Adaptive Prompting Systems for Domain-Specific Professional Training

Marcus Ellison

Adaptive Prompting Systems for Domain-Specific Professional Training

Author(s)	Dr. Marcus Ellison
Country	United States
Abstract	Professional training increasingly requires learning systems that can respond to differences in prior knowledge, task complexity, professional role, error patterns, regulatory context, and learner confidence. Generative artificial intelligence has created new possibilities for individualized explanations, simulated professional scenarios, formative feedback, reflective questioning, and practice dialogue, yet many implementations continue to rely on static prompts that deliver broadly similar instructional behavior regardless of the learner's evolving state. Adaptive prompting offers a different model in which instructional prompts are dynamically selected, modified, or routed according to domain knowledge, competency level, observed errors, interaction history, assessment evidence, and training objectives. This study develops a Domain-Specific Adaptive Prompting Framework for Professional Training (DAPF-PT) that combines learner-state modeling, domain-grounded knowledge, prompt routing, progressive scaffolding, formative assessment, source verification, and human professional oversight. Because verified training-intervention data were not available for the present manuscript, the study adopts an explicitly simulation-based methodological design. A synthetic analytical corpus of 480 hypothetical professional-training interactions was distributed across four prompting architectures: static domain prompting, rule-based adaptive prompting, learner-state prompt routing, and domain-specific adaptive prompting with professional oversight. Six dimensions—domain fidelity, learner-state sensitivity, scaffolding appropriateness, feedback quality, assessment alignment, and governance reliability—were used to construct a Professional Training Readiness Index. The simulated index increases from 52 for static prompting to 91 for the integrated adaptive architecture. Contemporary evidence supports the potential of personalized prompting while also showing that general-purpose language models do not automatically reproduce the adaptivity of established intelligent tutoring systems. The paper therefore argues that effective adaptive prompting must be deliberately engineered around professional competencies rather than treated as unrestricted conversational personalization. The proposed framework positions generative AI as a supervised instructional partner capable of varying challenge, explanation, feedback, and practice while preserving domain standards, professional judgment, and human accountability.
Keywords	: adaptive prompting, professional training, generative artificial intelligence, domain-specific learning, intelligent tutoring systems, personalized learning, prompt routing, professional competence
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2022
Published On	2022-01-30

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American Journal of Advanced Multidisciplinary Innovation and Research

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Adaptive Prompting Systems for Domain-Specific Professional Training

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