AI Assessment Aggregation for Consistent LMS Course Feedback
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Solution Overview
Problem
In large classes, instructors face challenges in providing efficient, fair, and effective assessments due to inconsistencies among multiple assessors, leading to conflicting feedback and potential biases in course evaluation.
Innovation Solution
A method and system for course assessment that aggregates feedback from multiple assessors, utilizing AI agents to resolve inconsistencies and provide a cohesive assessment to learners, including weighted formulas and AI-driven integration of language and grade-based assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple assessors are used to evaluate learners, then the assessment can provide differing viewpoints and perspectives, but conflicts and inconsistencies arise among the feedback provided by each assessor
Solution Approach 1:
An AI agent is introduced as an intermediary to review, reconcile, and aggregate assessments from multiple human assessors. The AI agent resolves conflicts and inconsistencies by analyzing all assessor feedback and producing a unified, coherent assessment that maintains reliability while preserving the benefits of multiple perspectives.
Solution Approach 2:
The system merges multiple assessor assessments into a single unified assessment by combining their feedback through AI-driven aggregation. This process integrates differing viewpoints and perspectives while eliminating conflicts, producing a cohesive final assessment that reflects the collective expertise of all assessors.
2Measurement precision
If multiple assessors are assigned to provide feedback, then more comprehensive evaluation is achieved, but the process becomes more complex and time-consuming
Solution Approach 1:
The AI agent autonomously reviews, analyzes, and aggregates assessments from multiple assessors without requiring manual intervention to resolve conflicts. The system self-manages the complexity of coordinating multiple assessors by automatically reconciling their feedback and producing a unified assessment, thereby maintaining comprehensiveness while reducing process complexity.
Solution Approach 2:
The AI agent serves as an intermediary that simplifies the complex process of managing multiple assessors by automatically handling conflict resolution and feedback aggregation, reducing the administrative burden and complexity associated with coordinating multiple evaluators.
3Ease of operation
If traditional teaching assistants are used for assessment, then some human judgment is provided, but consistency and fairness deteriorate in large classes
Solution Approach 1:
The AI agent acts as an intermediary that enhances the work of teaching assistants by automatically reviewing and reconciling their assessments with those of other assessors. This ensures consistency and fairness across large classes while preserving the benefits of human judgment and maintaining operational feasibility.
Solution Approach 2:
The system merges the assessments of teaching assistants with those of other assessors through AI-driven aggregation, ensuring that all human judgments are considered while eliminating inconsistencies and biases. This produces a fair and consistent final assessment that leverages the feasibility of human evaluation at scale.
Data Source
AI summary
A method and system for course assessment. The method including: receiving assessments from a plurality of assessors for at least one learner; reviewing the assessments for any issues by, for example, an AI agent; if issues are found, returning the assessments for additional review by, for example, an AI agent, if not, continuing; aggregating the assessments by, for example, an AI agent; reviewing the aggregated assessment for any aggregated issues by, for example, an AI agent; if aggregated issues are found, returning the aggregated assessments for additional review by, for example, an AI agent, if not, continuing; and providing the aggregated assessment to the learner. The system makes use of processor and memory to execute instructions to implement the method.