AI Dissertation Memo Dissertation in Practice requirements at the University of Missouri–St. Louis (UMSL), specifically for EdD candidates, and determine what is expected in terms of data collection and practical application. I’ll also find examples or frameworks that align with your goal of building leadership capacity in AI-related risk management for schools.

I’ll follow up with guidance tailored to your context and training project.

Dissertation in Practice (Ed.D. Capstone)

Overview of the Dissertation in Practice

A Dissertation in Practice (DiP) is the culminating scholarly project for many Doctor of Education (Ed.D.) programs. It focuses on impactful, applied research – using academic inquiry to directly improve a real-world problem in an educational or organizational setting. In contrast to a traditional Ph.D. dissertation (which often investigates a theoretical question to expand knowledge), an Ed.D. DiP is explicitly intended to change or inform practice in the field. This concept was developed by the Carnegie Project on the Education Doctorate (CPED) as part of refocusing Ed.D. programs on real-world impact. CPED defines a Dissertation in Practice as “a scholarly endeavor that impacts a complex Problem of Practice (PoP)” – meaning the project tackles a persistent, context-specific issue in the practitioner’s workplace with the goal of improving outcomes. In essence, the DiP is both a research study and a practical intervention, designed by a scholarly practitioner to address an urgent challenge within their professional sphere.

Purpose and Key Features of a DiP

The purpose of a Dissertation in Practice is to solve a meaningful problem in practice and develop the Ed.D. student’s capacity as a leader and change agent. Rather than simply writing about a problem, the Ed.D. candidate takes action to improve the situation and produces a tangible impact. Some key features include:

Real Problem of Practice: The project begins with a clearly identified Problem of Practice – “a persistent, contextualized, and specific issue embedded in the work of a professional practitioner”. For example, in your context the PoP might be the emerging risks AI poses to school safety and insurance. The DiP is grounded in this real-world problem that the practitioner and their organization face.
Impact-Focused and Action-Oriented: The goal is to enact change or improvement, not just observe. The research is impact-focused, meaning it aims for a measurable difference in an organization or community. Often the Ed.D. student will implement a solution or innovation (such as a new program, policy, or in your case a full-day AI training seminar) as part of the project.
Applied Research Methods: Because the emphasis is on solving the problem, Ed.D. candidates frequently use applied research approaches like action research, improvement science, program evaluation, or design-based research. These methods involve iterative testing and refining of solutions in real time. For instance, one might plan an intervention, do or implement it, study the results (collect data), and act on the findings (this Plan-Do-Study-Act cycle is common in action research). The researcher is typically an insider in the setting, working with colleagues or stakeholders to address the issue.
Deliverables with Evidence: A DiP usually produces some concrete deliverable or change in practice – e.g. a professional development workshop, a new curriculum, a policy recommendation, or a technology implementation. Crucially, it also involves gathering evidence on the impact of that deliverable. In other words, it’s not enough just to execute a solution; you must systematically evaluate what happened. For example, an Ed.D. student might incorporate a training module (as you plan to do) into their project and collect data on its outcomes, then analyze and discuss how effective it was. This could include pre- and post-training surveys, interviews, observations, or other data to measure changes in participants’ knowledge, attitudes, or behaviors. The dissertation will report on these findings to show what improvement occurred or what was learned.
Flexible Formats and Collaboration: Many Dissertations in Practice still follow a traditional structure (often a five-chapter format), but programs may allow flexibility in how the project is presented. Some Ed.D. programs even encourage group or team dissertations where a cohort of students works together on different aspects of a large problem of practice. Whether done individually or in a team, the final product is expected to be a practitioner-oriented document showcasing expertise in solving the problem. It should be written clearly for both academic and practitioner audiences, often blending scholarly research with practical documentation (e.g. including the training materials or implementation plans in appendices).

Another hallmark of the DiP is that its results are meant to extend beyond the university committee. CPED notes that Ed.D. findings should be shared with stakeholders and inform real practice, not just sit on a shelf. In your case, that means the insights from your AI-in-schools training (and any recommendations you develop) should ideally be disseminated to the school insurance professionals, school leaders, or even published in practitioner outlets so others can learn from it. This ensures the project has a life in the professional community, consistent with the Ed.D.’s focus on immediate applicability.

How a DiP Differs from a Traditional Dissertation

While both a Ph.D. dissertation and an Ed.D. Dissertation in Practice require rigorous research, their focuses and formats differ. Below are some key differences between a traditional doctoral dissertation and a Dissertation in Practice:

Goal/Purpose: A Ph.D. dissertation strives to extend theory or generate new knowledge, whereas an Ed.D. Dissertation in Practice is designed to impact a concrete problem of practice and develop the practitioner as a leader. In other words, the Ph.D. is theory-driven, and the Ed.D. is solution-driven (often with an explicit leadership development component for the doctoral student).
Research Questions: Ph.D. research questions are typically theoretical or abstract, arising from gaps in the scholarly literature. In a DiP, the questions are grounded in practical challenges – they are “significant, high-leverage questions focused on complex problems of practice”, often framed around real issues of equity, ethics, or effectiveness in one’s workplace. These inquiry questions are often co-constructed with stakeholders and very user-centered (e.g. “How can our school district’s safety team proactively manage AI-related risks?”) rather than purely researcher-curiosity driven.
Literature Review: A Ph.D. dissertation includes a comprehensive review of scholarly literature (theories, historical studies, etc.) to situate the work in an academic context. An Ed.D. DiP also uses literature, but in a more targeted way: it combines scholarly and professional sources to “understand the problem, find solutions, and develop measures that will provide evidence of change (or not)”. In practice, this means you’ll review not only academic research on AI in education, for example, but also industry reports, policy documents, and expert practitioner publications on risk management. The literature is used as a tool to frame the problem and inform action (rather than just to identify a theoretical gap).
Methodology: Ph.D. researchers often maintain an objective outsider stance, using established quantitative or qualitative methods to test hypotheses or explore phenomena. In contrast, Ed.D. practitioner-researchers are insiders intervening in their own context. The DiP employs “practical measures and processes aimed at uncovering if the change is working”. This might involve mixed methods (quantitative and qualitative data) but always oriented toward evaluating an intervention. For example, you might use surveys (quantitative) to measure participants’ knowledge gain and focus groups (qualitative) to gather deeper feedback – whatever methods will best determine whether your AI training made a difference. The emphasis is on action and evaluation: design a solution, implement it, and study its effect.
Analysis and Reflection: In a Ph.D. dissertation, analysis is largely researcher-driven (with the goal of generalizable findings or theory development). In a DiP, analysis is often collaborative and iterative – you might involve participants or colleagues in reflecting on the results, and the focus is on learning what works in that specific context. The Ed.D. candidate engages in critical reflection on the change process, sometimes cycling through multiple rounds of implementation and improvement (e.g. refining your training curriculum based on initial feedback – an iterative approach common in improvement science and action research).
Outcomes and Dissemination: The culmination of a Ph.D. is typically a published dissertation and journal articles aimed at the scholarly community. An Ed.D. DiP produces actionable knowledge for immediate use. The findings are disseminated in practical ways – communicated to local stakeholders, incorporated into organizational policies, and often shared in professional forums (in addition to any scholarly publications). Success is measured partly by whether the project led to an improvement or informed decision-making in the organization. For example, a successful DiP might result in a new AI risk management protocol adopted by the insurance professionals’ organization, and the Ed.D. graduate might present these results at an educational leadership conference or a school safety workshop, not just in an academic journal.
Professional Impact: Earning a Ph.D. is commonly a step toward an academic or research-focused career. By contrast, completing a DiP is meant to advance your professional practice and leadership in the field. The Ed.D. dissertation process is intertwined with your development as a practitioner-leader. CPED explicitly notes that the Ed.D. capstone should impact “self as a leader” as well as the problem being addressed. In other words, by solving a problem of practice, you also grow in your capacity to lead change. This aligns with the goal of Ed.D. programs to produce “influential leadership” in education settings. After a DiP, you should be not only an expert on your topic, but also better equipped to guide teams, make data-informed decisions, and champion improvements in your organization.

The Research Process: Data Collection and Evidence of Impact

One of your core questions was whether you need to collect data or if simply giving the presentation is enough. In almost all cases, a Dissertation in Practice will require systematic data collection and analysis. Think of the DiP as a blend of project implementation and research study. Delivering the full-day AI training seminar is a central action, but you will also design an inquiry around that action – this means planning how to gather evidence on the problem and the seminar’s impact. In fact, Ed.D. programs make it clear that the final capstone must be a rigorous, research-based endeavor, even if it looks different from a traditional dissertation. As one guide puts it: no matter what Ed.D. program you choose, you will always have to complete a research-based doctoral project – in “no dissertation” models, the project simply focuses more on applied action and deliverables. In other words, you’re expected to address your Problem of Practice through scholarly inquiry, not just professional intuition.

At the University of Missouri–St. Louis (UMSL) specifically, the Ed.D. program emphasizes that candidates will be “leading critical analyses of existing problems of practice and proposing solutions to those problems of practice that can be assessed for effectiveness.” (emphasis added). This means your presentation by itself is not the endpoint – you must build in an evaluation of how effective that presentation (or the associated initiative) is in addressing the problem. Similarly, UMSL notes that the Ed.D. dissertation is meant to document practical expertise and often involves reviewing extant data or engaging stakeholders in the research process.

What does data collection look like in a DiP? It doesn’t necessarily mean a large experiment or a complex statistics-heavy study (though it can). It does mean you’ll use inquiry methods to gather information before, during, and after your intervention. For example, you might collect baseline data that defines the problem (e.g. a survey of school insurance professionals to gauge their current understanding of AI risks, or an analysis of incident reports to see if AI-related issues have arisen). During or after your full-day training, you could collect feedback and outcome data – for instance:

Conduct pre- and post-workshop surveys or knowledge assessments to see if participants’ understanding of AI implications in school risk management improved.
Record questions, discussions, or case-study responses during the tabletop exercises to qualitatively assess changes in thinking.
Interview a few participants or have them fill out reflection forms weeks later to find out if they applied what they learned (e.g. “Did the seminar lead you to change any policies or practices regarding AI risk in your school insurance work?”).
If feasible, track any practical outcomes – perhaps the insurance professionals develop new guidelines for schools after the training, or report increased inter-department collaboration (“breaking down silos”) on safety issues involving technology.

All such data would form the evidence base for your dissertation. You would then analyze this information to evaluate the seminar’s impact. For instance, your analysis might reveal that 80% of participants showed significant improvement on a post-test about AI policy, or that in interviews they cite specific changes they plan to implement. You might also note challenges (maybe some legal/ethical dilemmas remain unresolved, indicating areas for further training). Gathering and analyzing data is what makes your project scholarly: it transforms a one-time presentation into a research-informed evaluation of an intervention. In essence, you are testing a hypothesis like “A targeted training will increase school insurers’ preparedness for AI-related risks” and using data to confirm or refine it. This aligns with best practices for DiPs – for example, even when Ed.D. students implement programs or trainings, they are expected to collect qualitative and quantitative data on the impact of those programs as evidence. The data doesn’t have to be complicated, but it does need to be systematically gathered and tied back to your inquiry questions.

To summarize, doing the presentation alone is not enough for a dissertation – you must also document the problem and outcomes with evidence. The DiP will typically include chapters (or sections) such as: an introduction to the Problem of Practice, a literature review on AI in education and risk management (and related legal/ethical issues), a description of your intervention (the Safety 360° AI training design and implementation), a methodology section explaining how you collected data (e.g. surveys, interviews, document analysis), a results section presenting what you found, and a discussion reflecting on what those findings mean for practice. This process ensures your project meets the academic requirements of the University and contributes actionable knowledge. UMSL’s Ed.D. curriculum supports this by including courses like Data Analysis for Educational Practitioners, Research Methods for Practitioners, etc., to prepare you for designing and analyzing a practice-based study. The Dissertation in Practice at UMSL is an 8-credit capstone project, so you will work closely with faculty advisors over the course of proposal, data collection, and writing. They will guide you on specifics such as Institutional Review Board (IRB) approval for research with human subjects (e.g. surveying seminar participants), how to format your report, and how to meet any university formatting requirements. While the UMSL website might not spell out every requirement publicly, the general expectation is clear: the DiP must involve identifying a problem, implementing a solution, and providing evidence of effectiveness in a scholarly manner.

Typical Steps in a Dissertation in Practice: To give you a big-picture view, completing a DiP often involves the following steps (iteratively):

Identify a Problem of Practice: Define the urgent issue in your professional context that needs addressing. (E.g. “School risk managers are not prepared for the rapid emergence of AI and its implications for student safety, data privacy, and liability.”) This becomes the guiding focus of your project.
Review the Knowledge Base: Investigate what is already known about the problem. You’d review scholarly literature on AI in K-12 education, legal cases or policies, ethical frameworks like MCEE for educators, and practitioner resources on risk management. This helps ground your approach in evidence and best practices, and shapes your inquiry questions.
Design an Intervention or Innovation: Plan a solution strategy. In your case, that’s the full-day AI tabletop training seminar for insurance professionals, which you might design collaboratively with your colleagues (Fred and Troy, as mentioned). Define the objectives (e.g. to raise awareness of AI risks across all “silos” of school operations – physical, virtual, social, psychological – aligning with the Safety 360° concept). Also plan what data to collect (for instance, a pre/post knowledge test, or scenario responses during the workshop).
Proposal and Approval: You will typically write a proposal that includes the above elements (problem, literature, intervention plan, and methodology for data collection). This is submitted to your faculty committee for approval before you proceed. They ensure your plan is sound and ethically compliant (this is where IRB approval comes in if needed).
Implement the Project: Carry out the action. Host the seminar, engage participants in discussions about AI scenarios in schools (covering legal, ethical, and strategic facets as described in the request), and encourage cross-role collaboration (breaking down silos among educators, IT, legal, etc. in attendance). You, as the Ed.D. researcher, will both facilitate the training and observe/record what happens for your study.
Collect Data During and After Implementation: As described earlier, gather the information that will help you evaluate impact. This could be immediate (survey at end of the seminar) and follow-up (a survey or interviews a month later to see if practices changed). You might also collect any documents that result (maybe a draft “AI risk management guideline” produced by the group, if that emerges, or even the questions raised by attendees). Make sure to collect data in alignment with your proposal – both quantitative (numerical scores, ratings) and qualitative(people’s comments, observations) data can be valuable.
Analyze the Results: Examine the data for outcomes and insights. For quantitative data, you might calculate gains in test scores or use simple statistics to see trends. For qualitative data, you might code responses or identify common themes (e.g. many participants express concern about student data privacy with AI tools – a theme that validates the need for updated policy). The analysis should circle back to your research questions: e.g. “Did the training increase knowledge or change attitudes? What concerns or needs emerged? What elements of Safety 360° were most engaged by participants?”
Reflect and Refine: In some DiPs, there may be a cycle of reflection leading to a second round of action. (If your timeline allows, you could adjust your approach and do a follow-up session, for instance.) Even if not, you will reflect on what the data means. Perhaps you find that legal dimensions of AI (Fred’s part) raised more questions than could be answered in one day – indicating a need for ongoing legal guidance. Or you discover that participants felt more empowered to collaborate with other departments afterward (a success in breaking silos). These reflections connect back to practice and theory – you might relate findings to what literature said, and discuss surprises or challenges.
Write the Dissertation: Finally, you compile everything into a coherent document. It will read somewhat like a case study of a change initiative: stating the problem and context, explaining your methods, presenting data results, and discussing the implications for the organization and for broader educational practice. UMSL notes that the Ed.D. dissertation “highlights the candidate’s expertise in an area of practice” – so you will be demonstrating your expertise in the intersection of AI and educational risk management. Expect to also articulate how this work contributes to more effective practice (e.g. recommending that the Safety 360° approach be updated to include AI risk protocols, or suggesting annual AI risk training as a standard for school insurers). You will then defend this dissertation before your committee. After successful defense and any revisions, you’ll have completed your Dissertation in Practice.

Throughout this process, collecting and analyzing data is essential – it’s what elevates the project to a doctoral-level contribution. Just doing a presentation, even if it’s excellent, would not fulfill the requirements because the university needs evidence that you can engage in inquiry and critically assess the outcome. Think of your DiP as both a leadership initiative and a form of research on that initiative. This dual nature is what makes the Ed.D. capstone powerful: it’s immediately useful to your organization and also produces new insights for the field.

The DiP at UMSL and Building Leadership Capacity

Since you mentioned the University of Missouri–St. Louis (UMSL) Ed.D. specifically: UMSL’s program is a 3-year cohort-based Ed.D. in Educational Practice, and the Dissertation in Practice is indeed the required final capstone (8 credit hours of your program). Students are admitted into a Learning Community of Practice cohort centered on a theme, and some cohorts opt for a collaborative (group) dissertation approach to tackle a high-impact problem together. Whether done in a team or individually, the DiP is the vehicle through which you demonstrate your ability to apply what you’ve learned to a real problem. UMSL’s materials emphasize action research and working directly with community stakeholders as part of the dissertation research experience. The dissertation can take many forms, but it must be a “practitioner-oriented document” showcasing how you’ve improved an area of practice. In practical terms, expect guidance from faculty on structuring your dissertation; often it will still have recognizable sections (Introduction, Background/Literature, Methodology, Results, Conclusion), but you might include products of practice (like your training curriculum, slide decks, or risk assessment tools you developed) as part of the evidence.

A major outcome of the Ed.D. journey – and your DiP – is building leadership capacity. UMSL notes that graduates will “emerge as transformative leaders equipped to address complex educational challenges and drive innovation in their fields.” This means that by engaging in the DiP, you are practicing the very leadership skills the program wants to instill. In your project, consider the leadership dimensions at play: for example, coordinating a comprehensive Safety 360° strategy involves leading across silos (bringing together people from different departments or specialties), which is a key leadership competency. Your role in designing and facilitating the AI risk tabletop exercise places you in a leadership position – you are guiding peers through unfamiliar territory (AI in schools) and influencing organizational strategy. The DiP will likely ask you to reflect on this experience: How did you exercise leadership? How did you handle ethical dilemmas (perhaps referencing the Model Code of Ethics for Educators (MCEE) if that’s part of your ethical framework)? What communication and collaboration strategies did you use to engage the whole community in risk management (“Safety 360°” as you called it)? By analyzing these questions, your dissertation not only reports on the training’s impact, but also demonstrates your growth as a leader.

In fact, CPED’s framework highlights that the Ed.D. dissertation should impact “self as a leader” as much as the problem being addressed. Ed.D. candidates are often mid-career professionals aiming to expand their influence. Thus, a Dissertation in Practice is a chance to prove your leadership capacity by successfully orchestrating change. Your project on AI risk management for schools is an excellent opportunity to do so – it touches on cutting-edge challenges, requires ethical and strategic thinking, and will benefit from you leveraging your professional network (legal experts like Fred, ethical perspective from Troy, etc.) to lead a comprehensive response. This aligns perfectly with UMSL’s goals of developing scholar-practitioners who “collaborate effectively with diverse communities and build partnerships through strong communication skills” and “implement change in their professional practice… using critical reflective practice.”. In short, by completing the DiP you will not only have delivered a valuable training, but you’ll also show that you can lead initiatives that make a difference – a key mark of an Ed.D.-level professional.

Conclusion and Next Steps

In summary, a “Dissertation in Practice” is the Ed.D. capstone project focused on a real-world problem, such as the implications of AI for school insurance in your case. It involves designing and implementing a solution (like your full-day AI training) and conducting research around that solution to evaluate its effectiveness. You will need to collect and analyze data – this is what distinguishes the DiP as doctoral research rather than just a professional task. UMSL’s program, like other CPED-influenced Ed.D. programs, requires that the solution be assessed with evidence and documented in a scholarly manner. Doing the presentation is an essential part of the project, but it’s not the whole of it; the dissertation will be the written narrative and analysis of that project from start to finish.

As you move forward, it would be wise to consult any Ed.D. handbooks or your faculty advisor at UMSL for specific guidelines on the format and requirements (they may have templates or examples of past Dissertations in Practice). You might also look at CPED’s resource library or databases of award-winning DiPs to see how others structure their work. Given what we know: plan on crafting a proposal, getting approval, executing your training with a data collection plan in place, and then writing up a thorough reflection on the outcomes. By doing so, you’ll fulfill the requirements of the Dissertation in Practice and contribute valuable insights on AI risk management in education – all while enhancing your own leadership skills and professional credibility. Good luck with your project, and remember that the emphasis is on both action and inquiry. With a well-executed DiP, you’ll not only help “fuel your membership in a full day seminar” as the request envisioned, but also leave a lasting scholarly contribution on how schools can navigate the risks and rewards of AI in a holistic, 360° safe manner.

Sources:

OnlineEdDPrograms FAQ – What is an Ed.D. Dissertation in Practice? (Describes the definition, purpose, and format of the Dissertation in Practice, emphasizing impact on a Problem of Practice and use of applied research methods.)
Carnegie Project on the Education Doctorate (CPED) – What is a Dissertation in Practice? (Explains CPED’s vision for the DiP, including differences from a Ph.D. dissertation in purpose, questions, literature, methods, etc., and the focus on the practitioner’s leadership role.)
UMSL Ed.D. in Educational Practice – Program Description (University of Missouri–St. Louis program details confirming that the Ed.D. culminates in a Dissertation in Practice, which is a practitioner-oriented capstone requiring analysis of a problem and assessment of a solution’s effectiveness; notes on collaborative dissertation options and stakeholder engagement.)
EdDPrograms.org – No Dissertation Ed.D. models (Highlights that even “no traditional dissertation” Ed.D. programs require a rigorous research project, typically a Dissertation in Practice addressing a complex Problem of Practice with an innovative, applied project integrating leadership and equity considerations.)
Example from CPED/Impacting Ed Journal (Comparison of Ph.D. vs Ed.D. dissertation expectations, illustrating how Ed.D. work is disseminated to stakeholders and centered on practical impact).
OnlineEdDPrograms – Discussion of Applied Approaches (Describes methodologies like action research and the importance of gathering qualitative and quantitative data to evaluate outcomes in a Dissertation in Practice, reinforcing the need for data collection in your project).