AI Explanation Supplementing for Novice Software Users
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Solution Overview
Problem
Software applications provide explanations for results that lack sufficient detail for novice users, making them unhelpful.
Innovation Solution
A user interface tool allows users to select from multiple modalities to supplement explanations using a generative artificial intelligence model, generating detailed explanations, follow-up questions, term definitions, or example scenarios based on natural language prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the software application provides a standard explanation for results, then the explanation can be generated efficiently, but the explanation lacks sufficient detail for novice users
Solution Approach 1:
The explanation system transitions from a static, fixed explanation to a dynamic, adaptive explanation that changes based on user characteristics. The system detects whether a user is novice or experienced and automatically adjusts the explanation detail level accordingly, providing comprehensive details for novices while maintaining efficiency for experienced users.
Solution Approach 2:
The system changes the explanation parameter (detail level) based on user type. For novice users, the system increases the explanation detail parameter to provide thorough guidance, while for experienced users, it maintains a more concise explanation, thus resolving the contradiction between information completeness and generation efficiency.
2Ease of operation
If the explanation is simplified for experienced users, then the explanation generation is faster, but novice users find the explanation insufficient
Solution Approach 1:
The explanation dynamically adapts to user needs by detecting user expertise level and adjusting the explanation complexity accordingly. This ensures that novice users receive detailed, easy-to-understand explanations while experienced users receive concise information, optimizing both ease of operation and time efficiency for different user segments.
Solution Approach 2:
The system applies different explanation qualities to different user groups. Instead of a uniform explanation, it provides localized explanation depths tailored to each user's expertise level, ensuring that novice users get comprehensive guidance while experienced users get succinct information, thereby improving ease of operation without unnecessarily increasing time for experienced users.
3Adaptability or versatility
If the software application uses a single explanation format, then the system is simpler to implement, but it cannot accommodate different user needs
Solution Approach 1:
The explanation system becomes dynamic by automatically detecting user characteristics and adjusting explanation format accordingly. The system maintains relative simplicity through automated detection while achieving high adaptability by seamlessly providing user-specific explanation formats without requiring manual configuration or complex user input.
Solution Approach 2:
The system performs self-service by automatically detecting user expertise levels and selecting appropriate explanation formats without requiring explicit user specification. This self-adjusting mechanism provides versatile, user-specific explanations while keeping the system implementation relatively simple, as the system handles the complexity internally through automated detection and adaptation.
Data Source
AI summary
A method for generating supplemental content for an explanation for a particular result determined by a software application includes receiving data indicative of a user selecting a first modality of a plurality of different modalities for supplementing the explanation. In response to receiving the data, the method includes providing inputs to a generative artificial intelligence model. The inputs include data indicative of the explanation and data indicative of a first natural language prompt associated with the first modality. The method includes receiving an output from the generative artificial intelligence model. The output includes supplemental content for the explanation. The method includes displaying the supplemental content for viewing via a user interface.