Figure 2. From One Size Fits All Toward Adaptive Development With AI
FROM: ONE SIZE FITS ALL
TO: INDIVIDUALIZED DEVELOPMENT
Every learner moves through the same path.
Each learner follows a path tailored to their needs, role, context, and demonstrated proficiency.
AI continuously adapts development based on: Demonstrated Proficiency Job
Stronger Leader
Same Course
Learner A
Learner A
Coaching
Advanced Scenarios
Portfolio Leadership
Standardized Instruction
More Effective Practitioner
Same Content
Learner B
Assignment Operational Context Developmental Gaps Workforce Demand Signals Performance Patterns
Learner B
Refresher Support
Workflow Guidance
Risk Tools
Same Place
Adaptive Individualized Development
Learner C
Broader Contributor
Learner C
Collaboration Practice
Cross- Functional Development
Mission Simulation
Learner D
Same Outcome
High- Confidence Operator
Learner D
Real-time Decision Support
Targeted Reinforcement
Performance Feedback
•Fixed Pathways •Standard Pacing
•Periodic Instruction •Limited adaptation
Knowledge Retrieval
Decision Support
Reinforcement in Workflow
Learning
Coaching
Designed for standardization and scale
Source. Author
Emerging Workforce Adaptation
tion professionals learn, collaborate, make decisions, and perform? If the existing course-based learn - ing model is approaching the upper end of its life cycle, it’s time to ask, “What does the next model of acquisi - tion education look like?” While still in its early phases, AI is beginning to re- veal the outlines of a different learn - ing paradigm, one less dependent on static instructional events and more centered on personalized develop- ment through continuous adaptation and on-the-job support. Emergence of a new learning paradigm. Historically, acquisition education and training operated through a standardized instructional model. Learners progressed through predetermined courses, faculty de- livered a standardized curriculum developed by the institution, and learning remained largely separated from operational execution. The re - sult was a one-size-fits-all approach to learning, regardless of individual experience or developmental needs. AI-enabled systems can change this
relationship between the institution and its learners. AI reshapes acquisition education from a standardized, course-centric instructional model toward a more adaptive, individualized, and work- flow-integrated approach to work- force development (Figure 2). Instead of moving every learner through the same courses and train- ing experiences, AI systems can per - sonalize learning based on an in- dividual’s needs, performance, job assignments, and work environment. As a result, learning can be tailored to individual development points rather than requiring everyone to follow the same path. At the same time, AI embeds learn- ing within an individual’s workflow rather than confining it to classrooms or discrete instructional events. Guid- ance, coaching, knowledge retrieval, decision support, and targeted feed- back occur in real time during execu - tion. In effect, AI shifts learning from episodic instructional events toward continuously adaptive operational support at the point in time of need.
Early indicators of this shift toward the use of AI are appearing within acquisition education and training. During a recent Services Acquisition Workshop, students described nu- merous ways they already integrate AI into their day-to-day work to support analysis, writing, and decision-mak- ing. Similar patterns emerged during recent executive-level courses, where participants integrated AI directly into simulation activities. While learners were encouraged to explore AI’s use during portions of the simulation, many incorporated it into planning, analysis, coordination, and decision- support activities in ways that mir- ror how they use AI in the workplace. This suggests the acquisition work- force may already be adapting to AI- enabled ways of working faster than acquisition education is evolving. Simulations evolve significantly within this emerging paradigm. Rather than functioning as one-time training events, simulations become evolving learning environments
JULY – AUGUST 2026 | DEFENSE ACQUISITION MAGAZINE 15
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