Navigating Ethical Boundaries: AI-Driven Data Collection and Analysis in Academic Research
DOI:
https://doi.org/10.69569/jip.2025.755Keywords:
Academic integrity, Artificial intelligence ethics, Data privacy, Research methodology, Student perceptionsAbstract
The integration of artificial intelligence into academic research has revolutionized data collection and analysis, yet raises critical ethical concerns about privacy, consent, and data integrity. This study investigates college students' perceptions and experiences regarding ethical boundaries in AI driven research methodologies in Manila. Employing a mixed-methods research design, the study surveyed 384 college students from selected universities in Manila who had completed at least one research course. Data were collected through a validated researcher-made questionnaire and semi structured interviews conducted over three months. Quantitative data were analyzed using descriptive statistics and inferential tests, while qualitative data were analyzed using thematic analysis to identify emerging ethical concerns. Results revealed that 78.40% of respondents expressed moderate to great concern about AI-mediated data privacy, with significant differences across academic disciplines (χ² = 24.56, p < .001). Students identified informed consent transparency (M = 4.21, SD = 0.87), algorithmic bias (M = 3.94, SD = 0.92), and data security (M = 4.35, SD = 0.76) as primary ethical considerations. The study concludes that while AI offers unprecedented research capabilities, educational institutions must establish comprehensive ethical frameworks and enhance student awareness of AI-related research ethics to ensure responsible implementation in academic settings.
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