HCAICRLGDec 12, 2022

Interactive introduction to self-calibrating interfaces

arXiv:2212.05766v12 citationsh-index: 17
Originality Synthesis-oriented
AI Analysis

It provides an educational tool for curious minds to learn about an interaction paradigm, but it is incremental as it builds on existing research without new experimental results.

This interactive paper introduces the self-calibrating interface paradigm, which allows interfaces to adapt to user preferences in real-time, demonstrated through a PIN entering task with increasing input complexity from buttons to spoken words.

This interactive paper aims to provide an intuitive understanding of the self-calibrating interface paradigm. Under this paradigm, you can choose how to use an interface which can adapt to your preferences on the fly. We introduce a PIN entering task and gradually release constraints, moving from a pre-calibrated interface to a self-calibrating interface while increasing the complexity of input modalities from buttons, to points on a map, to sketches, and finally to spoken words. This is not a traditional research paper with a hypothesis and experimental results to support claims; the research supporting this work has already been done and we refer to it extensively in the later sections. Instead, our aim is to walk you through an intriguing interaction paradigm in small logical steps with supporting illustrations, interactive demonstrations, and videos to reinforce your learning. We designed this paper for the enjoyments of curious minds of any backgrounds, it is written in plain English and no prior knowledge is necessary. All demos are available online at openvault.jgrizou.com and linked individually in the paper.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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