Exploring an Invisible Version Control Technique to Reduce the Cognitive Load of Research Workflows

Despite the cognitive demands of research workflow, a critical gap exists in research tools: they force a trade-off between usability and robust data safety. Consequently, researchers rely on manual file management, creating ‘Version Chaos’ (e.g., thesis_v1.docx, final_v5.docx) that imposes severe extraneous cognitive load. This friction stems from two systemic failures: a ‘Skills Divide,’ where secure tools like Git are too technical for non-STEM scholars, and a ‘Connectivity Divide,’ where unstable cloud platforms force a reliance on vulnerable offline word processors. To address this gap, the study employs a three-phase Design Science Research (DSR) mixed-methods approach. Data was collected via structured surveys (N=48) and open-ended questionnaires (n=15) and was evalu-ated using descriptive statistics and inductive thematic analysis. First, quantitative results established a baseline of workflow friction, proving that manual file management triggers ‘Version Anxiety’ and interrupts the Flow State. Second, the study designed a high-fidelity ‘Local-First Automated Version Control’ (LFAVC) prototype. Finally, qualitative narratives validated the prototype, and the prototype's “Invisible Versioning” successfully shifts the burden of workflow processes from the human to the machine. Then, its ‘Velocity Analytics’ provides the visual feedback necessary to sustain long-term motivation. The study concludes that future aca-demic tools must provide automatic version control without requiring technical skills. Practically, these findings provide software developers and HCI designers with a clear framework to build accessible, low-stress writing environments that ensure local control over data and reduce researcher burnout.