Assessment of Artificial Intelligence in Students' Research
DOI:
https://doi.org/10.7719/irj.v27i1.1035Keywords:
Artificial Intelligence, student research, AI tools, higher education, literature reviews, data analysis, academic writing, challenges, opportunitiesAbstract
This study examines the growing influence of artificial intelligence (AI) tools on student research in higher education, focusing on both their opportunities and challenges. AI-powered writing assistants, literature review platforms, citation generators, and data analysis tools are increasingly integrated into academic workflows. Findings show that these tools enhance efficiency and quality by automating repetitive tasks such as grammar checking, citation management, and data organization. Students reported positive impacts, with weighted mean scores indicating agreement that AI improves clarity in writing (3.81), supports data analysis (3.71), and strengthens overall research quality (3.85). These opportunities allow learners to devote more time to higher-order skills such as critical analysis, argumentation, and creative idea generation. However, the study also highlights significant challenges. Students expressed concerns about limited training (3.65), difficulties in trusting AI-generated results (3.68), and the need for greater institutional support (3.70). Neutral responses regarding access barriers (3.28) and effective tool usage (2.97) suggest uneven readiness across the academic community. Ethical issues such as academic integrity, over-reliance on technology, and risks of unintentional plagiarism further complicate AI’s role in research. The finding shows the underscore of the dual nature of AI in higher education: it offers powerful opportunities to enhance research productivity and learning outcomes, yet it also presents risks that must be carefully managed. The study concludes by recommending comprehensive institutional guidelines, targeted training programs, and ethical frameworks to ensure responsible integration of AI.
References
Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971. https://pubs.acs.org/doi/abs/10.1021/acs.jchemed.3c00323
Ateeq, A., Alzoraiki, M., Milhem, M., & Ateeq, R. A. (2024, October). Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment. In Frontiers in education (Vol. 9, p. 1470979). Frontiers Media SA. https://doi.org/10.3389/feduc.2024.1470979
Bobula, M. (2024). Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. Journal of Learning Development in Higher Education, (30). https://doi.org/10.47408/jldhe.vi30.1137
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Copyright (c) 2026 Edelresa S. Juachon, Honorata D. Yaneza, Arthur V. Cabradilla, Azael May P. Jomadiao, Bonipart P. Salinas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Open Access. This article, published by JPAIR Multidisciplinary Research, is licensed under a Creative Commons Attribution-Noncommercial 4.0 International (CC BY-NC 4.0). You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material). Under the following terms, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
