An Examination of the Use of AI (Artificial Intelligence) Technology as Experienced by Scholarly Practitioners in an Educational Doctorate Program

Authors

DOI:

https://doi.org/10.5195/ie.2025.472

Keywords:

article dissertation, dissertation, Carnegie Project on the Education Doctorate (CPED), scholarly practitioner

Abstract

This study examined the applications and perceptions of AI tools in doctoral studies, focusing on their efficacy in enhancing research effectiveness. A survey found that most participants used AI tools in their doctoral studies (63%), with the majority of those users reporting some positive impact from their usage. The most indicated uses of AI were proofreading, researching scholarly articles for literature reviews, and the organization and structure of research. Future research may include a larger sample size and examine instruments for alignment with the program practices and curriculum to best capture responses that indicate participants' program-specific use of AI tools. The study concluded that AI tools have not yet been integrated into research within doctoral studies, and 47% of participants did not find them conducive to effectively communicating research findings in their doctoral work.

Author Biographies

Michelle Harris, University of Portland

Michelle Harris is the Senior Research Fellow with the Multnomah County Partnership for Education Research. She is currently in the final year of her doctoral studies at the University of Portland in the Doctor of Education Program. She is also the US Coordinator for the Schools of Mass Destruction. Her areas of research focus are Restorative Justice Practices in Education, Best Practices in Higher Education, and Best Practices in K12 Education. She has also served as the Mini-Grant Program Chair for the Peace and Justice Association (PJSA) since 2021. Michelle is a Public Speaker and Activist with a focus on Restorative Justice in the areas of Mass Incarceration, Education Justice, Nuclear Disarmament, and Climate Justice. 

Nicole Soriano

Nicole Soriano is an Assistant Professor in the School of Education at Morningside University. She received her EdD at the University of Portland. Her research interests include teacher education, educational equity, culturally relevant teaching, and LGBTQ+ inclusive pedagogies. She recently completed a three-year role as the Senior Research Fellow with the Multnomah County Partnership for Education Research (MCPER).

Nicole Ralston , University of Portland

Nicole Ralston is an Associate Professor in the School of Education at the University of Portland in Portland, Oregon. She received her PhD in Educational Psychology with an emphasis in Measurement, Statistics, and Research Design from the University of Washington in 2013. Dr. Ralston joined the University of Portland in 2014. She has previous experience teaching and instructional coaching in diverse schools; teaching courses at the University of Washington; and coordinating, managing, and collecting data for large research grants. At the University of Portland, Dr. Ralston teaches educational research and math methods courses, conducts research in the areas of partnership models, applied research in collaboration with districts, and teacher research, and co-directs the Multnomah County Partnership for Education Research.

References

Ahmed, M. A. (2023). ChatGPT and the EFL classroom: Supplement or substitute in Saudi Arabia’s eastern region. Information Sciences Letters, 12(7), 2727–2734.

Anik, M. H., Raaz, S. N. C., & Khan, N. (2023). Embracing AI assistants: Unraveling young researchers' journey with ChatGPT in science education thesis writing. Research Square https://doi.org/10.21203/rs.3.rs-3481002/v1

Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition (pp. 1–60). The New Media Consortium.

Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. Journal of the Association for Computing Machinery, 37(4), 1–44. https://scholar.google.com/scholar_url?url=https://arxiv.org/pdf/2303.04226.pdf%3Ftrk%3Dpublic_post_comment-text&hl=en&sa=T&oi=gsb-gga&ct=res&cd=0&d=12604090720681450553&ei=GMAEZrKYIJav6rQP2f-mMA&scisig=AFWwaeZ39LhEIok1wRJ5ihHRxhAq

Chandra, S., Shirish, A. & Srivastava, S. C. (2022). To be or not to be …human? Theorizing the role of human-like competencies in conversational artificial intelligence agents, Journal of Management Information Systems, 39(4), 969–1005

Chen, F. H., Looi, C. K., & Chen, W. (2009). Integrating technology in the classroom: a visual conceptualization of teachers’ knowledge, goals and beliefs. Journal of Computer Assisted Learning, 25(5), 470–488.

Chiu. (2023). The impact of Generative AI (GenAI) on practices, policies, and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2253861

Coley, M., Snay, P., Bandy, J., Bradley, J., Molvig, O. (2023). Teaching in the age of AI. Vanderbilt University Center for Teaching. https://cft.vanderbilt.edu/guides-sub-pages/

Crawford, J., Cowling, M., & Allen, K. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice, 20(3), 1–21. https://doi.org/10.53761/1.20.3.02

Creswell, J.W. (2013) Research design: Qualitative, quantitative, and mixed methods approaches. 4th Edition, SAGE Publications.

Edwards, C., Edwards, A., Spence, P. R., & Lin, X. (2018). I, teacher: Using artificial intelligence (AI) and social robots in communication and instruction. Communication Education, 67(4), 473–480.

Gillani, N., Eynon, R., Chiabaut, C., & Finkel, K. (2023). Unpacking the “Black Box” of AI in Education. Educational Technology & Society, 26(1), 99–111.

Harsyah, A. S. (2024). The exploration of students' literacy and attitude in using artificial intelligence for writing thesis [Masters thesis, Universitas Jambi]. https://repository.unja.ac.id/61346/2/cover.pdf

Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Educational Technology & Society, 21(3), 48–59.

Monika, M., Divyavarsini, V., Suganthan, C. (2023). A survey on analyzing the effectiveness of AI tools among research scholars in academic writing and publishing. International Journal of Advance Research and Innovative Ideas in Education, 9(6), 1292–1305.

Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95.

Saldaña, J. (2021). The coding manual for qualitative researchers. SAGE.

Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528.

Schwenke, N., Söbke, H., & Kraft, E. (2023). Potentials and challenges of chatbot-supported thesis writing: An autoethnography. Trends in Higher Education, 2(4), 611–635.

Siemens, G. & Gasevic, D. (2012). Guest Editorial: Learning and Knowledge Analytics. Educational Technology and Society. 15, 1–2

Tommer, E. (n.d.-a). Home - The Carnegie Project on the Education Doctorate. MemberClicks. https://www.cpedinitiative.org/

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

Wang, Y., Pan, Y., Yan, M., Su, Z., & Luan, T. H. (2023). A survey on ChatGPT: AI-generated contents, challenges, and solutions. IEEE Open Journal of the Computer Society, 4(1), 280–302.

Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116–129. https://www.jstor.org/stable/27032860

What is a large language model (LLM)? | cloudflare. Cloudflare. (n.d.). https://www.cloudflare.com/learning/ai/what-is-large-language-model/

Wilkerson, M. (n.d.). The framework ©. MemberClicks. Carnegie project for educational doctorate, https://cped.memberclicks.net/the-framework

Zhu, M., Bonk, C. J., & Berri, S. (2022). Fostering self-directed learning in MOOCs: Motivation, learning strategies, and instruction. Online Learning, 26(1), 153–173.

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Published

2025-02-07

How to Cite

Harris, M., Soriano, N. E., & Ralston, N. (2025). An Examination of the Use of AI (Artificial Intelligence) Technology as Experienced by Scholarly Practitioners in an Educational Doctorate Program . Impacting Education: Journal on Transforming Professional Practice, 10(1), 8–17. https://doi.org/10.5195/ie.2025.472

Issue

Vol. 10 No. 1 (2025): The Role of Generative Artificial Intelligence (AI) in Doctoral Research and Writing

Section

Themed-The Role of Generative Artificial Intelligence (AI) in Doctoral Research and Writing

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Copyright (c) 2025 Michelle Harris, Nicole Soriano, Nicole Ralston

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