Revisiting Doctoral Liminality in the Era of Artificial Intelligence (AI) and Digital Scholarship: Implications for Research Integrity and Supervision

Doctoral education is undergoing profound transformation due to the rapid integration of artificial intelligence (AI) and digital technologies; however, this shift has introduced significant challenges related to research competence, supervision, and research integrity. Despite the growing use of AI-assisted research tools, there remains limited empirical understanding of how these technologies interact with doctoral students’ transitional (liminal) experiences and institutional support systems to influence ethical research outcomes. In particular, concerns have emerged regarding the potential for AI misuse, weakened critical thinking, and gaps in supervisory guidance, raising critical questions about how doctoral training systems can effectively sustain research quality and integrity in the digital era. This study addresses this gap by providing a rigorous empirical and theoretical examination of doctoral liminality within the context of AI and digital scholarship. Specifically, it investigates how doctoral liminality, digital research environments, AI tool usage, and supervisory support influence research competence and responsible research conduct. Adopting a quantitative cross-sectional design, data were collected from 320 doctoral students across selected universities in Kenya and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal a robust and well-fitting model with strong explanatory power (R² = 0.62 for research competence; R² = 0.55 for research integrity). AI tool usage emerged as the strongest predictor of research competence (β = 0.36, p < 0.001), followed by doctoral liminality, digital research environment, and supervisory support. Research competence, in turn, exhibited a strong positive effect on responsible research conduct (β = 0.74, p < 0.001), confirming its central role as a pathway to ethical research behavior. Furthermore, research competence significantly mediates the relationship between technological and developmental factors and research integrity, while supervisory support plays both a direct and moderating role in enhancing the effective and ethical use of AI tools. By integrating technological, institutional, and behavioral perspectives, this study advances the conceptualization of doctoral liminality in the digital era. It contributes to emerging scholarship on AI-enabled research and provides policy-relevant insights for strengthening doctoral supervision, embedding AI governance, and promoting competence-based approaches to research integrity in increasingly digitized academic environments.

Doctoral Liminality; Artificial Intelligence (AI); Digital Scholarship; Research Competence

1. Abuya, J.O., Obino, P.O., and Owoche, P.O., (2022). Doctoral Liminality and Responsible Conduct Practices in Doctoral Research. American Journal of Education and Information Technology, www. Sciencepublishinggroup.com/j/ajeit). [Google Scholar] [Crossref]

2. Abimbola, S., Negin, J., Jan, S., & Martiniuk, A. (2020). Towards people-centred health systems: A multi-level framework for analysing primary health care governance in low- and middle-income countries. Health Policy and Planning, 35(Suppl 2), ii15–ii23. https://doi.org/10.1093/heapol/czaa076 [Google Scholar] [Crossref]

3. Aguinis, H., Ramani, R. S., & Cascio, W. F. (2023). Methodological practices in international business research: An after-action review of challenges and solutions. Journal of International Business Studies, 54(2), 203–217. https://doi.org/10.1057/s41267-022-00525-7 [Google Scholar] [Crossref]

4. Aitchison, C., & Mowbray, S. (2022). Doctoral supervision in a digital age: New challenges and opportunities. Higher Education Research & Development, 41(3), 867–880. https://doi.org/10.1080/07294360.2021.1882403 [Google Scholar] [Crossref]

5. Atun, R., de Andrade, L. O. M., Almeida, G., Cotlear, D., Dmytraczenko, T., Frenz, P., García, P., Gómez-Dantés, O., Knaul, F. M., Muntaner, C., & de Paula, J. B. (2021). Health-system reform and universal health coverage in Latin America. The Lancet, 385(9974), 1230–1247. https://doi.org/10.1016/S0140-6736(14)61646-9 [Google Scholar] [Crossref]

6. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 [Google Scholar] [Crossref]

7. Boud, D., & Lee, A. (2021). Changing practices of doctoral education. Studies in Higher Education, 46(7), 1341–1352. https://doi.org/10.1080/03075079.2019.1695111 [Google Scholar] [Crossref]

8. Castañeda, L., & Selwyn, N. (2021). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 18(1), 22. https://doi.org/10.1186/s41239-021-00254-5 [Google Scholar] [Crossref]

9. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155 [Google Scholar] [Crossref]

10. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating? Ensuring academic integrity in the era of generative AI. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148 [Google Scholar] [Crossref]

11. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage. [Google Scholar] [Crossref]

12. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101 [Google Scholar] [Crossref]

13. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). Wiley. [Google Scholar] [Crossref]

14. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., & Galanos, V. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]

15. Fanelli, D., Costas, R., & Larivière, V. (2021). Misconduct policies, academic culture, and career stage. Science and Engineering Ethics, 27(3), 1–20. https://doi.org/10.1007/s11948-021-00306-3 [Google Scholar] [Crossref]

16. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 [Google Scholar] [Crossref]

17. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. [Google Scholar] [Crossref]

18. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

19. Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education. Center for Curriculum Redesign. [Google Scholar] [Crossref]

20. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2023). NMC horizon report: Higher education edition. EDUCAUSE. [Google Scholar] [Crossref]

21. Kehm, B. M. (2020). Global university rankings and their impacts. European Journal of Education, 55(1), 1–3. [Google Scholar] [Crossref]

22. Kruk, M. E., Gage, A. D., Arsenault, C., Jordan, K., Leslie, H. H., Roder-DeWan, S., Adeyi, O., Barker, P., Daelmans, B., Doubova, S. V., & English, M. (2022). High-quality health systems in the SDG era. The Lancet Global Health, 6(11), e1196–e1252. https://doi.org/10.1016/S2214-109X(18)30386-3 [Google Scholar] [Crossref]

23. Lee, A. (2018). How are doctoral students supervised? Studies in Higher Education, 43(7), 123–135. [Google Scholar] [Crossref]

24. Lovitts, B. E. (2020). Leaving the ivory tower: The causes and consequences of departure. Rowman & Littlefield. [Google Scholar] [Crossref]

25. Manathunga, C., Pitt, R., & Critchley, C. (2022). Leading doctoral supervision in the digital era. Higher Education Research & Development, 41(5), 1425–1438. [Google Scholar] [Crossref]

26. Materu, P. (2021). Financing higher education in Africa. World Bank. [Google Scholar] [Crossref]

27. McAlpine, L., & Amundsen, C. (2021). Doctoral education: Research-based strategies. Springer. [Google Scholar] [Crossref]

28. Nitzl, C., Roldán, J. L., & Cepeda, G. (2016). Mediation analysis in PLS-SEM. Industrial Management & Data Systems, 116(9), 1849–1864. [Google Scholar] [Crossref]

29. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]

30. Resnik, D. B. (2020). The ethics of research with human subjects. Springer. [Google Scholar] [Crossref]

31. Selwyn, N. (2021). Education and technology: Key issues and debates (3rd ed.). Bloomsbury. [Google Scholar] [Crossref]

32. Stokel-Walker, C. (2023). AI tools and academic writing. Nature, 614, 620–622. [Google Scholar] [Crossref]

33. Taylor, S., Kiley, M., & Humphrey, R. (2021). Distributed doctoral supervision. Studies in Graduate and Postdoctoral Education, 12(2), 145–160. [Google Scholar] [Crossref]

34. Turner, V. (1969). The ritual process: Structure and anti-structure. Aldine. [Google Scholar] [Crossref]

35. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. [Google Scholar] [Crossref]

36. Van Gennep, A. (1960). The rites of passage. University of Chicago Press. [Google Scholar] [Crossref]

37. Veletsianos, G., & Shaw, A. (2022). Digital scholarship and education. British Journal of Educational Technology, 53(2), 221–234. [Google Scholar] [Crossref]

38. Wisker, G. (2020). The doctoral journey (2nd ed.). Palgrave Macmillan. [Google Scholar] [Crossref]

39. World Bank. (2022). Digital development overview. World Bank. [Google Scholar] [Crossref]

40. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2023). Systematic review of AI in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1–27. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study