International Perspectives on Military Education

International Perspectives on Military Education

volume 3 | 2026

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AI-Supported Enlisted Professional Military Education

ChatGPT Integration in the Staff Noncommissioned Officer Leadership Seminar Program

Ivan A. Linares

https://doi.org/10.69977/IPME/2026.006

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Abstract: Enlisted professional military education (EPME) must adapt to address the complexities of twenty-first-century warfare, such as doctrinal complexity, sustained operational stress, and the need for scalable, individualized learning solutions. This study tests the hypothesis that artificial intelligence (AI), serving as a real-time supplemental tutor, can enhance EPME in five areas: reducing operational stress, enabling scalable and asynchronous access, providing context-aware personalized instruction, improving comprehension and critical thinking, and preserving doctrinal fidelity. Employing a single-case qualitative design, the research investigated a simulated staff sergeant completing the 15-week Staff Noncommissioned Officer (SNCO) Leadership School Seminar Program (SLSSP) with ChatGPT as the exclusive instructional resource. The AI delivered explanations, posed questions, assigned exercises, provided feedback, and facilitated reflection through a standardized weekly protocol. The participant achieved full mastery of the learning objectives and demonstrated increased metacognitive awareness and critical thinking. The findings suggest that AI can deliver scalable, asynchronous instruction, allowing human instructors to focus on students’ personal and professional development. Ethical safeguards, including systematic cross-verification with Marine Corps doctrine and an emphasis on epistemic vigilance, ensured doctrinal integrity. These results highlight AI’s potential as a force multiplier in enlisted education.

Keywords: enlisted professional military education, EPME, artificial intelligence, AI, ChatGPT, staff noncommissioned officer, SNCO, Leadership School Seminar Program, SLSSP, asynchronous instruction, metacognition, doctrinal fidelity

 

Introduction

Enlisted professional military education (EPME) faces both operational and intellectual challenges. The Marine Corps expects its staff noncommissioned officers (SNCOs) to advise commanders in increasingly complex operational environments, interpret multidomain doctrine, and develop subordinates capable of adaptive thinking beyond prescriptive checklists. However, the educational system designed to foster these competencies is constrained by sustained operational stress, global dispersion, and competing mission demands that limit Marines’ available time. These factors create a critical need for online educational opportunities accessible to Marines regardless of location. Although these challenges are well documented, it remains uncertain whether current technological tools can do more than efficiently deliver content or fundamentally transform the learning processes of enlisted Marines.

This study was initiated to address this question. The instructional program examined is the Staff Noncommissioned Officer Leadership School Seminar Program (SLSSP), a seminar-based extension of the SNCO Leadership School curriculum that allows Marines to participate at their duty stations through instructor-facilitated seminars. This format supplements, rather than replaces, resident attendance, accommodating Marines whose operational commitments prevent participation in resident cycles and supporting broader institutional efforts to expand online educational opportunities. Congressional interest in AI integration within military operations prompted a controlled evaluation of AI’s instructional potential in EPME.[1] Initial use of ChatGPT in the SLSSP revealed patterns that warranted systematic investigation. What began as a responsive experiment evolved into a structured 15-week case study, during which the AI consistently delivered on-demand doctrinal support, maintained engagement beyond scheduled hours, and promoted deeper reflection through iterative questioning. These findings informed the central hypothesis; AI, functioning as a real-time supplemental tutor, can substantially enhance EPME across five dimensions: operational stress, scalability, personalization, comprehension, and doctrinal fidelity.

The operational imperative is clear: Marines must be prepared for complex battlefields where rapid learning directly affects survivability and mission success. EPME must therefore integrate traditional instruction with adaptive tools that augment, rather than replace, human educators. A key consideration is whether these tools can reveal aspects of learning that current assessment frameworks do not capture. For instance, when a staff sergeant independently explores doctrinal questions beyond assigned requirements, this demonstrates the emergence of educational momentum. Determining whether EPME can identify, nurture, and leverage such intrinsic learning is a fundamental challenge addressed by this research.

Existing research supports the potential for AI integration in military education, yet significant gaps remain. Kathleen Moore and William Barry, in their study of data and AI literacy at the Army War College, found that students who received structured AI tool instruction demonstrated significantly greater familiarity with AI capabilities and a stronger intention to use these tools in future professional contexts.[2] However, no military branch has systematically investigated, through empirical case study, the impact of AI as a dedicated supplemental instructional agent on measurable learning outcomes in enlisted professional military education. Previous studies have focused on hybrid models where AI supports but does not replace human instruction, leaving a critical gap in understanding AI’s specific potential within enlisted military education. This study seeks to address that gap directly.

 

Purpose and Thesis

This study formally examined whether AI could bridge the identified gaps in EPME delivery. The hypothesis was developed inductively during the course of the research and refined through 15 weeks of observation, as described in the introduction above. These observations confirmed and clarified the five dimensions introduced previously, grounding each in observed instructional behavior rather than theoretical projection.

•    The first dimension addresses stress on the force. AI provides a responsive, on-demand support system that meets Marines at their points of operational commitment, resolving foundational questions before seminar sessions begin. This approach creates space for human mentorship and meaningful dialogue, rather than consuming limited instructor time on routine clarification.

•    The second dimension concerns scalability and asynchronous access. AI enables online educational opportunities for globally dispersed Marines, sustaining learning momentum and allowing engagement with high-quality EPME on individual schedules between instructor-led sessions, without sacrificing rigor or doctrinal alignment.

•    The third dimension involves context-aware personalized learning. AI delivers tailored tactical scenarios and leadership challenges that are calibrated to the learner’s role and demonstrated performance level.

•    The fourth dimension addresses comprehension, metacognition, and critical thinking. AI deepens understanding and reflective thinking through Socratic dialogue and adaptive feedback, transforming routine study into intentional leadership preparation.

•    The fifth dimension concerns doctrinal fidelity. AI maintains alignment with Marine Corps publications through systematic source verification and established habits of cross-checking, upholding professional standards and preventing the instructional drift to which decentralized learning environments are particularly susceptible.

 

Literature Review

The Strategic Imperative for AI-Enhanced Military Education

The rapidly evolving character of modern warfare demands adaptive changes in how the military educates its leaders. The United States has entered a period of accelerating global competition in AI, one in which the nation with the largest and most capable AI ecosystem will set international standards and realize broad economic and military advantages.[3] This strategic imperative extends directly to military professional education, where AI integration represents both an opportunity to enhance learning outcomes and a necessity for preparing leaders for the realities of AI-enabled warfare. Developing AI-literate enlisted leaders is not a peripheral modernization effort; it must be a core readiness requirement. AI readiness will not be a niche commodity; it will become a mission essential task aligned to the doctrine, organization, training, materiel, leadership, personnel, and facilities (DOTMLPF) initiatives.[4]

 

National AI Strategy and Military Applications

NAVMC 3000.1, United States Marine Corps Artificial Intelligence Implementation Plan, establishes the federal framework for understanding military AI integration. The plan’s provisions for military applications direct the Department of Defense to identify the talent and skills its workforce requires to leverage AI at scale and to implement talent development programs to meet those requirements.[5] This direction carries direct implications for EPME, where developing AI-literate leaders represents a strategic investment in long-term operational effectiveness.

The national strategy’s human-centered approach affirms that AI should complement rather than displace human work (the human-always-in-the-loop concept). The federal framework’s requirement that AI systems pursue objective truth and remain free from ideological bias aligns with military education’s foundational emphasis on doctrinal fidelity and rigorous analytical thinking.[6]

 

Marine Corps Strategic Framework for AI Integration

The Marine Corps’ commitment to AI integration aligns with broader force modernization efforts outlined in United States Marine Corps Artificial Intelligence Implementation Plan, which establishes five strategic goals and identifies three workforce groups critical to AI implementation.[7] The five strategic goals are mission alignment, competent workforce, deployment at scale, governance, and partnerships and collaboration, each aimed at evolving the Marine Corps into an AI-enabled force capable of decision advantage across all warfighting functions. Of these five, the competent workforce goal carries the most direct implications for EPME, framing AI literacy not as a specialized technical capability but as a baseline competency that must be developed across the enlisted continuum if the broader integration effort is to succeed.

The three workforce groups identified as critical to implementation are users, developers, and leaders. Users are Marines who employ AI capabilities to enhance operational effectiveness; developers are those who build and maintain AI solutions; and leaders are those responsible for making informed risk decisions regarding AI employment across the force.[8] The distinction matters for EPME because the user category encompasses the largest share of the enlisted force and represents the population for which professional military education must develop AI competence. Staff noncommissioned officers occupy a particular position within this framework: they function as users in their own AI engagement, but they also carry responsibility for developing AI-literate subordinates and advising commanders on AI-related risk decisions, placing them at the intersection of all three workforce categories.

This structural feature of the workforce taxonomy reinforces the case for AI-supported EPME at the SNCO level. A senior enlisted leader who has engaged AI as a learning tool during professional military education is better positioned to model responsible AI use for subordinates, articulate AI capabilities and limitations to officer counterparts, and exercise the epistemic judgment that the United States Marine Corps Artificial Intelligence Implementation Plan identifies as essential to AI employment across the total force.[9] Successful adoption of the strategic framework therefore depends not only on technical proliferation but on developing the cognitive and professional habits that AI-supported instruction is positioned to cultivate.

 

The EDCOM Campaign Plan

The Education Command Academic Year 2026–2029 Campaign Plan establishes the institutional framework within which this study’s findings carry the most direct operational relevance. The plan organizes the Marine Corps University enterprise around four lines of effort: Joint all-domain officer (JADO) education program, international military students and integrated deterrence, the EPME continuum, and asynchronous learning, AI, and disruptive technology. Of these lines of effort, two directly connect to AI influence, indicating a higher priority from the commanding general of Education Command (EDCOM). Line of effort three specifically focuses on improving EPME modalities to produce higher-performing, disciplined, and capable graduates for the Fleet Marine Force. This mandate establishes concrete objectives, including enhanced educational mechanisms and AI feedback tools for EPME at the E-5 through E-7 levels.[10] The plan’s candid assessment that the current training and education system is not adequately preparing the enlisted Marine for the future operating environment reflects an institutional gap that calls for educational innovation. Line of effort four calls for producing senior officers capable of supporting Joint all-domain operations, navigating complex information environments across multiple domains simultaneously.[11] To achieve this effort, it is imperative that the senior enlisted force is ready to advise and support the JADO officers during operational commitments. Realizing this vision requires enlisted Marines with advanced cognitive capabilities, the ability to integrate strategic concepts with tactical execution, and the epistemological awareness necessary for multidomain planning.[12] The plan establishes measurable near-term outcomes, including the expectation that by the end of academic year 2026, at least one major command and one critical partner will request assistance in developing doctrines and planning templates for the employment of disruptive technologies.

 

The Doctrinal Foundation for Adaptive Learning

Warfighting, Marine Corps Doctrinal Publication (MCDP) 1, articulates that while the character of war evolves with technology and tactics, its nature remains constant, defined by friction, uncertainty, disorder, and the human dimension.[13] This enduring reality demands Marines be capable of decentralized decision-making under stress and ambiguity. AI-enhanced enlisted education directly supports this requirement by accelerating cognitive development and strengthening dwell-time preparation efforts, which are essential to modern warfare success.

Command and Control, MCDP 6, reinforces this educational imperative by emphasizing that effective leadership relies not on rigid hierarchy but on creating a shared understanding that enables rapid decision-making and harmonized action.[14] EPME operates across a range of occupational specialties, and AI-enabled education can help students identify critical correlations between occupations, enabling them to see the holistic expectations of that rank across the Fleet.

Learning, MCDP 7, frames learning as a continuous, adaptive process rather than a series of episodic training events, affirming that Marines must engage in lifelong, self-directed, peer-­supported, and institutionally enabled learning.[15] AI-supported instruction is uniquely positioned to enable this vision. Hypothetically, a Marine stationed in Okinawa, Japan, can engage with doctrinal content at 0200, receive immediate feedback on a tactical planning exercise, and return the next evening to continue where they left off, sustaining the kind of continuous, self-directed learning that Learning describes as essential to professional development but that traditional resident and distance education models have consistently struggled to deliver at scale.[16]

 

Evaluation Framework and Literature Gaps

Moore and Barry, writing in The Military Scholarship of Teaching and Learning, examine how the Army War College’s approach offers a model for systematically evaluating AI integration in military education.[17] Their findings demonstrate that military students require comprehensive education about AI capabilities and limitations rather than mere exposure to tools. AI education prioritizes transparency, human oversight, and trust in the instructional relationship. The literature reveals a compelling convergence of strategic necessity, technological capability, and institutional readiness for AI integration. Yet, in EPME, a consequential gap persists. What remains unexamined is how AI, integrated as the primary supplemental instructional agent across a complete EPME course of study, affects doctrinal comprehension, metacognitive development, and the cultivation of curiosity-driven learning habits that current assessment frameworks, increasingly recognized as inadequate to contemporary EPME, are not designed to measure. This study directly addresses that gap and provides urgent empirical evidence at a time when institutional transformation is already underway.

 

Methodology

Research Design

This study employed a single-case qualitative design to evaluate the role of AI as a supplemental instructional agent in EPME. Following John W. Creswell and Cheryl N. Poth’s case study methodology, the research prioritized depth of contextual understanding over broad generalizability, a deliberate trade-off appropriate to an emerging area of inquiry where the primary need was rich descriptive evidence rather than confirmatory statistical inference.[18] Single-case designs are particularly well suited to exploratory research in novel instructional contexts, where the goal is to generate grounded hypotheses and establish proof-of-concept findings that can anchor subsequent large-scale investigation.[19] This study serves precisely that purpose: as a rigorous first-stage examination of what AI-supported EPME instruction looks like in practice, what it produces, and the questions it surfaces for the research agenda that follows.

The experiment simulated a Marine Corps staff sergeant completing the 15-week SLSSP curriculum, with ChatGPT serving as the exclusive supplemental instructional agent aligned with SLSSP learning objectives. The central research question was: Can an AI tutor, specifically ChatGPT, enhance doctrinal comprehension, critical thinking, and reflective learning for an enlisted Marine in a way that meaningfully complements traditional instruction?

 

Researcher-as-Participant and Bias Management

The researcher served simultaneously as the simulated study participant, a design condition that warrants direct methodological engagement rather than disclosure alone. This approach introduced potential observer bias in two forms: the researcher’s prior institutional knowledge may have elevated performance beyond what a typical SNCO participant would achieve, and the researcher’s interpretive framework may have influenced how AI outputs were evaluated and coded. Both risks were managed through several structural controls. All AI-generated outputs were cross-verified against Marine Corps publications by the researcher in the role of subject matter expert, creating a documented verification record independent of the learning simulation. Weekly protocols were standardized and applied uniformly across all 15 weeks. Performance outcomes were benchmarked against known course averages from prior cohorts, providing an external reference point that partially compensates for the absence of a control group. Readers should interpret the findings as indicative rather than definitive, appropriate to the exploratory function this study is designed to serve.

 

Participant Profile and AI Calibration

The study participant was modeled as a typical Marine staff sergeant attending the SNCO Leadership School. To establish a consistent baseline for AI interaction, synthesized information was used for the participant to assign four core duty areas representative of the cognitive demands that modern SNCOs must master. Mission-focused reasoning required providing concise status updates on readiness impacts related to personnel and equipment.[20] Professional development encompassed fostering the personal and professional growth of Marines within the participant’s charge.[21] Advisory capacity required advising officer counterparts on operational, organizational, and institutional objectives.[22] Communication excellence demanded exhibiting effective public speaking skills and sophisticated representation of the unit.[23]

These four duty areas were operationalized through an explicit prompt provided to the AI at the outset of the study, which directed ChatGPT to respond to all queries with the knowledge, experience, perspective, and communication style appropriate to the rank and role of a Marine staff sergeant:

Respond to all queries with the knowledge, experience, perspective, and communication style appropriate to this rank and role. Maintain the professionalism, leadership mindset, and institutional knowledge expected of a Marine SSgt while demonstrating the advanced leadership concepts being taught at SLSSP. Your responses should reflect both tactical expertise and the strategic thinking development that occurs at this educational level.

 

This calibration ensured that AI-generated content remained contextually grounded in the rank-appropriate expectations of the SLSSP curriculum, preserving the instructional integrity of the simulation and reducing the risk of responses that lacked the institutional voice and leadership framing essential to meaningful EPME engagement.

Control was exercised entirely at the prompt level. The calibration directive above, together with the retention directives embedded in the 11-step weekly protocol described below, constituted the instructions governing AI behavior throughout the study; no custom model development or fine-tuning was performed. This design choice was deliberate, testing the instructional value of a commercially available tool under controls that any learner or schoolhouse could replicate without specialized technical infrastructure.

 

Curriculum Learning Targets

The SLSSP curriculum is organized around four program outcomes that define the graduate’s expected institutional role and six student learning outcomes that define the curriculum’s measurable learning targets. Together, these establish the criteria against which the simulated participant’s performance was evaluated and the framework within which the 11-step weekly protocol was designed to operate.

The program outcomes specify that graduates of the SNCO Leadership School will be able to serve as:

•    Trusted leaders and advisors dedicated to professional and personal growth.

•    Indomitable warfighters with a bias for intelligent action who understand and embrace the Marine Corps’ warfighting philosophy and the Marine Air-Ground Task Force’s (MAGTF) role in Marine, Joint, and naval operations.

•    Guardians of Marine Corps values and standards, traditions, and esprit de corps while fostering positive command climates and unit cohesion.

•    Committed professionals, dedicated to country and Corps, who demonstrate strength of character to persevere and lead in the face of adversity.[24]

 

The student learning outcomes (SLO) operationalize these program outcomes as measurable instructional targets:

•    SLO 6.1. Analyze the foundations of Marine Corps leadership and the SNCO’s role in ethical decision-making to uphold institutional values and standards.

•    SLO 6.2. Analyze the SNCO’s role in advising and counseling Marines on discipline, ethical behavior, and professional growth.

•    SLO 6.3. Apply critical thinking and creative problem-
solving techniques to effectively communicate and supervise viable solutions to complex problems.

•    SLO 6.4. Explain the Marine Corps warfighting philosophy and how it is applied throughout the MAGTF.

•    SLO 6.5. Explain the strengths of the MAGTF and how its integration in naval and Joint operations enhances operational effectiveness.

•    SLO 6.6. Apply professional communication skills to convey decisions, resolve conflict, and advocate for resources.[25]

 

Each weekly lesson within the 15-week curriculum contributes to one or more of these outcomes through lesson-level educational objectives, which served as the third input in the standardized 11-step protocol described below.

 

Course Structure and Procedure

The complete 15-week SLSSP curriculum was followed. Each week, the simulated participant interacted with ChatGPT using a standardized 11-step protocol. The methodology emphasized minimal prompting to preserve AI’s natural instructional responses and avoid introducing researcher bias. Following each weekly cycle, the researcher, in the role of subject matter expert, cross-checked all AI-generated outputs against Marine Corps publications to verify doctrinal accuracy and identify any instances of factual drift.

The 11-step weekly protocol proceeded as follows:

1.     The week’s program course overview was entered with the directive to “retain for follow-on prompts.”

2.     The course introduction (when available) was provided with the directive to “retain for follow-on instructions.”

3.     The week’s educational objectives were entered with the directive to “retain the following information.”

4.     Required readings were uploaded with the directive to “retain the attached information to aid in answering follow-on questions.”

5.     Issues for consideration were submitted with the directive to “answer the following.”

6.     Quiz items were submitted to ChatGPT through screenshot analysis with the directive to “answer the questions,” allowing the AI to interpret each item as it appeared in the original assessment instrument and generate responses against the doctrinal content provided in earlier protocol steps.

7.     Discussion forum responses were requested at a 250-word count requirement per response.

8.     Impromptu instructor-provided questions were entered for response as they arose organically during seminar sessions.

9.     Assignments were completed with the directive to “take the instructions and complete the assignment.”

10.     Reflection journal entries were generated with the directive to “generate a 250-word count journal entry on the designated lesson.”

11.     After-action reports were produced from that specific lesson with the directive to “generate an after action on the designated lesson.”

 

Data Collection and Assessment Metrics

Performance was assessed through a combination of qualitative and quantitative measures, systematically capturing both academic outcomes and indicators of cognitive development across the full 15 weeks.

Table 1 presents the complete 15-week curriculum sequence, organized by lesson topic, lesson number, thematic focus, and the specific AI tutor engagement emphasis applied during each week. Table 2 presents the assessment framework employed throughout the study. Every lesson across the program was supported by AI interaction, with the participant submitting structured prompts drawn from the standardized 11-step weekly protocol established at the outset of the study. Engagement was not passive. The participant submitted initial prompts to orient the AI to the week’s content, then refined outputs through successive iterations, challenging responses, requesting deeper explanation, applying doctrinal content to scenario-based problems, and cross-checking AI-generated material against official Marine Corps publications. This iterative prompt-and-response cycle was the primary mechanism through which doctrinal comprehension, critical thinking, and reflective growth were developed and observed across all 15 weeks.

 

Table 1. SNCO Leadership School modules and AI tutor engagement focus

Week	Lesson topic	Lesson #	Theme	AI tutor engagement focus
1	Complex problem solving	6905	Leadership	Logical fallacies, elements of thought, critical thinking
2	Effective communication	7610	Communication	Speech rehearsal feedback, 7Cs drills, social media scenarios*
3	Command and control	6720	Warfighting	Command and Control, MCDP 6, Q&A, AI-assisted command decision games
4	Supporting the commander’s leadership philosophy	7920	Leadership	Organizational analysis, alignment of commander’s intent
5	Influencing the command climate	7930	Leadership	Role-play scenarios
6	Unit readiness program	7770	Warfighting	ADDIE model tutoring, readiness metrics+
7	Warfighting	6705	Warfighting	Tactical decision games
8	Tactical planning	6740	Warfighting	Operations order and interpretation
9	Naval integration and Joint operations	6710	Warfighting	Joint operations synthesis and analysis
10	Legal processes	6630	Leadership	Nonjudicial punishment process guidance
11	Coaching	6915	Leadership	Mentorship scenarios
12	NCO development	6916	Leadership	Historical leadership studies
13	Military correspondence	6620	Communication	Rules, regulations, and understanding of military writing
14	Performance evaluation	6930	Leadership	Personnel reporting practices
15	Professional military ethics	6910	Leadership	Ethical dilemmas

* The 7Cs refers to the seven attributes of effective communication: clear, concise, concrete, correct, coherent, complete, and courteous.
+ ADDIE refers to the five-phase instructional systems design model consisting of analysis, design, development, implementation, and evaluation.
Source: compiled by the author.

 

Table 2. Assessment framework: Data collection methods and measured outcomes

Category	Data collection method	Measured outcomes
Engagement	AI interaction logs	Participation, time on tasks, frequency of use
Knowledge retention	Quizzes, exams	Comprehension and recall scores
Critical thinking	Class forums, scenario solutions	Depth and quality of reasoning
Reflective growth	Weekly journals	Evidence of metacognitive regulation
Application of learning	Practical assignments, leadership practicums, real-world discussions	Demonstrated application in realistic contexts
Doctrinal alignment	Cross-verification of AI output	Consistency with official publications

Source: compiled by the author.

 

Specific Data Sources

Seven distinct data categories were systematically collected throughout the study period:

•    Engagement logs consisted of all AI-student chat interactions, capturing frequency of use, time on task, and the nature of prompts submitted across each week of the curriculum as structured by the 11-step weekly protocol.

•    Knowledge checks comprised weekly quiz results, providing quantitative benchmarks for doctrinal comprehension and recall.

•    Assignments included all written work evaluated against standard rubrics, offering evidence of the participant’s ability to apply course content in structured tasks.

•    Discussion contributions consisted of forum posts collected across all 15 weeks of the curriculum.

•    Reflection journals comprised weekly entries produced at the close of each lesson.

•    Peer and instructor feedback captured anecdotal observations of the participant’s performance from those engaged in the seminar environment.

•    Doctrinal verification involved systematic cross-checking of all AI-generated outputs against authoritative Marine Corps source documents.

 

Analysis Plan

The analytical framework employed a convergent mixed-methods approach, synthesizing quantitative performance data with qualitative indicators of cognitive and behavioral change. Quantitative analysis compared weekly quiz scores with known course averages to assess relative performance across the 15-week program. Qualitative analysis applied an inductive thematic coding approach to journal entries and discussion contributions, identifying recurring patterns across three dimensions: evidence of increased confidence and deeper reflection during live seminar sessions, metacognitive growth observable in the participant’s self-assessment and evaluation of reasoning processes, and the trajectory of progressive mastery from week 1 through week 15. Discussion post coding focused on the presence of doctrinal language and the depth of operational application; journal coding focused on observable shifts from descriptive summaries of lesson content toward analytical reflection on reasoning processes, self-correction, and deliberate evaluation of learning strategies.

Doctrinal verification served as a third analytical layer, with all AI-generated content systematically cross-checked against official Marine Corps publications. This verification process functioned both as a data source and as an ethical safeguard, ensuring that instructional content remained aligned with authoritative doctrinal standards throughout the study period.

 

Ethical Considerations

The integration of AI as a supplemental instructional agent required careful attention to academic integrity and to genuine learning outcomes. Several safeguards were implemented throughout the study. All written submissions remained the original work of the simulated student, serving solely as a tutor to assess the extent of its benefit as a real-time tutor rather than as an author of assessed content. Official Marine Corps publications were cited whenever AI referenced doctrinal materials, ensuring that sourcing remained transparent and verifiable. Epistemic vigilance, defined here as the deliberate habit of healthy skepticism toward AI-generated information and the consistent practice of independent verification before accepting AI outputs as authoritative, was maintained throughout the study. The study design explicitly framed AI as a supplemental tool rather than a replacement for human judgment, preserving the primacy of critical thinking and instructor mentorship within the learning and communication process.

 

Limitations

This study’s findings are bounded by several methodological and contextual constraints. As a single-case study, generalizability remains limited, as results reflect one simulated SNCO experience and may not capture the variability present across diverse Marine Corps education populations or individual military occupational specialties. AI model constraints present a structural limitation, as ChatGPT is not a military-specific platform and relies on consistent user cross-verification to maintain doctrinal fidelity. The researcher-as-participant condition introduces the potential for performance and interpretive bias described above. The intentional absence of instructor oversight means the study minimized human intervention that would be present in real-world EPME applications. Physical leadership limitations constrain measurable outcomes, as competencies such as interpersonal presence, physical bearing, and in-person mentorship cannot be replicated or assessed solely through AI interactions.

Future research should incorporate larger, more diverse samples, varied operational contexts, and blended learning environments that integrate both AI support and direct instructor engagement. This study nonetheless provides a rigorous and ethically sound framework for exploring AI’s impact within existing EPME structures, generating the foundational dataset and proof-of-concept evidence needed to inform responsible institutional scaling.

 

Results and Findings

Overview

The findings of this study are presented across three interrelated dimensions that emerged from the systematic analysis of the simulated Marine staff sergeant’s progression through the SLSSP curriculum. The SLSSP curriculum is organized around the six student learning outcomes enumerated above, and the participant’s engagement with each lesson was evaluated against the corresponding outcomes. These outcomes are organized and analyzed across three dimensions central to enlisted leader preparation: comprehension and doctrinal mastery, metacognitive regulation and epistemological awareness, and context-aware personalized learning. Scalability implications, while suggested by these findings, are addressed in the discussion section below because they extend beyond what a single-case study can directly demonstrate. Together, these dimensions provide an integrated view of how AI-supported instruction influenced both performance outcomes and underlying cognitive habits across the 15-week course.

The three findings’ dimensions presented here represent a refinement of the five hypothesis dimensions introduced earlier:

•    The hypothesis dimensions of comprehension and critical thinking emerged in the data as two analytically distinct phenomena, comprehension and doctrinal mastery on the one hand, and metacognitive regulation and epistemological awareness on the other, and are reported separately.

•    The hypothesis dimensions of stress on the force, scalability, and personalization were observed in the data, with personalization yielding sufficient evidentiary weight to merit its own findings dimension, while stress on the force and scalability are addressed together in the discussion below as projected implications rather than demonstrated outcomes.

•    The hypothesis dimension of doctrinal fidelity is reported within the comprehension and doctrinal mastery dimension, where the empirical evidence of doctrinal alignment was concentrated.

 

Comprehension and Doctrinal Mastery

The participant consistently demonstrated not only knowledge retention but the capacity to apply doctrine in complex, scenario-based contexts, moving beyond rote memorization to critical analysis across multiple lesson areas.

The most demanding test of doctrinal comprehension occurred during weeks one and seven, when the participant applied warfighting functions to complex problem-solving and battalion-level amphibious assault and cordon-and-search scenarios. The participant correctly identified how each warfighting function, including command and control, fires, and logistics, would need to be synchronized, and accurately anticipated points of friction before they were introduced, demonstrating operational grasp of doctrine rather than surface familiarity.

Two examples illustrate AI’s capacity to guide procedural mastery with doctrinal specificity. During week 10, the participant navigated the entire nonjudicial punishment (NJP) process end-to-end, drafting charge sheets, preparing NJP briefings with Uniform Code of Military Justice references, and writing simulated rebuttal letters with procedural precision, including exact rights advisement sequences and documentation requirements. During week eight, the participant produced a five-paragraph order for a company attack through iterative refinement with ChatGPT, arriving at a product that course faculty, applying the program’s standard tactical planning rubric, evaluated as excellent and requiring only minor edits before submission. Both examples demonstrate a form of instructional support that static distance learning formats cannot replicate: sustained, adaptive guidance through multi-step tasks with doctrinal stakes, tailored to the individual.

During weeks 5 and 15, role-playing toxic command climate scenarios developed the participant’s ethical decision-making practice, shifting from intuitive responses toward structured situational analysis. During week nine, analysis of the Goldwater-Nichols Department of Defense Reorganization Act of 1986 and expeditionary advanced base operations reflected sophistication consistent with upper-tier SLSSP performance expectations. During week 14, demonstrated competency in fitness report audits, Junior Enlisted Performance Evaluation System (JEPES) scoring, and career progression advising reflected the real-world leadership application that distinguishes high-performing SNCO graduates.

During the full 15 weeks, the AI-assisted participant achieved an average of 98 percent on weekly doctrinal quizzes, compared with typical scores of 85–90 percent in prior cohorts completing the same assessments through traditional study methods.

 

Metacognitive Regulation and Epistemological Awareness

The study revealed significant and measurable growth in metacognitive skills across the 15-week period, with the participant’s approach to problems becoming progressively more analytical and deliberate as the curriculum advanced.

Early during the course, the participant answered scenario questions quickly and intuitively. By mid-course, a clear behavioral shift toward structured analysis was evident. During an ethical scenario addressing toxic leadership, the participant outlined multiple situational facets and wargamed the consequences of different response options before arriving at a recommended course of action, a level of analytical deliberateness absent from early course responses. This shift represents one of the study’s most significant findings, suggesting that AI-mediated Socratic dialogue can produce measurable changes in how learners approach problems, not merely in what they know.

Epistemic vigilance developed into a self-directed habit rather than a researcher-imposed requirement. By week nine, during discourse with fellow colleagues, the participant stated that they have learned not to take ChatGPT’s answers at face value. They realized that the true method to gain insightful use with ChatGPT to ask, “Where does that information come from?” This awareness made the participant more skeptical and requested more context in responses with ChatGPT. This growth was demonstrated in practice when the participant independently identified and corrected ChatGPT’s misattribution of the Goldwater-Nichols enactment date as 1987 rather than 1986. Structured exposure to AI’s limitations, rather than undermining confidence in the tool, appeared to strengthen the learner’s critical stance toward all information sources, a finding with direct implications for how epistemic vigilance should be built into AI-supported instructional frameworks.

Weekly reflection journals documented a discernible trajectory across the 15-week program, with early entries characterized by descriptive summaries of lesson content giving way by mid-course to analytical reflection on reasoning processes, self-correction, and deliberate evaluation of learning strategies. By week 15, journal entries consistently demonstrated the kind of metacognitive awareness that Learning identifies as essential to the self-directed, adaptive learner, suggesting that sustained AI interaction, when structured around iterative prompting and reflective documentation, can accelerate the development of cognitive habits that traditional asynchronous PME formats rarely produce at measurable levels within a single course cycle.

 

Context-Aware Personalized Learning

The AI’s responsiveness to context manifested through two distinct personalization mechanisms across the 15-week curriculum. The first was role-based personalization in which the AI calibrated its responses to the rank, duties, and institutional voice of a Marine staff sergeant as established in the calibration prompt. Tactical scenarios, leadership challenges, and advisory situations were framed at the level of complexity appropriate to an SNCO operating at the intersection of small-unit leadership and senior-staff advisory functions, rather than at the level of a junior Marine or a commissioned officer. The second was performance-based personalization, in which the AI adjusted the depth and pacing of its instruction to the participant’s evolving comprehension during the 15 weeks. Earlier weeks tended toward foundational explanation and structured questioning; later weeks featured more demanding Socratic exchange, increased expectation of independent doctrinal reasoning, and tighter feedback on conceptual precision. This adaptive capability did not replace the seminar environment or the human instructor’s role in facilitating peer dialogue, ethical reasoning, and leadership mentorship; it filled the instructional space between scheduled seminar sessions, providing the kind of individualized reinforcement and challenge that a single instructor managing a full seminar cohort cannot consistently deliver to each student simultaneously.

Contextual relevance was maintained consistently across all lesson areas. Rather than generating generic instructional responses, ChatGPT framed explanations within recognizable military contexts, applying warfighting concepts to unit-level scenarios, referencing the MAGTF construct when discussing Joint integration, and adopting the voice and professional register expected of SNCO-level discourse. During week three, for example, when the participant engaged with command-and-control doctrine, AI did not simply define terms but situated them within realistic command post decision-making scenarios that reflected the operational demands a staff sergeant would actually experience. When the participant encountered challenging concepts, AI provided additional drills and alternative explanations calibrated to that specific doctrinal area. When mastery was evident, AI advanced the material’s complexity without delay, introducing higher-order application problems that pressed beyond the lesson’s baseline learning objectives. This dynamic calibration extended to individualized feedback on writing assignments, where the system offered line-by-line coaching of a quality and specificity that time-constrained instructors are rarely positioned to provide consistently across an entire cohort.

That said, this coaching quality was not unconditionally reliable. As documented elsewhere in this study, AI-generated content required systematic cross-verification against authoritative Marine Corps publications, and instances of factual drift were identified and corrected by the participant throughout the program. The value of AI’s feedback capability is therefore contingent on the learner’s epistemic vigilance, reinforcing rather than replacing the critical evaluation skills that effective SNCO education is designed to develop.

Learner autonomy emerged as a consistent and instructionally significant pattern across the 15-week program. The participant regularly explored topics beyond the structured curriculum, including contemporary conflicts and comparative strategy analysis. During week nine, the participant initiated a discussion of South China Sea naval operations to test how Joint integration concepts applied to current operational realities, and they frequently posed follow-up questions driven by curiosity rather than assessment requirements, including requests for historical examples of Marine Corps leaders who embodied servant-leadership principles.

Whether AI availability generated this curiosity or simply provided a frictionless outlet for curiosity that traditional asynchronous formats had previously suppressed cannot be determined from a single case study. What the data does suggest is that AI’s persistent availability removed the structural barriers, fixed schedules, instructor gatekeeping, and peer performance anxiety that typically constrain self-directed inquiry in distance PME environments. The result was a pattern of learning behavior that current EPME assessment frameworks are not designed to capture, raising consequential questions about what enlisted education is currently measuring and what it may be missing.

Two representative exchanges illustrate the qualitative character of AI engagement. During week one, when asked whether critical thinking is an art or a science, ChatGPT responded that it applies intellectual standards systematically, like a science, but requires creativity and intuition, like an art, to adapt in dynamic environments, setting the tone for conceptual analysis rather than fact retrieval throughout the program.[26]

During week seven, exploring the concept of exploiting disorder in maneuver warfare, the participant prompted ChatGPT with the following question: “How does maneuver warfare use disorder to defeat the enemy?” ChatGPT responded: “Maneuver warfare exploits disorder by acting faster than the enemy can react. Creating and exploiting gaps forces the enemy into reactive, uncoordinated actions.”[27] This response directly reflects Warfighting’s articulation that maneuver warfare seeks to shatter the enemy’s cohesion through a series of rapid, violent, and unexpected actions that create a turbulent and rapidly deteriorating situation with which the enemy cannot cope. The AI did not reproduce doctrine verbatim but translated its conceptual core into language accessible at the squad and platoon level, demonstrating the kind of doctrinal fidelity combined with instructional adaptability that the study’s research framework identified as a critical capability requirement for effective AI-assisted EPME.

 

Summary of Quantitative Outcomes

The participant achieved a 98 percent average on weekly doctrinal quizzes, placing performance in the top 10 percent of overall course grades relative to prior cohort benchmarks. Engagement metrics reflected daily study habits sustained across the full 15 weeks, with zero missed deadlines and only two minor administrative clarifications required from human instructors during the entire course. Faculty recognized improved analytical thinking and confident application of doctrinal concepts as distinguishing characteristics of the participant’s performance, with the capstone after-action review brief noted as among the strongest observed.

Taken together, these outcomes validate the central hypothesis across all five dimensions, though the nature of that validation differs by dimension and warrants precise characterization. Critically, these outcomes did not emerge from AI use in isolation. They were produced by a deliberate instructional design that embedded AI tools within a structured learning and communication process, anchoring each week’s engagement in the 11-step protocol, calibrating AI responses to the professional standards of a Marine staff sergeant, requiring systematic cross-verification against authoritative Marine Corps publications, and framing reflective journaling as a formal instrument of metacognitive development. It was this design, not the technology alone, that created the conditions under which meaningful learning occurred. The quantitative performance data confirm consistent doctrinal comprehension at a high level, but the more consequential evidence lies in what the numbers alone cannot show. The participant’s unprompted application of Joint integration concepts to live South China Sea operations, the documented shift in reflection journal entries from descriptive summaries to analytical reasoning, and the faculty recognition of the capstone after-action review brief as among the strongest they had observed collectively suggest that AI-supported instruction produced learning that extended beyond what standardized assessments are designed to measure. The documented growth in metacognitive skills and epistemic vigilance, particularly the participant’s developed habit of independently verifying AI outputs against authoritative Marine Corps publications, indicates that AI tutoring did not produce passive dependence but actively cultivated the critical evaluation skills essential to effective SNCO leadership. The scalability findings demonstrate AI’s potential to ease stress on the force and expand online educational opportunity without compromising instructional quality, provided the conditions of learner discipline, doctrinal guardrails, and periodic human oversight documented in this study remain in place. Finally, the emergence of self-directed, curiosity-driven inquiry outside formal assignment structures points toward a dimension of AI’s instructional impact that neither performance metrics nor current EPME assessment frameworks are designed to capture, and that the discussion section below examines as perhaps the study’s most consequential and unresolved finding.

 

Discussion

The results of this case study demonstrate that AI, when integrated as a supplemental instructional agent with appropriate doctrinal guardrails, can meaningfully enhance EPME delivery across all five dimensions examined. The findings are consistent with the hypothesis developed throughout the research and carry practical implications for how the Marine Corps approaches enlisted education modernization at a time when institutional appetite and strategic direction are already aligned.

 

Evaluation against Research Framework

Each dimension of the hypothesis was validated through the study’s evidence base, with outcomes that strengthened progressively as the instructional design matured across the 15-week arc. AI eased stress on the force by functioning as an on-demand knowledge repository, resolving foundational questions before seminar sessions and creating space for human mentorship and higher-order dialogue that instructor time alone cannot sustain at scale.

The participant completed the full 15-week program with approximately three hours per week of direct human instructor contact, a figure unremarkable in isolation but significant in context. That limited contact did not result in motivational deterioration, deadline slippage, or disengagement from course material commonly associated with dropout risk found in asynchronous distance education. Research indicates that online students face elevated attrition risk when independent study is characterized by isolation, limited interaction with instructors, insufficient or delayed feedback, and the absence of a responsive instructional presence.[28] AI’s continuous availability addressed each of these vulnerabilities directly. AI-adapted responses based on demonstrated performance maintained optimal learning zones through dynamic calibration that group instructional settings cannot replicate, providing additional scaffolding when the participant encountered difficulty and advancing complexity when mastery was evident. Socratic dialogue was not left to emerge organically from AI interaction. It was deliberately structured into the study design through prompts that required the participant to justify reasoning, evaluate competing courses of action, and apply doctrinal principles to realistic operational scenarios, with AI functioning as the consistent interlocutor that sustained that dialogue between seminar sessions.

The result was documented growth in reflective inquiry, evidenced by the evolution of weekly reflection journals from factual summaries to metacognitive analysis across the program. Systematic cross-verification against Marine Corps publications ensured doctrinal accuracy throughout, with minor errors such as the Goldwater-Nichols date discrepancy caught through verification protocols, demonstrating the practical effectiveness of epistemic vigilance as an embedded instructional practice rather than a stated aspiration.

 

Risk Mitigation and Implementation Considerations

The study’s structured protocols successfully managed three critical risks inherent to AI integration in a doctrinal educational context.

Doctrinal drift: The gradual divergence of AI-generated content from authoritative Marine Corps publications that occurs when a general-purpose language model operates without domain-specific constraints or verification requirements. This was addressed not by the AI, which has no mechanism for self-correction against external doctrine, but by the participant through mandatory cross-verification of all AI outputs against authoritative source documents as a nonnegotiable step in the weekly protocol.

Overreliance: The tendency of learners to treat AI outputs as authoritative rather than as a starting point for critical evaluation. This was addressed through the response calibration directive established at the study’s outset, which explicitly framed ChatGPT as a tutor operating within the professional standards of a Marine staff sergeant rather than as a doctrinal authority, a distinction that shaped every subsequent interaction and was reinforced by the participant’s progressively independent verification habits during the 15 weeks.

AI hallucination: The well-documented tendency of large language models to generate plausible but factually incorrect outputs with equal confidence to accurate ones. Rather than expecting this risk to resolve itself through model improvement, the study’s design treated hallucination as a permanent feature of the instructional environment and built epistemic vigilance directly into the learning process as a behavioral norm. This approach produced an instructionally significant outcome. The participant developed the mental habit of treating all information, AI-generated or otherwise, as provisional until verified against authoritative sources, a cognitive disposition that Learning identifies as foundational to the adaptive, self-directed learner and one that sustained intellectual engagement during the full 15 weeks precisely because it kept the participant actively evaluating rather than passively receiving.[29]

Dr. Benjamin Jensen, professor of strategic studies at Marine Corps University, reinforces this implementation framework by emphasizing that context is central to responsible AI integration in military education.[30] He cautions that AI risks producing outputs divorced from the instructional environment when deployed purely as a prediction engine without doctrinal grounding. Responsible AI use in EPME must therefore remain context-driven, embedding doctrinal guardrails that verify outputs against official Marine Corps publications, designing prompts that explicitly reference the operational and institutional learning environment, and cultivating in students the epistemic vigilance necessary to evaluate AI-generated content against authoritative sources without requiring instructor intervention at every point of uncertainty.

Human instructors retain their irreplaceable role not as validators of AI output but as the contextual authority who models critical thinking, frames the moral and ethical dimensions of leadership that AI cannot navigate with doctrinal precision, and ensures that the learning environment develops Marines who trust their own judgment over any single information source, artificial or otherwise.

Nothing within this framework is specific to ChatGPT. The 11-step protocol, the calibration directive, and the mandatory cross-verification requirement are platform-agnostic controls that any sufficiently capable large language model can support, whether commercial or government-hosted. Parallel initiatives across the Marine Corps’ training and education enterprise already demonstrate this transferability, as for example, within the Twentynine Palms training enterprise, Marine Air Ground Task Force Training Command policy authorizes the use of generative artificial intelligence to develop training scenarios and student handouts through Department of Defense accredited platforms.[31] Separately, Marine Corps University has tested a large language model-facilitated after-action review capability and planned to deploy the associated training software at Sergeants School aboard Marine Corps Air Ground Combat Center Twentynine Palms.[32] Future implementation will require secure, DOD-approved platforms and blended instructional models that incorporate periodic and deliberate human mentorship to preserve the interpersonal leadership development that AI cannot replicate. The study’s finding that approximately three hours per week of direct human instructor contact sustained a complete 15-week course is not an argument for minimizing the instructor’s role, it is an argument for redistributing it. In a deliberately designed AI-supported EPME model, human instructor time is not scarce so much as it is misallocated, consumed by instructional functions that AI can perform with consistency and immediacy at any hour, leaving insufficient time for the dimensions of enlisted leader development that resist automation entirely. AI assumed the instructional functions that scale most readily, including on-demand doctrinal explanation, adaptive feedback on written work, iterative Socratic dialogue, and real-time application of doctrine to scenario-based problems, freeing human instructors to concentrate their engagement on the moral weight of ethical decision-making under pressure, the interpersonal dynamics of counseling and mentorship, the modeling of professional character, and the kind of shared human experience that has always been the irreplaceable core of effective EPME. In this study, that redistribution of instructional labor did not diminish the instructor’s role. It clarified and elevated it.

 

Strategic Alignment and Institutional Benefits

These findings directly support the Education Command Academic Year 2026–2029 Campaign Plan’s objectives for scalable, high-quality EPME delivery and NAVMC 3000.1, Marine Corps Artificial Intelligence Implementation Plan requirements for responsible AI integration across the total force.[33] The study’s outcomes map onto line of effort three’s goal of returning higher-performing graduates and line of effort four’s directive to use AI to enhance education through asynchronous methods in support of advising officers in the JADO environment. Cost-effective instructional delivery to globally dispersed Marines, consistent curriculum adherence independent of instructor availability, and the cultivation of continuous learning habits enabled by 24/7 AI accessibility each represent institutional benefits with direct operational consequences for a force that cannot always bring Marines to school when professional development is needed most.

 

Implications for Force Modernization

This case study validates AI’s potential to modernize EPME while preserving the Marine Corps’ core values and institutional character. The evidence supports a practical pathway in which AI supplements human instruction, enabling ubiquitous and continuous learning that prepares Marines for complex operational environments without displacing the human judgment, mentorship, and leadership development that define effective EPME.

The study’s most consequential implication, however, may not be what the performance data confirm but what the behavioral evidence suggests. The participant’s pattern of self-directed inquiry, pursuing doctrinal questions after hours, initiating discussions of current operational realities, and requesting historical leadership examples outside assignment requirements, represents a qualitative dimension of learning engagement that performance averages do not capture. These behaviors are neither incidental nor are they accidental. They are the product of a curriculum deliberately designed to create the conditions for optimal engagement, structured prompting that required justification of reasoning, reflective journaling that demanded self-assessment, and Socratic dialogue that refused to let surface-level answers stand unchallenged. What the behavioral evidence suggests is not that AI produced curiosity in a participant who lacked it, but that a structured AI-supported instructional design created the environment in which an already capable Marine could demonstrate the intellectual orientation that effective EPME has always sought to develop and that enlisted Marines are fully capable of sustaining when the conditions support it. That AI-supported instruction appeared to cultivate rather than suppress this orientation, and that current EPME assessment frameworks are not designed to recognize it when it emerges, is a finding worth examining carefully as the Marine Corps considers how to scale AI integration responsibly across the enlisted continuum.[34]

 

Scalability and Asynchronous Delivery

The findings from this single-case study suggest scalability implications that, while consistent with the patterns observed across the 15-week protocol, are not directly demonstrated by the study and warrant explicit framing as projected rather than demonstrated outcomes. The scope conditions under which the participant succeeded were specific: sufficient self-discipline to apply the 11-step protocol consistently, the institutional knowledge to recognize doctrinal drift when it occurred, and the cultivated habit of cross-verification that converted AI’s instructional support into reliable learning. Whether learners without these conditions would produce comparable outcomes is the central empirical question that subsequent research must address. Multicase and cohort-level studies are needed to test the scalability projections offered here against larger samples, varied operational contexts, and learner populations whose readiness conditions differ from those documented in this study. Longitudinal designs would further illuminate whether the metacognitive dispositions cultivated through AI-supported instruction persist beyond the course of study and transfer to operational decision-making. The case offered here is most usefully understood as a proof-of-concept that justifies investment in those subsequent studies, not as evidence that scalability has already been demonstrated at a level that would warrant immediate institutional adoption without continued empirical validation.

 

Conclusion

This research contributes empirical evidence to a previously limited body of enlisted military-specific AI education literature, establishing a foundation for evidence-based policy development at a moment when that strong base is urgently needed.

The findings demonstrate that AI sustained instructional continuity across the full 15 weeks with minimal human intervention, suggesting practical potential to address EPME’s scalability challenges and resource constraints without sacrificing educational rigor or doctrinal integrity. The study’s structured protocols successfully managed the risks most associated with AI integration in a doctrinal context, including factual drift, overreliance, and the erosion of epistemic standards, demonstrating that responsible implementation is achievable within existing EPME frameworks.

The study’s limitations define the boundaries of what these findings can claim and the directions in which the research agenda must expand. Future investigations should examine instructor perspectives, blended learning models, and large-scale implementation challenges to inform institutional adoption strategies that are both operationally sound and educationally rigorous.

Nevertheless, this research establishes AI’s viability as a force multiplier for EPME modernization, and that term is used here with doctrinal intentionality rather than as rhetorical convenience. In Marine Corps warfighting doctrine, a force multiplier does not replace combat power.[35] It amplifies existing capability, extending the reach, effectiveness, and sustainability of the force without substituting for the human judgment, leadership, and moral courage that no technology can replicate. AI in the instructional context operates on precisely the same principle. It does not replace the staff noncommissioned officer educator, the curriculum designer, or the seminar facilitator. It extends their reach across time zones and operational schedules, sustains instructional momentum between sessions, and ensures that no Marine’s professional development stalls when stress on the force outpaces the hours a human instructor can give. By amplifying human instruction rather than replacing it, AI offers the Marine Corps a pathway to deliver consistent, high-quality professional military education at scale while preserving the mentorship, moral development, and leadership formation that have always defined the irreplaceable core of enlisted military culture and that this study’s evidence confirms no algorithm is positioned to provide.

The future of EPME may depend less on optimizing content delivery and more on developing the institutional capacity to recognize, cultivate, and assess the kind of curiosity-driven learning that transforms competent Marines into genuinely adaptive leaders. The behaviors documented in this study are not incidental outcomes but indicators of the intellectual orientation that effective EPME is ultimately designed to produce.

Whether the Marine Corps’ current assessment frameworks are adequate to recognize and cultivate the intellectual qualities the modern operating environment demands is a question the institution can no longer defer. How it chooses to answer will shape the intellectual character of its enlisted force for generations to come.

 

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Appendix

This appendix presents selected examples of AI-student interaction from the 15-week study, organized in lesson sequence with one representative artifact per lesson. Materials illustrate the nature, depth, and doctrinal character of AI engagement across the curriculum, providing transparency into the quality of interaction that quantitative performance data alone cannot convey. The table identifies each lesson and the doctrinal source that governed its analytical framing during data analysis (table A.1, figure A.1).

 

 

Table A.1. The 15-week lesson plan

Week	Lesson code	Lesson title	Doctrinal anchor	Why
1	SN6905	Complex Problem Solving	Warfighting, MCDP 1 Marine Corps Planning Process, MCWP 5-10	Systems framing
2	SN7610	Effective Communication	Command and Control, MCDP 6	Leader communication
3	SN6720	Command and Control (C2)	Command and Control, MCDP 6	C2 architecture
4	SN7920	Supporting the Commander’s Leadership Philosophy	Leading Marines, MCWP 6-10	Diagnostic posture
5	SN7930	Influencing the Command Climate	Sustaining the Transformation, MCTP 6-10A	Climate assessment
6	SN7770	Unit Readiness Program	MCO 3500.110, Policy and Guidance for Mission Essential Task List (METL) Development, Review, Approval, Publication and Maintenance, B-3	METL planning
7	SN6705	Warfighting	Warfighting, MCDP 1	Maneuver warfare
8	SN6740	Tactical Planning	Marine Infantry Platoon, MCIP 3-10A.3i Command and Control, MCDP 6 Marine Corps Planning Process, MCWP 5-10	Tactical sketching, MCPP
9	SN6710	Naval Integration and Joint Operations	Competing, MCDP 1-4	EABO framing
10	SN6630	Legal Processes	MCO 1900.16, Separation and Retirement Manual	Administrative action
11	SN6915	Coaching	MCO 1500.61, Marine Leader Development	Developmental practice
12	SN6916	NCO Development	MCO 1500.61, Marine Leader Development	Developmental architecture
13	SN6620	Military Correspondence	SECNAV M-5216.5, Correspondence Manual  MCO 5216.20B, Marine Corps Supplement to the Department of the Navy Correspondence Manual	Correspondence standards
14	SN6930	Performance Evaluation	MCO 1610.7B, Performance Evaluation System (PES)	Evaluation responsibilities
15	SN6910	Professional Military Ethics	Leading Marines, MCWP 6-10	Formative leadership

Source: compiled by author.

 

Figure A.1. Systemic recruitment process diagram

Analytical note: The diagram organizes recruitment around three interdependent nodes and arrays contributing failures as coupled pressures rather than discrete defects. For a participant encountering systems framing for the first time, this representation models the conceptual shift the lesson demands and demonstrates the AI’s value as an early curriculum scaffolding partner. See, Marine Corps Planning Process, MCWP 5-10 (Washington, DC: Headquarters Marine Corps, 2020), 1-2.

Source: image generated by ChatGPT, March 2025.

 

Week 1. Lesson SN6905, Complex Problem Solving

The opening SLSSP lesson introduces complex problem solving, asking the staff noncommissioned officer to frame interdependent issues as systems rather than checklists. The participant prompted the AI to render recruitment shortfalls visually across an all-volunteer force.

 

Week 2. Lesson SN7610, Effective Communication

The second SLSSP lesson examines how staff noncommissioned officers match message to medium. The participant transitioned from considerate leadership to ask how the appropriate communication method is determined, prompting the AI to offer a seven-part decision framework illustrated in the matrix below (table A.2).

 

Table A.2. Seven-part decision framework

Think about the medium’s strengths
Method	Best for
Face-to-face	Emotional topics, immediate feedback, leadership presence
Phone/radio	Fast decisions, urgent updates
Email or MCTIMS	Official records, detailed info, written instructions
Text/chat	Quick coordination, informal updates
Social media	Broad announcements, morale posts, command messaging
Briefing/formations	Orders, updates, PME, awards

MCTIMS = Marine Corps Training Information Management System.
Source: compiled by author.

 

Week 3. Lesson SN6720, Command and Control

The third SLSSP lesson examines how command translates intent into coordinated action through control. Following discussion of that translation, the participant asked the AI to construct a metaphorical apparatus for command and control (C2), eliciting an extended conductor-and-orchestra analogy mapping each C2 element onto a structural component of musical performance (table A.3).

 

Table A.3. Conductor-and-orchestra C2 analogy

C2 component	Musical metaphor	Function in C2
Commander	Conductor	Provides vision and synchronizes the unit
Control	Sheet music, cues, tempo	Regulates execution and maintains unity of effort
Subordinates	Musicians	Executes the plan with discipline and creativity
Combat operations center	Concert hall and instruments	Apparatus that enables C2 to function
Feedback	Audience reaction + sound balance	Real-time adjustment based on results and input

Source: compiled by author.

 

Week 4. Lesson SN7920, Supporting the Commander’s Leadership Philosophy

The fourth SLSSP lesson asks the staff noncommissioned officer to distinguish leadership, command, and management as related but separable functions within the command philosophy. The participant prompted the AI to render their distinct contributions and overlapping vulnerabilities, eliciting the diagram as an inversion of the lesson’s doctrinal frame (figure A.2).

 

Figure A.2. AI-generated Venn diagram of disruptions to leadership, command, and management, with critical vulnerabilities mapped to centers of gravity

Analytical note: The diagram inverts the lesson’s affirmative frame by treating each function as a center of gravity and naming the vulnerabilities that disrupt it. Locating shared failures (e.g., lack of unity, breakdown in communication, failure to adapt) at the intersection demonstrates the AI’s capacity to extend a doctrinal construct analytically rather than restate it, modeling the diagnostic posture Leading Marines, MCWP 6-10, 3-22, asks of the SNCO.

Source: image generated by ChatGPT, April 2025. 

 

Week 5. Lesson SN7930, Influencing the Command Climate

The fifth SLSSP lesson examines how staff noncommissioned officers shape command climate through consistent leadership behavior. Following discussion of obstacles to applying core leadership rules, the participant asked whether inconsistent application could produce localized toxic environments, eliciting the rule-by-rule mapping below of leadership failures to the subcultural microclimates they cultivate (table A.4).

 

Table A.4. How failure to follow the seven rules breeds subcultures

Rule ignored	Resulting toxic subculture
Rule 1: Lead by presence	Leaders become absent. Influencers (toxic SNCOs or cliques) fill the void and shape the climate.
Rule 2: Communicate expectations	Each group or section creates its own “standard,” creating inconsistency across the unit.
Rule 3: Early correction	Toxic behaviors go uncorrected, becoming accepted norms.
Rule 4: Recognize effort	Marines disengage; high performers burn out; and underperformers hide.
Rule 5: Empower subordinates	Leaders hoard control or defer everything, causing junior Marines to stop caring.
Rule 6: Promote dignity	Cliques, “in-groups,” and discriminatory microcultures emerge.
Rule 7: Model behavior	Hypocrisy breeds cynicism; Marines copy bad examples and stop trusting leadership.

Source: compiled by author.

 

Week 6. Lesson SN7770, Unit Readiness Program

The sixth SLSSP lesson grounds staff noncommissioned officer leadership in unit readiness. Following an extended planning sequence in which the participant developed a training readiness package for Marine Heavy Helicopter Squadron 461 (HMH-461), the participant asked how SNCOs contribute to a unit’s mission essential task list, eliciting a five-part framework illustrated in part by the implementation oversight matrix below (table A.5).

 

Table A.5. Implementation oversight, command-level coordination

What	Who	When	Why
Training rhythm	Training SNCO + operations chief	Weekly	Keeps sections aligned and deconflicted
Qualification readiness tracker/on-the-job tracker review	SNCOs in charge + executive officer	Weekly	Enables rapid decisions and adaptation
Progress brief to commanding officer	Training coordinator	Monthly	Assures commander of alignment and impact
After action report collection and synthesis	Section leads	After each qualification rotation	Improves future planning

Source: compiled by author.

 

Week 7. Lesson SN6705, Warfighting

The seventh SLSSP lesson grounds staff noncommissioned officer judgment in maneuver warfare as articulated in Warfighting, MCDP 1. Following a complete mission, enemy, terrain, troops, time, and civil considerations (METT-TC) analysis, the participant requested a visual overlay to support enemy course of action assessment, eliciting the tactical depiction below. Following the enemy COA depiction, the participant prompted the AI for a feasible platoon counter-COA consistent with Warfighting, MCDP 1, eliciting the maneuver overlay in which friendly axes of advance are arrayed against the previously identified enemy positions (figure A.3.a).

 

Figure A.3.a. AI-generated tactical overlay depicting enemy course of
action (COA) against the village objective

Analytical note: The overlay renders the participant’s METT-TC products as a coherent enemy scheme, locating machine gun and mortar positions, the PSA AK-V 9mm removal corridor, and converging axes of advance on the village. The exchange demonstrates the AI’s capacity to translate analytical text into a graphical situation template, supporting the visualization the Marine Corps Planning Process, MCWP 5-10, D-5, 3-4–3-5, asks of leaders developing friendly courses of action.

Source: image generated by ChatGPT, April 2025. 

 

Week 8. Lesson SN6740, Tactical Planning

The eighth SLSSP lesson develops staff noncommissioned officer proficiency in tactical planning at the squad and platoon level. Following a complete OAKOC-W analysis and platoon operations order, the participant requested a tactical sketch depicting squad positioning, fields of fire, listening/observation post placement, obstacles, and control measures, eliciting the overlay (figure A.3.b).[36]

 

Figure A.3.b. AI-generated tactical overlay depicting friendly counter-COA against enemy positions on hills 2 and 3

Analytical note: The counter-COA overlay completes the analytical pairing, depicting friendly maneuver against the enemy disposition in the prior figure. By rendering the squad’s axis of advance, the envelopment of hill 3, and the suppression of hill 2 and the main supply route position in a single frame, the AI demonstrates its capacity to support the comparative visualization at the heart of course of action development. See Marine Corps Planning Process, MCWP 5-10, 3-3, 3-5.

Source: image generated by ChatGPT, April 2025.

 

Week 8. Lesson SN6740 (continued), Tactical Planning

Following the tactical sketch, the participant requested a formatted operations order graphic depicting the mission, execution, fire support, administration and logistics, and command and signal paragraphs for 2d Platoon’s establishment of battle position 2, eliciting the briefing format rendering below (figure A.4.a).

 

Figure A.4.a. AI-generated tactical sketch overlay of 2d Platoon defense at battle position 2, depicting squad dispositions, fields of fire, and control measures

Analytical note: The sketch translates the participant’s textual planning products into the graphical conventions of a defensive overlay, arraying squad positions behind the line of departure, depicting fields of fire toward the suspected enemy avenue of approach, and identifying control measures including the phase line and target reference point. The exchange demonstrates the AI’s facility with the symbology Marine Infantry Platoon, MCIP 3-10A.3i, 148–49, prescribes for tactical sketches.

Source: image generated by ChatGPT, April 2025.

 

Week 9. Lesson SN6710, Naval Integration and Joint Operations

The ninth SLSSP lesson situates the staff noncommissioned Officer within naval and Joint contexts, framing expeditionary advanced base operations (EABO) as a central concept. Following a structured analysis across five dimensions, the participant requested a visual mental model to consolidate the work into a single reference framework, eliciting the diagram below (figure A.4.b).

 

Figure A.4.b. AI-generated operations order visualization for 2d Platoon’s defensive position at battle position 2, integrating the five-paragraph order format with terrain depiction

Analytical note: The graphic integrates the five-paragraph order with a terrain rendering of the defensive scheme, fusing the doctrinal text format with the visual situation it describes. Although the rendering shows AI limitations when reproducing fine textual detail, the underlying analytical move (linking written order to terrain-anchored graphic) reflects the briefing posture Marine Corps Planning Process, MCWP 5-10, 6-4, 7-1, 7-4, asks of the SNCO presenting orders to subordinates.

Source: image generated by ChatGPT, April 2025.

 

Figure A.5. AI-generated visual mental model of EABO analysis

Analytical note: The model arrays the participant’s analysis into four interlinked panels (strengths and weaknesses, operational gaps, critical war-
fighting functions, and integration across the force) anchored by a central definitional node. The exchange illustrates the AI’s value as a synthesizing partner late in an analytical sequence, consolidating discursive products into a teaching artifact suitable for PME discussion and consistent with the conceptual framing in Competing, MCDP 1-4, 3-6.

Source: image generated by ChatGPT, May 2025. 

 

Week 10. Lesson SN6630, Legal Processes

The 10th SLSSP lesson develops staff noncommissioned officer judgment in administrative and disciplinary processes under MCO 1900.16, Separation and Retirement Manual. Following a Socratic dialogue distinguishes page 11s, 6105s, NJPs, and administrative separation authority, the participant requested a decision matrix consolidating the dialogue into a PME reference tool (figure A.6).[37]

 

Figure A.6. AI-generated disciplinary decision matrix consolidating administrative actions across two scenarios

Analytical note: The matrix consolidates Socratic dialogue into a calibrated reference, distinguishing actions by infraction pattern, leadership posture, and prior record rather than infraction alone. The exchange demonstrates the AI’s capacity to convert exploratory inquiry into a working reference tool, supporting the proportional judgment MCO 1900.16, Marine Corps Separation and Retirement Manual, updated through admin chap. 3, 28 May 2025, para. 1004, asks of senior enlisted leaders recommending administrative action.

Source: image generated by ChatGPT, April 2025.

 

Week 11. Lesson SN6915, Coaching

The 11th SLSSP lesson develops the staff noncommissioned officer’s repertoire of developmental leadership practices, distinguishing the related but separable functions of counseling, coaching, and mentoring. The participant requested a structured representation of coaching that consolidated definition, implementation, developmental impact, and reward into a single reference framework (figure A.7).

 

 

Figure A.7. AI-generated infographic on coaching as a developmental leadership practice, anchored in MCO 1500.61, Marine Leader Development

Analytical note: The infographic organizes coaching around four interrogatives (why, how, what effect, what reward), grounding the practice in MCO 1500.61 while translating the order’s language into visual reference. The exchange demonstrates the AI’s facility with infographic synthesis, converting a doctrinal definition into a teaching artifact suited to PME use and consistent with the developmental posture MCO 1500.61, 4–5, asks of senior enlisted leaders.

Source: image generated by ChatGPT, May 2025. 

 

Week 12. Lesson SN6916, NCO Development

The 12th SLSSP lesson completes the developmental triad introduced in the prior week, defining mentoring under MCO 1500.61 as a voluntary developmental relationship characterized by mutual trust and respect. Following an extended Socratic exchange linking self-reliance, accountability, love, and community to the warfighting profession, the participant requested a structured comparison clarifying how mentoring is distinct from counseling and coaching (table A.6).

 

 

Table A.6. How mentoring differs from coaching and counseling

Aspect	Counseling	Coaching	Mentoring
Purpose	Address performance and expectations	Develop specific skills or behaviors	Develop the whole Marine (personally and professionally)
Focus	Near-term, performance-based	Task- or skill-based improvement	Long-term growth and leadership development
Formality	Formal, documented	Informal but intentional	Voluntary, based on mutual trust
Relationship	Senior-subordinate within chain of command	Often within chain of command, may be short-term	Can be outside chain of command; based on trust and rapport
Driven by	Leadership requirement	Developmental need	Mutual respect and willingness
End state	Improved performance and compliance	Strengthened competence and confidence	Transformation into a better Marine and leader

Source: compiled by author.

 

Week 13. Lesson 6620, Military Correspondence

The 13th SLSSP lesson develops staff noncommissioned officer judgment in written communication as a leadership skill. Following an extended exchange on correcting subordinate, peer, and senior writing and the operational significance of attention to detail, the participant requested a visual synthesis consolidating SNCO responsibilities for handling poorly written correspondence into a single reference artifact (figure A.8).

 

 

Figure A.8. AI-generated comparison of counseling, coaching, and mentoring across six developmental dimensions

Analytical note: The matrix synthesizes a discursive sequence on self-reliance, accountability, and community into a calibrated reference distinguishing the three developmental practices. The exchange illustrates the AI’s capacity to integrate philosophical exploration with doctrinal precision, producing an artifact suited to PME use and consistent with the developmental architecture MCO 1500.61, 5, establishes for senior enlisted leaders.

Source: image generated by author exchange with ChatGPT, May 2025. 

 

Week 14. Lesson SN6930, Performance Evaluation

The 14th SLSSP lesson develops staff noncommissioned officer judgment in performance evaluation. Following an exchange on protecting Joint Enlisted Performance Evaluation System (JEPES) scores against unwarranted alteration and on graduated procedures for contesting unjustified reductions, the participant applied operations order logic to performance evaluation, eliciting the AI’s distinction between specified and implied tasks summarized below (table A.7).

 

 

Table A.7. Performance evaluation based on specific and implied tasks

Task type	Definition	Examples in performance evaluation
Specified	Tasks explicitly directed by policy, orders, or command guidance	•  Submit fitness reports per MCO 1610.7B, Performance Evaluation System (PES)  •  Complete JEPES command input quarterly  •  Conduct performance counseling within prescribed timelines  •  Submit Performance Evaluation Review Board (PERB) packages when appropriate
Implied	Tasks not stated but necessary for an accurate, fair, mission-aligned evaluation	•  Maintain a running performance log  •  Mentor Marines on report impact  •  Validate master brief sheet/official military personnel file accuracy  •  Seek input from other leaders  •  Ensure consistency across reporting seniors

Source: compiled by author.

 

Week 15. Lesson SN6910, Professional Military Ethics

The 15th and culminating SLSSP lesson grounds staff noncommissioned officer judgment in professional military ethics. The participant submitted a seven-part written response on the ethical warrior, value conflicts, and the architecture of the command environment, then prompted the AI to validate multiple-choice items drawn from Jack Hoban’s The Ethical Warrior and a quote by Lieutenant General James N. Mattis.[38] The participant’s concluding reflection journal, excerpted below, distills the week’s analytical and personal synthesis.

This week’s lesson on professional military ethics reminded me that being a Marine is not just about following orders. It is about upholding values that define who we are, especially when no one is watching. The concept of the ethical warrior stood out most. As Marines, we are trained to fight and win our nation’s battles, but how we fight matters. Moral values such as respect for life, integrity, and honor must guide our actions in combat, garrison, and daily interactions. As a leader, my values directly influence morale, trust, and the overall tone of the unit. Ethical behavior must be modeled, not just expected. Our core values are not optional. They are essential tools for decision-making and leadership.

 

About the Author and Transparency Statement

Ivan A. Linares supports the distance education programs as a leadership curriculum subject matter expert within the College of Distance Education and Training at Marine Corps University. This article was researched, conceived, and directed by the author. The underlying case study, including the 15-week instructional protocol, data collection, doctrinal verification, and analytical observations, was conducted by the author in their professional capacity as a curriculum advisor and leadership and ethics subject matter expert supporting Marine Corps Education Command. The artificial intelligence tool ChatGPT was used during the editorial revision process to assist with structural organization, sentence-level editing, and scholarly tone refinement. All arguments, findings, interpretations, and conclusions are the author’s own. The author takes full responsibility for the accuracy and integrity of the work as submitted. Representative examples of AI-generated visual content produced during the research process are included in the appendix.

 

Disclaimer

The views expressed in this article are those of the authors and do not reflect the official policy or position of the National Defense University, the Department of Defense, the U. S. government, Marine Corps University, or the U.S. Marine Corps.