Screen Reader Micro-Prompting for Cognitive Task Errors
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Solution Overview
Problem
Individuals with cognitive disabilities face challenges in completing semi-repetitive tasks like reading due to frequent mistakes in sequence, which existing prompting software fails to address effectively, leading to continuous and unnecessary prompting.
Innovation Solution
A method of providing sporadic micro-prompts for semi-repetitive tasks, using a screen reader that highlights correct sentences and issues prompts when mistakes are detected, allowing users to correct errors before proceeding, with options to wait for user action or automatically correct mistakes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous prompting is provided to users with cognitive disabilities during semi-repetitive tasks, then task completion accuracy improves, but user frustration increases and prompting frequency becomes unnecessarily high
Solution Approach 1:
The system monitors user actions in real-time and provides feedback prompts only when a mistake is detected. The feedback mechanism tracks the sequence of actions performed by the user and compares it against the correct task sequence, issuing prompts selectively rather than continuously. This resolves the contradiction by providing sufficient feedback to ensure accuracy while avoiding excessive prompting that causes frustration.
Solution Approach 2:
Instead of continuous prompting, the system uses periodic monitoring of user actions and provides prompts only at specific moments when mistakes are detected. The prompting is periodic in nature, triggered by specific conditions (detected errors) rather than occurring continuously throughout the task. This reduces prompting frequency while maintaining task completion accuracy.
2Reliability
If existing prompting software is used for semi-repetitive tasks, then some task sequence errors are caught, but the software fails to detect mistakes accurately resulting in continuous and unnecessary prompting
Solution Approach 1:
The system employs a sophisticated feedback mechanism that monitors user actions and provides prompts only when actual mistakes are detected. The feedback is based on accurate recognition of task sequence deviations, ensuring that prompting occurs only when necessary rather than continuously. This improves mistake detection accuracy while reducing unnecessary prompting frequency.
Solution Approach 2:
The system changes the parameter of prompting from continuous to conditional based on detected errors. By monitoring task sequence parameters and comparing user actions against the correct sequence, the system adjusts prompting frequency dynamically based on actual performance needs rather than applying a fixed continuous prompting rate.
3Speed
If users are allowed to proceed without correction after mistakes in semi-repetitive tasks, then task flow speed increases, but error accumulation occurs leading to incorrect task completion
Solution Approach 1:
The system takes preliminary action by detecting mistakes and issuing correction prompts before users can accumulate multiple errors. By intervening at the point of first error detection, the system prevents error accumulation that would lead to incorrect task completion, while still allowing users to proceed with the task flow rather than blocking them entirely.
Solution Approach 2:
The feedback mechanism provides timely correction prompts when mistakes are detected, allowing users to correct errors and continue. This maintains task flow speed by not blocking progression while ensuring accuracy through immediate feedback on errors, preventing the accumulation of multiple mistakes.
Data Source
AI summary
A method is provided for prompting a user who interacts with content on a display screen via a pointing device that a potential mistake may have been made during the interaction. The interaction includes a plurality of actions associated with the content of the display screen. The content is created by programming code. Programming code is analyzed to determine the next most likely action to occur during user interaction with the display screen contents at a plurality of different points in the programming code. After an action occurs by a user at a point in the programming code, it is detected whether the user performed the next most likely action. The user is prompted with an indication if the user did not perform the next most likely action and therefore may have made a potential mistake.