A multi-informant mixed method study. Computers and Education: Artificial  
Intelligence, 9, 100506. https://doi.org/10.1016/j.caeai.2025.100506  
Page 1326  
www.rsisinternational.org