The Promise and Paradox of AI in Doctoral Education
DOI:
https://doi.org/10.24059/olj.v30i2.5842Keywords:
Adaptive artificial intelligence (AI), support for doctoral students, doctoral programs, artificial intelligence and doctoral education, doctoral student attrition, online doctoral programsAbstract
This paper proposes a conceptual model of adaptive artificial intelligence (AI) support for doctoral education that operationalizes supported autonomy across three interconnected dimensions of doctoral student development: cognitive, affective, and social. Each dimension represents a distinct but overlapping area where AI can provide responsive scaffolding while preserving student agency and intellectual ownership. This framework provides a structured approach to understanding how AI tools can enhance rather than diminish doctoral development when thoughtfully integrated into mentorship and supervision structures. Challenges and ethical use are addressed along with implications for doctoral programs. We suggest a balanced use of AI, a clear framework or guidelines for ethical use, and doctoral student supervision (dissertation chair) to reduce feelings of isolation, student attrition, and use of students’ time.
References
Ali, A., & Kohun, F. (2007). Dealing with social isolation to minimize doctoral attrition – A four stage framework. International Journal of Doctoral Studies, 2, 33–49. https://doi.org/10.28945/56
Aitchison, C., & Mowbray, S. (2015). Doctoral writing markets: Exploring the grey zone. In S. E. Eaton (Ed.), Handbook of academic integrity (pp. 1–12). Springer, Singapore.
Akbar, M. N. (2025). Use of artificial intelligence tools by doctoral students: A mixed-methods explanatory-sequential investigation. Journal of Further and Higher Education, 49(7), 995–1013. https://doi.org/10.1080/0309877X.2025.2515135
Anttila, H., Pyhältö, K., & Tikkanen, L. (2024). Doctoral supervisors’ and supervisees’ perceptions on supervisory support and frequency of supervision – Do they match? Innovations in Education and Teaching International, 61(2), 288–302. https://doi.org/10.1080/14703297.2023.2238673
Archana, S. N., Renjith, V. R., Padmakumar, P. K., C., S., & Aboobaker, N. (2025). AI assisted learning and research: An exploratory study among university students and scholars. Discover Education, 4(1), 390. https://doi.org/10.1007/s44217-025-00814-x
Bandura, A. (1997). Self-efficacy: The exercise of control (Vol. 11). Freeman.
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 (pp. 610-623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
Bista, K., & Bista, R. (2025). Leveraging AI tools in academic writing: Insights from doctoral students on benefits and challenges. American Journal of STEM Education: Issues and Perspectives, 6, 32-47. https://doi.org/10.32674/9m8dq081
Boyd P., & Harding, D. (2025). Generative AI: Reconfiguring supervision and doctoral research. Buildings & Cities, 6(1), 560. https://doi.org/10.5334/bc.560
Bravata, D. M., Watts, S. A., Keefer, A. L., Madhusudhan, D. K., Taylor, K. T., Clark, D. M., Nelson, R. S., Cokley, K. O., & Hagg, H. K. (2019). Prevalence, predictors, and treatment of impostor syndrome: A systematic review. Journal of General Internal Medicine, 35, 1252–1275. https://doi.org/10.1007/s11606-019-05364-1
Breitenbach, E. (2023). Factors influencing doctoral program completion. In D. Dias & T. Candeias (Eds.).Academic performance - students, teachers and institutions on the stage. IntechOpen. https://doi.org/10.5772/intechopen.113824
Budapest Open Access Initiative. (2001). Budapest Open Access Initiative. https://www.budapestopenaccessinitiative.org/
Burrington, D., Madison, R. D., Schmitt, A., & Howell, D. (2022). Student perspectives on dissertation chairs’ mentoring practices in an online practitioner doctoral program. Online Journal of Distance Learning Administration, 25(4). https://ojdla.com/articles/student-perspectives-on-dissertation-chairs-mentoring-practices-in-an-online-practitioner-doctoral-program
Buss, R., Markos, A., & Marsh, J. (2025). Generative AI use in an EdD program: Informal, independent student use and formalized, instructor-directed use. Impacting Education: Journal on Transforming Professional Practice, 10(1), 42–48. https://doi.org/10.5195/ie.2025.476
Carter-Veale W. Y., Tull, R.G., Rutledge, J. C., Joseph, L. N. (2016). The dissertation house model: Doctoral student experiences coping and writing in a shared knowledge community. CBE Life Science Education, 15(3), article 34. https://doi.org/10.1187/cbe.16-01-0081
Chakraverty, D. (2019). Impostor phenomenon in STEM: Occurrence, attribution, and identity. Studies in Graduate and Postdoctoral Education, 10(1), 2–20. https://doi.org/10.1108/SGPE-D-18-00014
Choi, Y., Bouwma-Gearhart, J., & Ermis, G. (2021). Doctoral students’ identity development as scholars in the education sciences: Literature review and implications. International Journal of Doctoral Studies, 16, 89-125. https://doi.org/10.28945/4687
Ciampa, K., & Wolfe, Z. M. (2023). From isolation to collaboration: Creating an intentional community of practice within the doctoral dissertation proposal writing process. Teaching in Higher Education, 28(3), 487-503. https://doi.org/10.1080/13562517.2020.1822313
Clance, P. R., & Imes, S. A. (1978). The imposter phenomenon in high achieving women: Dynamics and therapeutic intervention. Psychotherapy: Theory, Research & Practice, 15(3), 241–247. https://doi.org/10.1037/h0086006
Craddock, S., Birnbaum, M., Rodriguez, K., Cobb, C., & Zeeh, S. (2011). Doctoral students and the impostor phenomenon: Am I smart enough to be here? Journal of Student Affairs Research and Practice, 48(4), 429–442. https://doi.org/10.2202/1949-6605.6321
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE.
Dysthe, O., Samara, A., & Westrheim, K. (2006). Multivoiced supervision of Master’s students: A case study of alternative supervision practices in higher education. Studies in Higher Education, 31(3), 299–318. https://doi.org/10.1080/03075070600680562
Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., Fiedorowicz, J. G., Smith, A. L., & Solmi, M. (2024). How to optimize the systematic review process using AI tools. JCPP Advances, 4(2), e12234. https://doi.org/10.1002/jcv2.12234
Frambaugh-Kritzer, C., & Petroelje Stolle, E. (2024). Leveraging artificial intelligence (AI) as a critical friend: The affordances and limitations. Studying Teacher Education, 21(2), 188–211. https://doi.org/10.1080/17425964.2024.2335465
Gardner, S. K. (2009). Special issue: The development of doctoral students–Phases of challenge and support. ASHE Higher Education Report, 34(6). https://doi.org/10.1002/aehe.v34:6
Ge, X. (2010). Scaffold ill-structured problem solving processes through fostering self-regulation-A web-based cognitive support system. Cognitive and Metacognitive Educational Systems: AAAI Fall Symposium (FS-10-01).
Grichko, V., Schamber, B., Hall, S., Termansen, K., Swank, D., Barker, D., & Lehmann, E. (2025). Dissertation 2.0: An action research study on adapting with AI. Impacting Education: Journal on Transforming Professional Practice, 10(1). https://doi.org/10.5195/ie.2025.468
Henriksen, D., Mishra, P., Woo, L., & Oster, N. (2025). The education doctorate in the context of generative artificial intelligence: Epistemic shifts and challenges to practical wisdom. Impacting Education: Journal on Transforming Professional Practice, 10(1), 73–79. https://doi.org/10.5195/ie.2025.485
Holmes, W., Bialik, M., & Fadel, C. (2019) Artificial intelligence in education. Center for Curriculum Design. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
Jacso, P. (2005). Google Scholar: The pros and the cons. Online Information Review, 29(2), 208–214. https://doi.org/10.1108/14684520510598066
Jairam, D., & Kahl, D. H., Jr. (2012). Navigating the doctoral experience: The role of social support in successful degree completion. International Journal of Doctoral Studies, 7, 311–329. https://doi.org/10.28945/1700
Jameson, C. M., Torres, K. M., Goodin, J. B., & Mohammed, S. F. (2023). Online doctoral students’ perception of autonomy support to progress in dissertation. International Journal of Online Graduate Education, 6(1). https://doi.org/10.65201/001c.141598
Johnson, N., Seaman, J., & Seaman, J. (2024). The anticipated impact of artificial intelligence on US higher education: A national study. Online Learning, 28(3), 9–33. https://doi.org/10.24059/olj.v28i3.4646
Jordan, K. (2019). From social networks to publishing platforms: A review of the history and scholarship of academic social networking sites. Frontiers in Digital Humanities: Section Digital Learning Innovations, 6(5), 1–15. https://doi.org/10.3389/fdigh.2019.00005
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
King, P. M., & Kitchener, K. S. (1994). Developing reflective judgment: Understanding and promoting intellectual growth and critical thinking in adolescents and adults. Jossey-Bass.
Krumsvik, R. J. (2025). Chatbots and academic writing for doctoral students. Education and Information Technologies, 30, 9427–61. https://doi.org/10.1007/s10639-024-13177-x
Lam, M. T., & McDiarmid, M. (2016). Increasing number of databases searched in systematic reviews and meta-analyses between 1994 and 2014. Journal of the Medical Library Association, 104(4), 284. https://doi.org/10.3163/1536-5050.104.4.006
Lesh, J., & Lancaster, J. (2024). AI-assisted academia: Unveiling doctoral students’ perspectives on dissertation in practice innovation. Bulletin of Educational Studies, 3(2), 113–126. https://doi.org/10.61326/bes.v3i2.288
Li, V. W. K., & Lin, L. H. F. (2025). View of doctoral students’ use of generative artificial intelligence (GenAI) in academic writing: Their engagement with AI-powered writing tools. Discourse and Writing, 35(2025), 114–143. https://journals.sfu.ca/dwr/index.php/dwr/article/view/1165/1017
Liechty, J. M., Liao, M., & Schull, C. P. (2009). Facilitating dissertation completion and success among doctoral students in social work. Journal of Social Work Education, 45(3), 481–497. https://doi.org/10.5175/JSWE.2009.200800091
Lin, C. C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(1), 41. https://doi.org/10.1186/s40561-023-00260-y
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Liu, J., & Jagadish, H. V. (2024). Institutional efforts to help academic researchers implement generative AI in research. Harvard Data Science Review, (Special Issue 5). https://doi.org/10.1162/99608f92.2c8e7e81
Lovitts, B. E. (2001). Leaving the ivory tower: The causes and consequences of departure from doctoral study. Rowman & Littlefield.
Lovitts, B. E. (2008). The transition to independent research: Who makes it, who doesn’t, and why. The Journal of Higher Education, 79(3), 296–325. https://doi.org/10.1080/00221546.2008.11772100
Manathunga, C. (2007). Supervision as mentoring: The role of power and boundary crossing. Studies in Continuing Education, 29(2), 207–221. https://doi.org/10.1080/01580370701424650
Maphoto, K.B., Sevnarayan, K., Mohale, N. E, Suliman, Z., Ntsopi, T.J., & Modoena, D. (2024). Advancing students’ academic excellence in distance education: Exploring the potential of generative AI integration to improve academic writing skills. Open Praxis, 16(2), 142–159. https://doi.org/10.55982/openpraxis.16.2.649
Martinez, D. R. (2024). “How artificial intelligence is shaping human needs: A Maslow’s ‘AI-approach.’” LinkedIn. https://www.linkedin.com/pulse/how-artificial-intelligence-shaping-human-needs-david-roldán-martínez-d3hgf/
Mensah, F. (2025). The 50% tipping point: Addressing doctoral student attrition through institutional innovation. Journal of College Academic Support Programs, 7(1), 50–54. https://doi.org/10.58997/7.1pp1
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Migliore, L. (2024). Reimagining doctoral education in social sciences: Cultivating a new archetype of scholar-practitioner in the age of artificial intelligence. Phoenix Scholar, 7(10), 10–15. https://www.phoenix.edu/research/publications/phoenix-scholar/vol-7-issue-1.html
Morton, J., Storch, N., & Thompson, C. (2014). Feedback on writing in the supervision of postgraduate students: Insights from the work of Vygotsky and Bakhtin. Journal of Academic Language & Learning, 8(1), A24–A36. https://journal.aall.org.au/index.php/jall/article/view/308
Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593
Оliinyk, I., Bulavina, O., Romanenko, T., Tatarnikova, A., & Smirnov, A. (2024). Artificial intelligence in developing doctoral students’ research competencies. EDUWEB, 18(3), 294–305. https://doi.org/10.46502/issn.1856-7576/2024.18.03.22
Oliveira, J., Murphy, T., Vaughn, G., Elfahim, S., & Carpenter, R. E. (2024). Exploring the adoption phenomenon of artificial intelligence by doctoral students within doctoral education. New Horizons in Adult Education and Human Resource Development, 36(4), 248–262. https://doi.org/10.1177/19394225241287032
Omodan, B. I. (2025). Redefining the role of supervisors in the era of artificial intelligence: Implications for hybrid postgraduate research governance. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2536534
Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92(3), 544. https://doi.org/10.1037/0022-0663.92.3.544
Pittaway, L. A., Tantawy, M. M., Corbett, A. C., & Brush, C. (2023). Improving doctoral educator development: A scaffolding approach. Journal of Management Education, 47(6). https://doi.org/10.1177/10525629231197219
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2293431
Ringo, D. S. (2025). The effect of generative AI use on doctoral students’ academic research progress: The moderating role of hedonic gratification. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2475268
Rockinson-Szapkiw, A. J., Spaulding, L. S., & Bade, B. (2014). Completion of educational doctorates: How universities can foster persistence. International Journal of Doctoral Studies, 9, 293–308. https://doi.org/10.28945/2072
Rong, H., & Chun, C. (2024). Digital education council global AI student survey 2024. Digital Education Council. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024
Shulman, L. (1981). Disciplines of inquiry in education: An overview. Educational Researcher, 10(6), 5-23. https://doi.org/10.3102/0013189X010006
Sowell, R., Zhang, T., Bell, N., & Redd, K. Completion and attrition: Analysis of baseline demographic data from the Ph.D. Completion Project. Council of Graduate Schools. Washington, DC. Available at https://cgsnet.org/wp-content/uploads/2022/01/phd_completion_and_attrition_analysis_of_baseline_demographic_data-2.pdf
Spaulding, L. S., & Rockinson-Szapkiw, A. (2012). Hearing their voices: Factors doctoral candidates attribute to their persistence. International Journal of Doctoral Studies, 7, 199–219. https://doi.org/10.28945/1589
Stubb, J., Pyhältö, K., & Lonka, K. (2011). Balancing between inspiration and exhaustion: PhD students experienced socio-psychological well-being. Studies in Continuing Education, 33(1), 33-50. https://doi.org/10.1080/0158037X.2010.515572
Suber, P. (2012). Open Access. MIT Press.
Sverdlik, A., Hall, N. C., McAlpine, L., & Hubbard, K. (2018). The PhD experience: A review of the factors influencing doctoral students’ completion, achievement, and well-being. International Journal of Doctoral Studies, 13, 361–388. https://doi.org/10.28945/4113
Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024). Against artificial education: Towards an ethical framework for generative artificial intelligence (AI) use in education. Online Learning, 28(2). https://doi.org/10.24059/olj.v28i2.4438
Taber, K. S. (2018). Scaffolding learning: Principles for effective teaching and the design of classroom resources. In M. Abend (Ed.), Effective teaching and learning: Perspectives, strategies and implementation (pp. 1–43). Nova Science Publishers.
Tensen, D., Grainger, P., & Graham, W. (2025). Using AI to generate formative feedback in doctoral education. Assessment & Evaluation in Higher Education, 1–17. https://doi.org/10.1080/02602938.2025.2536558.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089
Tyndall, D. E., Forbes III, T. H., Avery, J. J., & Powell, S. B. (2019). Fostering scholarship in doctoral education: Using a social capital framework to support PhD student writing groups. Journal of Professional Nursing, 35(4), 300–304. https://doi.org/10.1016/j.profnurs.2019.02.002
van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614, 224–226.
https://doi.org/10.1038/d41586-023-00288-7
Vekkaila, J., Virtanen, V., Taina, J., & Pyhältö, K. (2018). The function of social support in engaging and disengaging experiences among post PhD researchers in STEM disciplines. Studies in Higher Education, 43(8), 1439–1453. https://doi.org/10.1080/03075079.2016.1259307
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (Vol. 86). Harvard University Press.
Wang, Y., & Li, W. (2023). The impostor phenomenon among doctoral students: A scoping review. Frontiers in Psychology, 14(2023). https://doi.org/10.3389/fpsyg.2023.1233434
Wegener, C., Meier, N., & Ingerslev, K. (2016). Borrowing brainpower - sharing insecurities. Lessons learned from a doctoral peer writing group. Studies in Higher Education, 41(6), 1092–1105. https://doi.org/10.1080/03075079.2014.966671
Weller, M. (2011). The digital scholar: How technology is transforming scholarly practice. Bloomsbury Academic.
Welsh, M. A., & Dehler, G. E. (2013). Combining critical reflection and design thinking to develop integrative learners. Journal of Management Education, 37(6), 771–802. https://doi.org/10.1177/1052562912470107
Young, S. N., Vanwye, W. R., Schafer, M. A., Robertson, T. A., & Poore, A. V. (2019). Factors affecting PhD student success. International Journal of Exercise Science, 12(1), 34–45. https://doi.org/10.70252/CEJT2520
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Ziyanak, S., & Abrams, H. (2026). Communication strategies in AI-related plagiarism cases. Online Journal of Distance Learning Administrators, 59(1). https://ojdla.com/articles/communication-strategies-in-ai-related-plagiarism-cases
Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 259531. https://doi.org/10.3389/fpsyg.2023.1259531
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