Generative Artificial Intelligence and the doctoral student: procedural and ethical considerations.

This action research study explores 73 doctoral students' perceptions of using Generative Artificial Intelligence (GAI) throughout their research journey in one educational doctorate (Ed.D) program. The first phase employed surveys, while the second incorporated semi-structured focus group interviews based on the survey data from a diverse sample of students across educational disciplines currently enrolled in the university's educational leadership doctoral program. In the study's first phase, the survey quantified educators' familiarity with, attitudes towards, perceived challenges, ethical considerations, and benefits of using GAI in doctoral research. The exploration of GAI in this practitioner-inspired doctoral program has uncovered essential insights into integrating emerging technologies in advanced academic settings. This study has highlighted the complexities and considerations accompanying the use of GAI tools in doctoral research, underscoring the need for a balanced approach aware of both the advantages and the challenges inherent in their adoption and offers possible solutions to increase ethical usage of GAI.

The Role of Advanced Academic Literacy Development in Enhancing Academic Writing and Publishing: A Narrative Inquiry of Indonesian Doctoral Students. Objectives: Academic writing and publishing are crucial elements of doctoral education, yet many Indonesian doctoral students face significant challenges in developing the advanced academic literacy necessary for scholarly publishing. Methods: Employing a narrative inquiry approach, this study explores the lived experiences of five doctoral students from five universities in Indonesia. Data were gathered through semi-structured interviews. Findings: Findings reveal that structured academic literacy programs, mentorship, and exposure to scholarly discourse play a crucial role in improving students’ writing and publishing capabilities. However, persistent challenges, including linguistic barriers, institutional constraints, and the pressures of the ‘publish or perish’ culture, hinder their progress. Conclusion: The study underscores the need for higher education institutions to provide comprehensive academic literacy training, sustained mentorship, and collaborative research opportunities to support doctoral students in achieving academic publishing success. These insights contribute to a broader understanding of the interplay between academic literacy and research productivity, with implications for doctoral education policies and scholarly writing development programs. Keywords: academic literacy, academic writing, doctoral education, Indonesian higher education, scholarly publishing.

The adoption of artificial intelligence (AI) in academia is an emerging field of interest. However, there is scant literature that explores the phenomenon of AI adoption by graduate students in doctoral education. This study employs collaborative autoethnography to explore and better understand the nuances of how doctoral students experience AI technologies within academic pursuits. A critical analysis of data revealed that the collective researcher-participant experiences offered the primary overarching theme of adoption strategy, with four distinct subthemes: adoption fear, adoption resistance, adoption feasibility, and adoption ethics. The findings suggest a balanced approach to AI adoption depends on the development of comprehensive strategies that are informed by a deep understanding of both the technological capabilities and the human factors involved. We urge both doctoral students and educators involved in doctoral programs to think critically about these identified themes. For doctoral students, this analysis offers valuable insights into challenges associated with integrating AI technologies into formal learning environments, potentially enhancing a management strategy for their doctoral studies. Educators tasked with integrating and evaluating AI technologies for doctoral coursework may develop a deeper understanding of the challenges their students may encounter during the adoption process.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Doctoral students are increasingly adopting generative artificial intelligence (GenAI) tools in their daily academic activities. However, it remains unclear how GenAI influences doctoral training, particularly in terms of supervisory and peer interactions within PhD programmes. This qualitative study investigated the impact of GenAI adoption on doctoral students’ interactions with supervisors and peers within their immediate academic environments. Guided by activity theory as the theoretical framework, we conceptualise doctoral training as an academic activity system mediated by GenAI tools within specific social and cultural contexts. Through in-depth interviews and thematic analysis, this study examined the experiences of 20 doctoral students who were early adopters of GenAI at an Australian university between June and August of 2023. Two key tensions emerged from the analysis: first, the tensions arising from the dual nature of GenAI tools, characterised by their affordances and inherent limitations; second, the conflict between productivity-oriented research practices and traditional academic norms. These tensions further triggered interpersonal tensions over differing attitudes or stances towards GenAI and conflicting expectations regarding supervisory responsibilities among students, supervisors and peers. The findings reflect evolving power relations, interpersonal dynamics and academic socialisation in the context of GenAI integration. This study offers theoretical and empirical insights for rethinking doctoral supervision and training in the GenAI era. Implications for practice or policy: GenAI integration in doctoral education requires redefining the roles, responsibilities and relationships between students, supervisors and peers. Doctoral supervision should transition towards a more collaborative approach, emphasising co-learning, open communication and human-AI collaboration. Doctoral programmes need to develop clear institutional policies and structured training programmes for supervisors and students to facilitate effective GenAI use and minimise related tensions.

Aim/Purpose: The study set out to understand the challenges doctoral students experience at different systemic levels of doctoral education through the perspective of ethical principles. Background: Doctoral students experience various challenges on their journey to the degree, and as high dropout rates indicate, these challenges become critical for many students. Several individual and structural level aspects, such as student characteristics, supervisory relationship, the academic community as well national policies and international trends, influence doctoral studies, and students’ experiences have been researched quite extensively. Although some of the challenges doctoral students experience may be ethical in nature, few studies have investigated these challenges specifically from an ethics perspective. Methodology: The study drew on qualitative descriptions of significant negative incidents from 90 doctoral students from an online survey. The data were first analyzed using a reflexive thematic analysis, and then the themes were located within different systemic levels of doctoral studies: individual (e.g., doctoral student, the individual relationship with supervisor) and structural (e.g., the institution, faculty, academic community). Finally, the ethical principles at stake were identified, applying the framework of five common ethical principles: respect for autonomy, benefiting others (beneficence), doing no harm (non-maleficence), being just (justice), and being faithful (fidelity). Contribution: Understanding doctoral students’ experiences from an ethical perspective and locating these among the systemic levels of doctoral studies contributes to a better understanding of the doctoral experience’s complexities. Ethical considerations should be integrated when creating and implementing procedures, rules, and policies for doctoral education. Making the ethical aspects visible will also allow universities to develop supervisor and faculty training by concretely targeting doctoral studies aspects highlighted as ethically challenging. Findings: In doctoral students’ experiences, structural level ethical challenges out-weighed breaches of common ethical principles at the individual level of doctoral studies. In the critical experiences, the principle of beneficence was at risk in the form of a lack of support by the academic community, a lack of financial support, and bureaucracy. Here, the system and the community were unsuccessful in contributing positively to doctoral students’ welfare and fostering their growth. At the individual level, supervision abandonment experiences, inadequate supervision, and students’ struggle to keep study-related commitments breached fidelity, which was another frequently compromised principle. Although located at the individual level of studies, these themes are rooted in the structural level. Additionally, the progress review reporting and assessment process was a recurrent topic in experiences in which the principles of non-maleficence, autonomy, and justice were at stake. Recommendations for Practitioners: Going beyond the dyadic student-supervisor relationship and applying the ethics of responsibility, where university, faculty, supervisors, and students share a mutual responsibility, could alleviate ethically problematic experiences. Recommendation for Researchers: We recommend that further research focus on experiences around the ethics in the progress reporting and assessment process through in-depth interviews with doctoral students and assessment committee members. Impact on Society: Dropout rates are high and time to degree completion is long. An ethical perspective may shed light on why doctoral studies fail in efficiency. Ethical aspects should be considered when defining the quality of doctoral education. Future Research: A follow-up study with supervisors and members of the academic community could contribute to developing a conceptual framework combining systemic levels and ethics in doctoral education.

The rapid spread of generative artificial intelligence (GenAI) is reshaping doctoral education and raising new questions about its implications for research practice and performance. Using large-scale data from the 2025 Chinese National Doctoral Graduates Survey, this study examines how doctoral students adopt GenAI and how different modes of engagement are associated with research performance. Latent class analysis identifies three usage modes: Textual Tool (51.4%), Task Assistant (43.8%), and Thought Partner (4.8%). Ordered logit results show that deeper modes of engagement are more likely among students with STEM backgrounds, interdisciplinary experience, intrinsic motivation, and lower satisfaction with supervisory guidance. Associations between GenAI use and research performance vary systematically by mode and discipline. Textual Tool use is mainly linked to gains in publication productivity, whereas the more comprehensive integration represented by Thought Partner is associated with improvements in dissertation quality. Disciplinary contexts further condition these patterns. In abstract and conceptually oriented fields such as the humanities and mathematics, GenAI use is largely concentrated on text-related functions and is associated with modest, primarily quantity-based outcomes. By contrast, in computational and experimental domains including economics, physics, computer science, and engineering, deeper engagement through Task Assistant and Thought Partner use is more consistently associated with higher research productivity and quality indicators. Overall, the findings highlight that the implications of GenAI for doctoral research depend on how it is integrated into disciplinary research practices, underscoring the need for discipline-sensitive approaches to GenAI governance in doctoral education.

Aim/Purpose: The conventional model of doctoral education, centered on conducting original research within an apprentice-supervisor framework, has evolved progressively toward alternative models, including shorter duration, the incorporation of a teaching component, a collaborative approach, and an emphasis on practice-based problem-solving. Using bibliometric methodologies, this paper aims to examine the intellectual landscape of doctoral education and supervision research over the past decade by identifying core literature, influential works, and key research trends, thereby supporting knowledge growth, innovation, and informed decision-making. Background: Doctoral education and its related supervision have undergone multi-dimensional transformations over the past two decades, leading to increased scholarly interest and an expanding body of literature. Despite this growth, we still know little about the intellectual structure of research within the field. Furthermore, hitherto few bibliometric and meta-analytic reviews have been conducted, leaving the conceptual landscape of doctoral education and supervision research under-mapped and difficult to navigate. Methodology: The study employs bibliometric methodologies, specifically citation and co-citation analyses, as well as bibliographic coupling, to rigorously and objectively map the intellectual structure of doctoral education and supervision research. These methods provide quantitative insights into relationships between documents, authors, and journals, facilitating the identification of research clusters and networks. Contribution: Drawing on a corpus of over 2,000 journal articles, the study analyses and maps the intellectual structure of the field, spotlighting influential researchers, institutions, and networks. The study identifies the areas where assimilation has taken place as a guide for future research. Further value is derived by identifying areas where there has been limited assimilation, and conclusions are drawn as to why such limited assimilation has occurred. Logical conclusions are then drawn regarding where future assimilation within doctoral education and supervision is needed, and how the field can make distinctive contributions to this literature. These contributions point to more effective collaboration, policymaking, and funding decisions within doctoral education and supervision research. Findings: The interdisciplinary nature of doctoral education and supervision is evidenced by the distribution of journal articles, which suggest a broad range of research interests, with a significant concentration in the social sciences. The findings have implications for various stakeholders, including doctoral students, educators, and policymakers, who seek insights into past research and contribute to an understanding of applying bibliometric review methodologies to capture insights into the intellectual structure of research fields. The study identifies a rapidly growing body of literature, reflecting an increasing interest in research on doctoral education and supervision. Citation and co-citation analyses reveal key academic communities and emerging trends within doctoral education and supervision research. Recommendation for Researchers: The paper represents a call to action, recommending that researchers continue to engage with rigorous bibliometric review methodologies to deepen their understanding of the field of doctoral education and supervision, moving beyond intellectual structure to intellectual content. Impact on Society: The study enhances institutional decision-making in doctoral education and supervision, supporting the development of effective teaching and research environments. Mapping intellectual communities fosters collaboration and knowledge sharing, ultimately benefiting doctoral students, educators, and policymakers. Future Research: While bibliometric analysis provides a broad overview, systematic reviews and meta-analyses could explore other diverse perspectives and methodologies, contributing to the intellectual development of the field. Despite the interdisciplinary nature of doctoral education, publications are predominantly found in the social sciences, which contrasts with the global dominance of STEM doctoral programs. Expanding research beyond social sciences is essential, as supervision practices vary across disciplines, with different approaches, actors, and dynamics shaping doctoral training. Recognizing these differences reinforces the need for tailored approaches – one size does not fit all.

This paper examines how artificial intelligence (AI) tools enhance feedback practices in doctoral education by providing a supplementary source of formative assessment. It explores the use of Generative AI alongside Grainger’s Formative Assessment Criteria-Based Tool (F.A.C.T.) in a single-subject case study of a pre-confirmation doctoral candidate at an Australian university. The study employs a naturalistic and interpretive approach with a sequential design, exploring interactions between a doctoral student and ChatGPT across multiple sessions where the AI tool evaluated a pre-confirmation thesis. Data collection included deidentified summarised feedback received from AI and an independent academic reviewer. Findings revealed that AI-generated feedback, guided by Grainger’s F.A.C.T. demonstrated thematic alignment with a human reviewer in identifying areas needing improvement, particularly regarding theoretical foundation and contribution to knowledge. However, the human reviewer provided contextually nuanced and discipline-specific feedback with more actionable suggestions. The study illustrates how, when coupled with formative rubrics, generative AI may serve as a supplementary feedback mechanism that may reduce power imbalances inherent in supervisor/reviewer-student relationships while providing expedient formative feedback. This research contributes to understanding how reflective practice in doctoral education may be enhanced through AI integration, addressing gaps in feedback literacy, socialisation, and standardised assessment parameters in doctoral contexts.

Our presentation discusses the practices and findings from a PhD workshop series at the Academic Resource Centre, Umeå University Library, Sweden. The partnership between librarians, writing tutors/researchers in supporting PhD research has recently become a new reality with Information Literacy courses offered as tools for resources and searching (Hassani, 2015; Paasio &amp; Hintikka, 2015; Garson,2016). The insights from our course contribute to this literature by re-conceptualizing “academic literacy”, including Information Literacy, in doctoral education.&#x0D; Adopting Academic Literacies (Lea &amp; Street, 1998, 2006) as our workshops’ underlying framework, we propose literacy beyond individual, transferable cognitive skills of writing and reading. Rather, it is an interrelated, dynamic, and situated set of knowledge, skills, and personal attributes that help PhD students acculturate into disciplinary discourses, the academic community, and wider social contexts.&#x0D; Our course approaches literacy holistically as comprising Research competence, Information literacy, and Academic English, with consideration to social processes (power, identity, and authority). The workshops cover critical reading, the literature review, writing abstract, communicating research and writing papers, but the PhD students are also encouraged to make sense of their writing by having critical, inquiry-based reflections about themselves, academia, and social discourses.&#x0D; The results from the first three workshop seasons emphasize knowledge co-creation – between academic librarians and researchers, and between workshop instructors and PhD students, as one key principle in developing academic literacies. The findings indicate that Information Literacy can be seen beyond tools and resources but rather a springboard that stimulates PhD students’ critical thinking in their becoming researchers. The positive feedback from the participants also gives the rationale for the expanding roles of the library (Delaney and Bates, 2018). These workshops have strengthened our belief that collaboration is one important strategy for librarians and writing tutors/researchers to acquire the skills of the future.&#x0D; Authors&#x0D; Mai Trang Vu, PhD, works at the Academic Resource Centre, Umeå University Library, and Department of Language Studies, Umeå University, Sweden, trang.vu@umu.se.&#x0D; Magnus Olsson, Academic Librarian, Academic Resource Centre, Umeå University Library, Sweden, magnus.olsson@umu.se.&#x0D; &#x0D; References&#x0D; Delaney, Geraldine, &amp; Bates, Jessica. (2018). How Can the University Library Better Meet the Information Needs of Research Students? Experiences from Ulster University. New Review of Academic Librarianship, 24(1), 63-89.&#x0D; Garson, D. S. (2016). Doctoral students becoming researchers: An innovative curriculum. Nordic Journal of Information Literacy in Higher Education, 8(1).&#x0D; Hassani, A. E. (2015). The role of Information Literacy in higher education: An initiative at Al Akhawayn University in Morocco. Nordic Journal of Information Literacy in Higher Education, 7(1).&#x0D; Lea, M. &amp; Street, B. (1998). Student writing in higher education: An academic literacies approach. Studies in Higher Education, 23(2), 157–72.&#x0D; Lea, M. &amp; Street, B. (2006). The ‘Academic literacies’ model: Theory and applications. Theory into Practice, 45(4), 368–77.&#x0D; Paasio, A-L, Hintikka, K. (2015). An Information Literacy course for doctoral students: Information resources and tools for research. Nordic Journal of Information Literacy in Higher Education, 7(1).

Social work education grounded in social work practice has been recently challenged to examine the role of science in its history, core constructs and domains, philosophical underpinnings, and graduate curriculum. Doctoral education has been added to the scrutiny at the recent Science in Social Work Roundtable in Doctoral Education. Based on Lev Vygotsky’s scaffolding approach to facilitate learning, this article discusses doctoral student education with the four stages of (1) positioning doctoral students for a scientific career, (2) promoting doctoral students as scholars of the academy, (3) promulgating doctoral students as scientists of the profession, and (4) preparing doctoral student to be stewards of the discipline.

Generative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI's impact on higher education has the potential to change students' learning experience as well as faculty's teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research. This study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation. The study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions. The data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians' professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians' expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises. This study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed.

Doctoral education in the United States forms a huge and diverse enterprise. Seen from the outside, American graduate education is often hailed as the "gold standard" to which other nations and academic institutions aspire. From the inside, however, doctoral education faces many challenges. This article provides some basic information concerning doctoral education in the United States and will focus attention on the challenges facing doctoral education. While some U.S. analysts would disagree, my basic perspective is that American graduate education in general and doctoral education in particular is largely successful and effective. The system of doctoral education as it has evolved in the United States over the past century and a half serves both the academic system and society reasonably well. Indeed, many of the problems facing doctoral education are engendered by the system's success. Some of the challenges facing doctoral education relate to broader societal forces while others are internal to the academic system. Doctoral education needs to be viewed alongside broader trends in American higher education, and especially graduate education.The doctorate, especially the Ph.D., is the pinnacle of a large and complex higher education system. This essay focuses mainly on the Ph.D. degree, the research-oriented doctorate, and not on the increasingly important professional doctorates such as the doctor of business administration (DBA), the doctor of law (JD), the doctor of education (Ed.D.), and others, although some attention will be paid to these degrees. Doctoral study also is related to graduate education generally-master's degrees in many fields including the traditional arts and sciences and in numerous professional fields (Conrad, Haworth, and Millar 1993). Postdoctoral study is also not considered in detail in this discussion, although in many fields in the physical and biomedical sciences a postdoctoral research appointment is increasingly considered part of research training and is quite common. Doctoral education cannot be separated from cither the American academic research enterprise or the arrangements for teaching large numbers of undergraduates in the larger research-oriented universities (Graham and Diamond 1997). Doctoral students, especially in the sciences, are an integral part of the research system. They provide the personnel at relatively low cost who do much of the research under the supervision of senior professors. The research grants provided by government agencies such as the National Science Foundation and many others, by private philanthropic foundations, and increasingly by corporations are the sources of funding for graduate assistants who work on research while studying for their doctorates. In many cases, dissertation topics relate to the funded research. This system of financial support for doctoral study and basic research works well for American higher education. It ensures financial support for students as well as faculty mentorship and supervision for them, and it ensures a steady source of labor for research projects. These research funds are awarded on a competitive basis, and as a result the bulk of financial support for doctoral students in the sciences goes to the prestigious research-oriented universities. Doctoral students in all disciplines, but especially in the social sciences and humanities, serve as teaching assistants and sometimes as lecturers for undergraduate courses. In return for modest stipends and tuition scholarships, doctoral students provide much of the teaching in large undergraduate courses. Typically, they work under the supervision of a senior professor and conduct discussion sections for students as well as helping with grading and evaluation. In some cases, advanced doctoral students independently teach courses. In the sciences, doctoral students may help with laboratory supervision. Funds for teaching assistants generally come directly from the university. …

Generative artificial intelligence (AI) changes the picture of graduate education by providing personalized learning, automated feedback, intelligent research assistants, and automated content creation (George, 2023). AI tools will support doctoral students in text generation, language translation, responding to academic queries, and data collection and analysis and encourage self-learning and thinking development (Rasul et al., 2023; Zou &amp; Huang, 2023). They also would be helpful for doctoral students working as teaching assistants and aiding in daily problems (Can et al., 2023; Parker et al., 2024). However, the rise of AI tools also leads to considerations of academic integrity, over-reliance on AI, misinformation, and the potential biases embedded in algorithms (George, 2023; Rasul et al., 2023). Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey. The panel invites a diverse group of doctoral students/candidates to share how AI tools can facilitate data collection and analysis and their critical understanding of AI systems. Manar Alsaid will talk about using AI and machine learning to detect complex misinformation on social media. The talk aims to enhance our understanding of misinformation and reduce its negative impacts. This presentation will provide valuable insights for research on misinformation and information literacy. Adam Eric Berkowitz will introduce the black-box tinkering method that experimentally discerns how AI systems operate. The method enhances the transparency of AI systems, challenging the technocratic paradigm. With three examples, Berkowitz encourages attendees to learn what black-box tinkering is, how to identify cases using it, and potential opportunities to incorporate it in research. Anisah Herdiyanti will share insights from a study comparing transcripts generated by Otter.ai and Zoom Meetings. The presentation will highlight both the benefits and challenges of AI-based notes and transcription software, including technical concerns and the convenience of automated result delivery. The audience will enhance their understanding of AI tools in qualitative data transcribing and the ethical considerations in the process. Rebecca Bryant Penrose will showcase the use of HeyGen, an AI-based video generator and translation tool, in an international interview project between students at California State University Bakersfield and a Ukrainian artist/author. The presentation will increase awareness of the potential use of AI-based video and help researchers overcome language barriers in data collection. The panel will last 90 minutes, including a 5-minute introduction and a 5-minute wrap-up. Each panelist will have 10 minutes to present their topics, followed by 5-minute Q&amp;As. A 25-minute moderated roundtable discussion will follow the panelists’ presentations to explore the potential use of different AI tools in research, including ChatGPT and AI-powered article summarizers. The panel’s learning outcomes include (1) Identifying challenges and opportunities to incorporate AI tools in research and study and (2) Explaining how to interact with AI tools to improve efficiency in research. It also provides a platform for doctoral students to share their knowledge of how AI changes research approaches and networks with each other.

BackgroundThe characteristics of nursing doctoral programs and the doctoral students’ experience have not been thoroughly investigated. Hence, this study aimed to describe the characteristics of nursing doctoral programs in East and South East Asian (ESEA) countries and regions from the views of doctoral program coordinators, and to explore the students’ experiences of and satisfaction with their doctoral nursing program.MethodsA cross-sectional survey was conducted using two self-designed questionnaires, one focusing on PhD program coordinators and the other on doctoral students. Characteristics of the nursing doctoral programs focused on program characteristics, faculty characteristics, career pathways for graduates, and challenges for nursing doctoral education. Doctoral students’ assessment of study experiences included quality of supervision, doctoral training programs, intellectual/cultural climate of institutions, general facilities/support, and the overall study experience and satisfaction.ResultsIn the PhD coordinators survey, 46 institutions across nine ESEA countries and regions participated. More than half of nursing departments had academic members from other health science disciplines to supervise doctoral nursing students. The majority of graduates were holding academic or research positions in higher education institutions. Faculty shortages, delays in the completion of the program and inadequate financial support were commonly reported challenges for doctoral nursing education. In the students’ survey, 193 doctoral students participated. 88.3% of the students were satisfied with the supervision they received from their supervisors; however, 79% reported that their supervisors ‘pushed’ them to publish research papers. For doctoral training programs, 75.5% were satisfied with their curriculum; but around half reported that the teaching training components (55.9%) and mobility opportunities (54.2%) were not included in their programs. For overall satisfaction with the intellectual and cultural climate, the percentages were 76.1 and 68.1%, respectively. Only 66.7% of the students felt satisfied with the facilities provided by their universities and nursing institutions.ConclusionDoctoral nursing programs in most of the ESEA countries value the importance of both research and coursework. Doctoral nursing students generally hold positive experiences of their study. However, incorporating more teaching training components, providing more opportunities for international mobility, and making more effort to improve research-related facilities may further enhance the student experience. There is also a need to have international guidelines and standards for quality indicators of doctoral programs to maintain quality and find solutions to global challenges in nursing doctoral education.