AI evidence extraction

Factors Influencing AI Tool Adoption in Research Among Junior and Senior Students of QCU College of Computer Studies

Christian I. Ancog, Imma R. Estrada, Heila Arianne T. Longaquit, Sean Whymz M. Lombre, Nicole T. Mayo, Harold R. Lucero · International Journal of Latest Technology in Engineering Management & Applied Science · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.51583/ijltemas.2026.150500102

Methodology & findings

Study design

Quantitative-descriptive correlational survey design using an online questionnaire distributed through Google Forms to junior and senior students (3rd and 4th year) enrolled in BSIT, BSIS, and BSCS programs at Quezon City University College of Computer Studies.

Sample

N = 130, 4 groups

Primary method

Frequency and Percentage Distribution (to describe respondent profile and frequency of AI tool usage); Weighted Mean (to determine level of AI tool adoption and influence of factors using 5-point Likert Scale); Pearson Product-Moment Correlation Coefficient (r) (to determine significant relationships between influencing factors and adoption); Multiple Regression Analysis (to identify which factors significantly predict adoption and assess combined effects of multiple independent variables)

Main result

The study found that "Perceived Usefulness (3.45) emerged as the strongest influencer" of AI tool adoption among students, and concluded 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." The composite mean for AI adoption indicators was 3.18, indicating "Moderate Level of Adoption" where students view AI as "a supplementary assistant rather than a primary researcher."

Reports effect sizes.

Research paradigm

positivist/quantitative

Author conclusions

"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." The authors conclude 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."

Risk of bias

Selection bias: Disproportionate stratified sampling with over-representation of BSIT students (94.6%); Accessibility bias: Respondents selected based on 'availability and willingness to participate'; Response bias: Online survey format may exclude students without consistent internet access; Temporal bias: Data collection period was limited with constrained accessibility; Selection bias: Disproportionate stratified sampling with 94.6% BSIT students, not proportional to actual population; Sampling bias: Convenience-based selection of respondents based on availability and willingness to participate; Geographic limitation: Single institution (Quezon City University) reduces generalizability; Response bias: Online survey format may exclude students with limited digital access; Temporal bias: Data collected during a specific period with variable accessibility; Selection bias: Disproportionate stratified sampling with 94.6% BSIT students, limiting representation of BSIS and BSCS programs; Availability bias: Respondents selected based on availability and willingness to participate; Accessibility bias: Limited time and accessibility of participants during data gathering period

Limitations

The authors acknowledge that "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." Additionally, the study was "heavily represented by BSIT students (94.6%), primarily focusing on Software Development (50%)," limiting generalizability across academic programs.

Open questions raised

The paper identifies that "empirical studies examining students' actual experiences, perceptions, and patterns of AI tool utilization within specific Philippine higher education contexts remain limited" and that "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."
Limited empirical studies examining students' actual experiences and perceptions of AI tool utilization within specific Philippine higher education contexts
Gap between bottom-up technological adoption and absence of holistic institutional structures and ethical practices
Incomplete existing practices in higher education institutions that are mostly reliant on individual faculty members
Lack of critical analysis of technical soundness, user-friendliness, and alignment with international operational standards
Need for quantified baseline of student experience among upper-level students (Juniors and Seniors) engaged in capstone research

Extracted from: pdfAgreement 67%

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