rightmodet

Rightmodet is a theoretical framework in human–computer interaction and artificial intelligence that describes how a system can align its behavior with a user's current goal by selecting the most appropriate mode of operation. The core idea is to infer user intent from context and actions and to switch between modes—such as advisory, assistive, autonomous, or manual—in a way that remains transparent and reversible.

The term emerged in discussions about mixed-initiative control in the early 2020s and has been used by researchers and practitioners across software design, robotics, and accessibility. It is not tied to a single organization, standard, or product, and its definitions vary across sources, reflecting differences in application domains and levels of automation.

Core principles include intent inference from input signals and environmental context, a clear taxonomy of modes with defined rights and responsibilities for both user and system, transparent feedback about the current mode and the rationale for transitions, and safeguards to prevent abrupt or ambiguous mode changes.

Applications are envisioned in consumer software to reduce cognitive load, in collaborative robots and vehicles to support safe handovers, and in accessibility tools to adapt to users with different abilities. In each case, rightmodet aims to minimize mode errors and miscommunication between user and system.

Critics warn that mode switching can introduce confusion if modes are not clearly defined or consistently communicated, and that intent inference may misinterpret user goals. Standardization, interoperability, and user control over the level of automation remain key challenges.

See also: human-in-the-loop, mode confusion, adaptive user interfaces.