In the Fall of 2023, the University’s Department of Educational Leadership, still experiencing the post effects that the global pandemic had on student retention, faced a particular manifestation of this national crisis. Nearly 100 students had completed all required coursework but remained stalled at the dissertation stage, with many approaching or exceeding the university’s ten-year maximum completion timeline. The global pandemic had further complicated this situation, with numerous students requesting extensions due to unprecedented personal and professional challenges.
This crisis presented a significant challenge and a unique opportunity. Rather than allowing these students to drop out or face automatic dismissal, the department developed an innovative intervention: the All But Dissertation (ABD) Cohort Program. This structured, comprehensive approach to dissertation support was designed to address the barriers that prevent students from completing their doctoral degrees.
The ABD Boot Camp Cohort Program was grounded in Vincent Tinto’s Theory of Student Involvement and Departure (
The theory identifies two distinct but interconnected systems within higher education institutions: academic and social systems. Academic integration encompasses both structural elements (academic performance, intellectual development) and normative elements (identification with institutional values and goals). Social integration similarly includes both structural aspects (extracurricular activities, peer interactions) and normative dimensions (identification with social communities within the institution). Tinto argues that successful integration into both systems increases institutional commitment, which in turn reduces the likelihood of departure.
Successful academic integration includes students’ academic performance, intellectual skill development, and satisfaction with institutional academic systems. For doctoral students, this involves transitioning from structured coursework to independent research, developing scholarly identity, mastering specialized methodologies, and maintaining productive relationships with advisors and committee members.
Social integration refers to students’ connections within the academic community, including relationships with faculty and peers, participation in disciplinary communities, and development of professional networks. At the doctoral level, social integration becomes particularly challenging during the dissertation phase, when students often experience isolation and reduced peer contact due to the individualized nature of dissertation work.
Tinto’s theory is particularly relevant to doctoral education because it recognizes that the factors influencing persistence change over time and across different educational phases. During the dissertation phase, traditional support structures may weaken, making students particularly vulnerable to departure. The theory suggests that interventions must address both academic integration (through structured support for research and writing skills) and social integration (through peer communities and faculty mentorship) to effectively support student persistence.
The theory’s application to doctoral education reveals challenges. Unlike undergraduate students, doctoral students must navigate the transition from dependent learner to independent learner/scholar while managing multiple commitments such as careers and families. The dissertation phase represents the most vulnerable period, where both academic and social supports may diminish significantly while academic demands increase significantly.
Research identifies several factors contributing to doctoral attrition. Personal barriers include stress, social isolation, procrastination, and perfectionism (
According to research, workshops focusing on dissertation writing and research skills support dissertation completion (
The integration of artificial intelligence tools in the research and writing process has proven to provide support for students. Recent research has examined the ethical use of AI tools including ChatGPT, Grammarly, and other language models for literature review enhancement, brainstorming, and writing assistance. Training emphasizes that AI tools should supplement rather than replace original thinking and scholarly work, with all AI-assisted content requiring proper attribution and human oversight (
This study employed a mixed-methods approach to evaluate the implementation and outcomes of the ABD Boot Camp Cohort Program. Data sources included: 1) Completion tracking data for all 100 eligible ABD students; 2) Program participation records including monthly workshop attendance; 3) Student satisfaction surveys administered at program completion; 4) Faculty feedback through structured interviews.
The satisfaction survey instrument consisted of 15 items using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) measuring perceived program effectiveness, peer support quality, faculty mentorship satisfaction, and overall program impact. The survey achieved a 78% response rate (31 of 40 participants). Completion data were analyzed using descriptive statistics, comparing pre-program and post-program graduation rates. All data collection procedures were reviewed and deemed exempt by the institutional review board as part of routine program evaluation activities.
The program targeted approximately 100 ABD students from the Educational Leadership doctoral program at Tennessee State University who had completed all required coursework but remained stalled at the dissertation stage. Students were eligible if they had been ABD for at least one semester and were approaching or had exceeded the university’s maximum completion timeline. Participation was voluntary, with students required to commit to attending monthly sessions and completing assigned deliverables. Of the 100 eligible students contacted, 40 chose to participate in the program, while 60 opted not to participate and faced potential dismissal from their programs.
Based on program enrollment data, participants represented both concentration areas within the Educational Leadership program: PreK-12 Administration and Higher Education Leadership. The broader program demographics during this period showed 72% female and 28% male students, with 70% identifying as Black/African American, 28% as White, and 2% as Two or More Races. The average age of doctoral students in the program was 49.1 years, with ages ranging from 35 to 72 years. Many participants were employed full-time in educational positions while completing their doctoral studies.
The ABD Cohort Program was developed as a comprehensive intervention targeting both the academic and social integration needs of students. The program consisted of monthly workshops held from January through November 2024, each focusing on specific aspects of the dissertation process.
Workshop topics were strategically sequenced to mirror the dissertation development process: literature review techniques, problem statement development, research design selection, data collection methods, analysis procedures, and defense preparation. Each session was recorded and made available to all participants, creating a permanent resource library.
The ABD Boot Camp program demonstrated significant success in supporting doctoral completion. Of the 40 participating students, nearly 50% (20 students) either completed their dissertations or were on track for completion by the December 2025 deadline. This completion rate compares favorably to the pre-intervention institutional graduation rate of 48% and exceeds the national average of 56% for EdD programs in Educational Leadership (
Metric |
ABD Rate |
Pre Rate |
Completion/On-Track Rate |
50% |
48% |
Program Duration |
11 months |
Variable |
Support Structure |
Workshops |
Traditional Advising |
Monthly workshop attendance varied throughout the program year, with the highest participation (73%, n = 35) occurring during the January Literature Review session featuring AI integration techniques. Attendance patterns showed strong engagement during critical dissertation development phases: Problem Statement development (31%, n = 15), Research Design workshops (52%, n = 25), and Data Collection training using Qualtrics (35%, n = 17). Lower attendance in later months reflected natural progression as students advanced through different dissertation stages (
Month |
Workshop Topic |
(n) |
Attendance |
January |
Literature Review |
35 |
73% |
February |
Problem Statement |
15 |
31% |
March |
Chapter 2 Outline |
24 |
50% |
April |
Research Design |
25 |
52% |
May |
Proposal Design |
12 |
25% |
June |
Data Collection—Qualtrics |
17 |
35% |
July |
Data Analysis |
12 |
25% |
August |
Writing Conclusions |
4 |
8% |
September |
Dissertation Refinement |
6 |
13% |
October |
Defense Preparation |
7 |
15% |
November |
|||
December |
The satisfaction survey (n = 31, 78% response rate) revealed high levels of program satisfaction across all measured domains. On the 5-point Likert scale, students reported strong agreement with statements about program effectiveness (M = 4.3, SD = 0.7), quality of peer support (M = 4.2, SD = 0.6), and faculty mentorship satisfaction (M = 4.4, SD = 0.5). Overall program impact received the highest rating (M = 4.5, SD = 0.4), with 87% of respondents rating the program as “very effective” or “extremely effective” in supporting their dissertation progress.
Of the 60 eligible students who chose not to participate in the ABD Boot Camp, preliminary data suggests significantly lower completion rates. While final outcomes will be assessed in January 2026, early indicators show that fewer than 20% of non-participants have made substantial progress toward dissertation completion during the same timeframe, highlighting the potential effectiveness of the structured intervention.
Currently, nearly 20 of the original students are on-track to complete their EdD degrees by the December 2025 deadline.
The program’s success can be attributed to its comprehensive approach to both academic and social integration, the core components of Tinto’s theoretical framework. The results demonstrate that structured interventions addressing both academic and social integration needs can significantly improve doctoral completion rates. Academic integration was addressed through structured workshops providing specific research and writing skills while social integration was fostered through cohort community building and regular interaction opportunities. The monthly workshop structure provided the external accountability many students needed to maintain momentum by breaking the dissertation process into discrete components with specific deadlines, addressing the overwhelming nature of the dissertation task that often leads to paralysis and procrastination.
The integration of Tinto’s framework proved particularly valuable in understanding why the program succeeded. By addressing both academic integration (through skill development workshops) and social integration (through peer cohort support), the intervention created the comprehensive support system that Tinto’s theory suggests is necessary for student persistence. The monthly structure maintained institutional commitment while providing the social connections that had been lost during the isolated dissertation phase.
The integration of AI tools represented an innovative approach to supporting doctoral students, providing 24/7 access to writing and research assistance that supplemented rather than replaced human mentorship and guidance. Consistent with recent research on AI in academic writing (
While the ABD Cohort Program demonstrates significant success, several limitations should be noted. The absence of a formal control group and the single-site design restrict causal claims and limit generalizability to other institutional contexts. The voluntary nature of participation introduces potential self-selection bias, as students who chose to participate may have possessed higher motivation or different characteristics than non-participants. The program was implemented at a single institution with a specific student population, which may limit generalizability. Long-term follow-up is needed to assess the sustainability of completion rates and the quality of dissertations produced.
Future researchers should examine the program’s effectiveness across different disciplines and institutional contexts. Additionally, investigation into the optimal integration of AI tools in doctoral education, while maintaining academic integrity, represents an important area for continued study.
The cost-effectiveness of such intensive interventions compared to traditional doctoral support models requires analysis. While the program demonstrated success, institutions must consider resource allocation and scalability when implementing similar initiatives.
The author acknowledges the use of Claude AI (