AI Sprints: Towards a Critical Method for Human-AI Collaboration

TL;DR

This paper proposes 'AI sprints' as a novel human-AI collaborative research method combining critical reflexivity with iterative AI dialogue, aiming to transform humanities research and understanding of digital objects.

Contribution

It introduces a new research methodology called 'AI sprints' that integrates critical humanistic inquiry with iterative AI engagement, expanding digital methods.

Findings

Demonstrates how AI sprints facilitate adaptive data analysis.

Identifies three cognitive modes: delegation, augmentation, overhead.

Provides a theoretical framework for epistemological shifts in hybrid research.

Abstract

The emergence of Large Language Models presents a remarkable opportunity for humanities and social science research. I argue these technologies instantiate what I have called the algorithmic condition, whereby computational systems increasingly mediate not just our analytical tools but how we understand nature and society more generally. This article introduces the possibility for new forms of humanistic inquiry through what I term 'AI sprints', as intensive time-boxed research sessions. This is a research method combining the critical reflexivity essential to humanistic inquiry with iterative dialogue with generative AI. Drawing on experimental work in critical code studies, I demonstrate how tight loops of iterative development can adapt data and book sprint methodologies whilst acknowledging the profound transformations generative AI introduces. Through examining the process of human-AI collaboration when undertaken in these intensive research sessions, I seek to outline this approach as a broader research method. The article builds on Rogers' digital methods approach, proposing that we extend methodologies to study digital objects through their native protocols, using AI systems not merely to process digital traces but to analyse materials traditionally requiring manual coding or transcription. I aim to show this by introducing three cognitive modes, cognitive delegation, productive augmentation, and cognitive overhead, explaining how researchers can maintain a strategic overview whilst using LLM capabilities. The paper contributes both a practical methodology for intensive AI-augmented research and a theoretical framework for understanding the epistemological transformations of this hybrid method. A critical methodology must therefore operate in both technical and theoretical registers, sustaining a rigorous ethical-computational engagement with AI systems and outputs.