# SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI

arXiv: https://arxiv.org/abs/2606.15601 · HTML: https://mlsanity.com/paper/2606.15601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 153 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 24 of 24)

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- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
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- [Conversational agents for fostering curiosity-driven learning in children](https://mlsanity.com/paper/2204.03546/) (2022)
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- [A Systematic Review of Generative AI for Teaching and Learning Practice](https://mlsanity.com/paper/2406.09520/) (2024)
- [Complement or substitute? How AI increases the demand for human skills](https://mlsanity.com/paper/2412.19754/) (2024)
- [Coach not crutch: Evidence that AI can improve writing skill despite reducing effort](https://mlsanity.com/paper/2502.02880/) (2025)
- [When Autonomy Breaks: The Hidden Existential Risk of AI](https://mlsanity.com/paper/2503.22151/) (2025)
- [Position: AI as Part of Self -- Extending the Mind Requires Cognitive Co-Regulation](https://mlsanity.com/paper/2605.16197/) (2026)

## Abstract (arXiv)

We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition. In SCAN, we systematize and formalize AI-human interaction by introducing a task-identification approach with four "sub-zones": Substitute, Complement, Aid, and Non-negotiable. After describing the four sub-zones, we demonstrate how SCAN framework can be applied for knowledge workers in the workplace and students in education to metacognitively "scan" their use of Generative AI. We then discuss how such framework can be related to cognitive load theory, cognitive offloading, sycophancy, three decision-making modes in human-AI interactions (automation, augmentation, and collaboration), future of work such as upskilling and deskilling, and how it accounts for both human-human and human-AI learning. We propose that SCAN offers a great starting point before discussing whether GenAI complements or replaces our abilities when completing a task, with a general objective of sustaining lifelong learning, and a specific goal of reaching hybrid intelligence.