Semantic Question Recommendations for Survey Ordering and Phrasing
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Solution Overview
Problem
Conventional survey creation systems suffer from inefficiencies, lack of accuracy, and inflexibility, leading to incomplete and inadequate responses, wasting computing resources and failing to optimize surveys with respect to question type, order, and phrasing.
Innovation Solution
A question recommendation system that provides customized suggestions to users during survey creation, optimizing surveys by suggesting questions, reordering, adding branching logic, and removing questions, using semantic labels and survey graphs to enhance respondent engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional survey creation tools are provided to enable individuals to build surveys, then survey creation functionality is improved, but survey optimization with respect to question type, order, and phrasing deteriorates
Solution Approach 1:
The system automatically analyzes survey questions and provides optimization suggestions without requiring manual intervention from survey creators. The AI system self-services by identifying question ordering issues, phrasing improvements, and structural optimizations based on the survey content and purpose
Solution Approach 2:
The system provides feedback to survey creators about potential improvements to their survey questions, including suggestions for reordering questions, rephrasing for clarity, and optimizing question types. This feedback loop enables continuous improvement of survey quality
2Adaptability or versatility
If survey questions are allowed to span various topics and wording styles, then survey flexibility is improved, but the ability to encode and organize survey questions uniformly deteriorates
Solution Approach 1:
The system transforms diverse survey questions into a uniform representation by analyzing semantic meaning, question type, and structural parameters. It encodes different questioning styles into standardized categories while preserving the original intent and diversity of questions
Solution Approach 2:
The system replaces manual categorization and encoding of survey questions with AI-based semantic analysis. This substitution enables automatic uniform encoding of diverse questions based on their meaning and purpose rather than requiring manual standardization
3Ease of operation
If respondents are sent to complete surveys without optimization, then survey distribution is simplified, but response completion rates deteriorate
Solution Approach 1:
The system performs preliminary optimization of survey questions before distribution, analyzing and suggesting improvements to question ordering, phrasing, and structure. This preliminary action ensures surveys are optimized for completion before being sent to respondents
Solution Approach 2:
The system provides feedback to survey creators about potential issues that may lead to non-completion, such as question ordering problems or ambiguous phrasing. This feedback enables creators to improve surveys before distribution to increase completion rates
Data Source
AI summary
The present disclosure relates to a question recommendation system that intelligently optimizes a survey being created by a user by providing customized suggestions. For example, in one or more embodiments, the question recommendation system provides a suggested question based on questions previous added by a user while creating a survey. In particular, the question recommendation system provides various recommendations to the user to further optimize a survey being created. For instance, the question recommendation system provides recommendations with respect to improving question ordering, question phrasing, and question type as well as recommends removing potentially inefficient questions.