GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER  EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS

CONFIDENCE OGBUGO; TAMARA PATRICK TAMARAKARE; CONFIDENCE OGBUGO; TAMARA PATRICK TAMARAKARE

Authors

CONFIDENCE OGBUGO ¹ Isaac Jasper Boro College of Education, Sagbama, Nigeria

TAMARA PATRICK TAMARAKARE ² College of Education, Sagabama,

Submitted: August 21, 2026 Original

Abstract

The introduction of generative artificial intelligence (AI) has greatly transformed research processes in higher education institutions. This paper analyses the potentialities, drawbacks, and broader implications associated with the use of generative AI tools for enhancing academic research productivity. Using modern research literature and policy documents, the paper explores the theoretical underpinnings of generative AI and discusses the growing application of this technology to literature review, academic writing, data analysis, ideation, and knowledge synthesis. Furthermore, the paper addresses how generative AI can increase research efficiency, accessibility, and collaboration among scholars, especially early career scholars and non-native English speakers. Despite these potentialities, the paper reveals some major issues that are associated with academic integrity, authorship attribution, algorithmic bias, misinformation, data privacy, and automation bias. The paper considers emerging policies and initiatives aimed at governing the implementation of AI tools in academic research. In addition to this, the paper discusses implications that arise from applying AI technology to teaching and learning and research in general. More specifically, the paper considers some issues concerning curriculum transformation and assessment redesign due to the impact of AI. Overall, generative AI provides considerable advantages in improving academic research productivity; however, it requires a careful balance between technological advancements and ethical, pedagogical, and institutional considerations. .

Keywords

Generative artificial intelligence academic research productivity higher education ChatGPT AI governance academic integrity scholarly communication

How to Cite

OGBUGO, C., TAMARAKARE, T. P. (2026). GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS. Isaac Jasper Boro College of Education Journal, 3(1), 194–207.

OGBUGO, CONFIDENCE, TAMARAKARE, TAMARA PATRICK. "GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS." Isaac Jasper Boro College of Education Journal, vol. 3, no. 1, 2026, pp. 194–207.

CONFIDENCE OGBUGO, TAMARA PATRICK TAMARAKARE. "GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS." Isaac Jasper Boro College of Education Journal 3, no. 1 (2026): 194–207.

OGBUGO, C., TAMARAKARE, T. P. (2026) 'GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS', Isaac Jasper Boro College of Education Journal, 3(1), pp. 194–207.

BibTeX (.bib) RIS (.ris)

Full text available via PDF download in the sidebar.

Figures & Tables

Figures and tables from this article are available in the full PDF version.

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic
parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness,
Accountability, and Transparency (FAccT) (pp. 610–623). https://doi.org/10.1145/3442188.3445922
Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education—A synthesis of the
literature and a future research agenda. Education and Information Technologies, 29, 21501–21522.
https://doi.org/10.1007/s10639-024-12723-x
in
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and
challenges
higher education.
https://doi.org/10.3390/educsci13111139
Education
Sciences,
13(11),
1139.
Committee on Publication Ethics (COPE). (2023). COPE position statement: Authorship and AI tools.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M.,
Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M., Al-Busaidi, K. A., Balakrishnan,
J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). So what if ChatGPT
wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative
conversational AI for research, practice and policy. International Journal of Information
Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
European Commission. (2024). Artificial Intelligence Act (AI Act).
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard
Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin,
R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical
framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and
Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023).
Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38.
https://doi.org/10.1145/3571730

Views

50
Top 70%

Downloads

26
Top 93%

Citations 28

Engagement Score

(Views × 1 + Downloads × 2 + Citations × 5)

GENERATIVE AI TOOLS AND ACADEMIC RESEARCH PRODUCTIVITY IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND IMPLICATIONS

Authors

Abstract

Keywords

How to Cite

Figures & Tables

References

Article Metrics

Share Article

Author Information

Related Articles