Vol. 2 No. 1 (2027): AI Literacy, Workforce Learning, and Human–AI Futures
Articles

Extending Henschke’s MIPI: An AI-supported framework for instrument design in doctoral research across disciplines

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  • John Henschke
John Henschke
University of Missouri & Lindenwood University
DOI: https://doi.org/10.46787/ijaipil.v2i1.7451

Published 2026-05-15

Keywords

  • MIPI, AI-supported instrument design, doctoral research, andragogy, scale development, research methodology, higher education

How to Cite

Henschke, J. (2026). Extending Henschke’s MIPI: An AI-supported framework for instrument design in doctoral research across disciplines. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2(1). https://doi.org/10.46787/ijaipil.v2i1.7451

Copyright (c) 2026 John A. Henschke

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Doctoral students frequently face challenges in designing valid and reliable research instruments, often due to limited structured frameworks, inconsistent methodological guidance, and time constraints. This article addresses these issues by extending Henschke’s Modified Instructional Perspectives Inventory (MIPI) into an AI-Supported Instrument Design (AI-SID) framework. Grounded in andragogical theory (Knowles, 1972; Henschke, 2008, 2017, 2020, 2021), the proposed model integrates human-centered theoretical constructs—beliefs, feelings, and behaviors—with AI-assisted processes to enhance item generation, refinement, validation, and cross-disciplinary adaptation. The framework outlines a five-stage process that combines conceptual grounding, human–AI co-design, AI-assisted validation, iterative pilot testing, and contextual scalability. By positioning artificial intelligence as a complementary tool rather than a replacement for scholarly judgment, AI-SID promotes increased rigor, efficiency, and accessibility in doctoral research. The article contributes a practical, scalable model aligned with emerging trends in AI integration in higher education and calls for future empirical validation across disciplines.

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