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  • Open Access
  • Article

3P Framework (Prompt, Persist, Produce): Accelerating Skill Development and End-to-End Results through Human-AI Collaboration

  • Neal Cody 1, †,   
  • Veda Sripada 1, 2, †,   
  • Katherine Papciak 1,   
  • Isaac Oyediran 1,   
  • Amanda F. Yanke 1,   
  • Bakhtawer B. Baloch 3,   
  • Chien-Hung Lu 1,   
  • Jameson Born 1,   
  • Nwe N. Aung 1,   
  • Prakruthi Harish 1,   
  • Rishav E. S. Dasgupta 1,   
  • Sashank B. Narayan 4,   
  • Gurkirat Sekhon 5,   
  • Natali Kolker 6,   
  • Nisha Singh 3,   
  • Oleksandr Romanenko 6,   
  • Eugene Kolker 4, 6, 7, 8, 9, *

Received: 29 May 2025 | Revised: 30 Jun 2025 | Accepted: 05 Nov 2025 | Published: 17 Nov 2025

Abstract

This work demonstrates how Human-AI collaboration can substantially accelerate advanced skill development and end-to-end results (transition “from learning to doing”) in complex fields that traditionally require extensive, lengthy training. In a graduate-level course “AI, Generative AI, and Data Science for Biomedical Informatics” taught by Eugene Kolker, the experimental group with little to no prior experience rapidly progressed from instruction to producing credible, real-world results. Enabled by Generative AI (GenAI), the experimental group members processed data, generated code, interpreted results, synthesized literature, and documented findings over a focused 20–hour implementation phase. Applying these sophisticated skills to a rigorous, multi-step analysis of eukaryotic enzymes, they observed underlying preferred sizes, consistent with and extending upon prior work. Notably, the more experienced control group failed to reproduce these results even after 50% more time. This striking success-failure differential was enabled by the 3P (Prompt, Persist, Produce) framework. 3P is a straightforward, robust, and reproducible Human empowerment methodology that draws its inspiration from the Socratic Method, Experiential Learning Theory, and Systems Thinking. The 3P approach is based on three pillars: (1) optimally structured communication, (2) Human expert facilitation (“player-coach” function), and (3) GenAI systems. Comparison of the results obtained by the experimental group with 3P (and GenAI) versus control group without 3P (and GenAI) revealed that 3P significantly shortens the time from question to insight and from learning to outcomes. Through Human-AI collaboration, 3P accelerates advanced skill development and end-to-end results, including, for instance, the preparation of this manuscript. The 3P “from learning to doing” paradigm not only transforms traditional training curves, but also offers a rapid, scalable, field-agnostic blueprint for accelerated research and development, innovation, workforce upskilling and reskilling, and measurable, end-to-end results.

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Cody, N.; Sripada, V.; Papciak, K.; Oyediran, I.; Yanke, A. F.; Baloch, B. B.; Lu, C.-H.; Born, J.; Aung, N. N.; Harish, P.; Dasgupta, R. E. S.; Narayan, S. B.; Sekhon, G.; Kolker, N.; Singh, N.; Romanenko, O.; Kolker, E. 3P Framework (Prompt, Persist, Produce): Accelerating Skill Development and End-to-End Results through Human-AI Collaboration. LifeAI 2025, 1 (1), 3.
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