[Paper] Meflex: A Multi-agent Scaffolding System for Entrepreneurial Ideation Iteration via Nonlinear Business Plan Writing

Source: arXiv - 2602.15631v1

Overview

The paper introduces Meflex, a novel writing assistant that leverages large language models (LLMs) to help entrepreneurship students craft business plans (BPs) in a non‑linear, iterative fashion. By coupling a flexible “idea canvas” with AI‑driven reflection prompts, Meflex aims to lower the cognitive load of ideation and make the BP drafting process more aligned with how real startups evolve.

Key Contributions

• Non‑linear BP scaffolding – a canvas that lets users jump between sections, merge ideas, and revisit earlier drafts without a rigid linear flow.
• LLM‑powered reflection & meta‑reflection – AI‑generated prompts that guide users to think divergently (explore alternatives) and then step back to evaluate the overall coherence of their plan.
• Empirical validation – a 30‑participant exploratory study measuring usability, cognitive load, and reflective depth, showing statistically significant improvements over traditional linear tools.
• Design guidelines for future AI‑augmented writing systems targeting complex, multi‑step creative tasks (e.g., product road‑mapping, grant proposals).

Methodology

1. System Design – Meflex combines a drag‑and‑drop canvas (sections like “Value Proposition”, “Market Analysis”, etc.) with an LLM backend (GPT‑4‑style) that generates context‑aware reflection questions and suggestion snippets.
2. User Study – 30 undergraduate entrepreneurship students were split into two groups:
   • Meflex group (interactive canvas + AI prompts)
   • Control group (standard linear BP editor)
     Participants completed a full business plan over two 90‑minute sessions.
3. Measurements –
   • Usability (System Usability Scale)
   • Cognitive Load (NASA‑TLX)
   • Reflective Thinking (self‑report scales for divergent and meta‑reflective processes)
   • Qualitative feedback via post‑session interviews.

Results & Findings

Qualitative comments highlighted that the ability to re‑arrange sections on the fly and receive targeted “what‑if” questions kept the creative flow alive, whereas the linear editor felt “stuck” after the first draft.

Practical Implications

• For EdTech platforms – Meflex’s canvas can be embedded into entrepreneurship courses, hackathon tools, or incubator portals to make BP drafting more interactive and less intimidating for novices.
• For product teams – The same non‑linear, AI‑augmented scaffolding can be repurposed for product requirement documents, road‑maps, or grant applications, where iterative refinement is key.
• For developers – The architecture (front‑end canvas + LLM API) is modular; swapping in a newer model or adding domain‑specific prompts (e.g., fintech, health‑tech) is straightforward.
• Reduced cognitive overload – By offloading “what‑to‑think‑next” to the AI, users can focus on content quality rather than procedural bookkeeping, potentially accelerating prototype‑to‑market cycles.

Limitations & Future Work

• Sample size & diversity – The study involved a single university cohort; broader validation across cultures and experience levels is needed.
• LLM reliability – Occasionally the AI generated suggestions that were tangential or factually inaccurate, requiring manual correction.
• Long‑term retention – The paper does not assess whether the reflective skills persist after the tool is removed.
• Future directions suggested by the authors include: integrating real‑time market data APIs for more grounded suggestions, expanding the canvas to support collaborative multi‑user editing, and conducting longitudinal studies to measure impact on actual venture outcomes.

Authors

• Lan Luo
• Dongyijie Primo Pan
• Junhua Zhu
• Muzhi Zhou
• Pan Hui

Paper Information

• arXiv ID: 2602.15631v1
• Categories: cs.HC, cs.SE
• Published: February 17, 2026
• PDF: Download PDF