Research Paper
Read the peer-reviewed academic paper introducing the MOTIVE framework for human-centered prompt engineering.
Citation
Sienou, A. (2026). MOTIVE: A Structured, Model-Agnostic Framework for Human-Centered Prompt Engineering. In Proceedings of the IEEE International Conference on Advanced Computing and Digital Systems Architecture (ACDSA 2026). IEEE. DOI: 10.1109/ACDSA2026.XXXXX
Abstract
Current approaches to prompt engineering lack systematic structure, reproducibility, and evaluation criteria. This paper introduces MOTIVE, a six-component framework designed for human-centered prompt engineering that is model-agnostic and applicable across professional domains. MOTIVE provides a tiered architecture (Essential, Professional, Governance) that scales prompt complexity to task requirements, five baseline archetypes (Explainer, Planner, Summarizer, Critic, Ideator) for common professional tasks, and component-level evaluation rubrics for iterative refinement. The framework is grounded in established theories from cognitive science, instructional design, and human-AI interaction. A multi-domain validation study across 10 professional domains and three leading AI models demonstrates significant improvements in prompt structural completeness, output quality, and cross-model consistency. MOTIVE offers a practical, evidence-based approach to transforming prompt engineering from ad-hoc practice to systematic discipline.
Key Findings
Structural Completeness
Average improvement in prompt component coverage across all domains.
Output Quality
Increase in evaluator-rated output quality using MOTIVE-structured prompts.
Cross-Model Consistency
Agreement rate across GPT-4, Claude 3, and Gemini on structured prompt outputs.
Practitioner Adoption
Average usability rating from pilot study participants across 10 domains.
Theoretical Foundations
Explore the theoretical underpinnings of MOTIVE, including instructional design theory, cognitive load theory, and human-AI interaction research.
Learn more →Validation Results
Quantitative and qualitative results from MOTIVE's multi-domain validation study. Evaluation scores, expert feedback, and cross-model testing.
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