- Part I: AI psychometric foundations
- Part 2: Methods and tools
- Part 3: Psychometric analyses
- Part 4: Hybrid models
- Part 5: Critical perspectives
- References
Part I: AI psychometric foundations
Definition and introduction to foundational concepts in AI psychometrics, positioning transformers as a threshold capability marking a milestone in AI psychometrics.
What is AI psychometrics?Reasons for AI psychometric progress What’s in an LLM?Encoder architecturesDecoder architecturesEncoder-decoder architecturesTraining a small LLM yourselfLLMs and psychometric biasPart 2: Methods and tools
Use of AI to develop the building blocks of assessments, including agents and multi-agent systems in task analysis, test blueprinting, test administration, scoring, and feedback.
Moral foundations as a use caseDo moral foundations matter in business?Semantic convergent and discriminant validityAI item generation strategiesGenerating items via an APIPart 3: Psychometric analyses
Introduces semantic alignment for item validity, pseudo factor analysis, network analyses, and convex hulls with applications to item analysis and free text scoring.
Semantic item alignmentSemantic item alignment resultsPseudo factor analysis Pseudo factor analysis resultsConvex hull analysesEmbedding compositionsArtificial crowdsModel fine tuningPart 4: Hybrid models
Integrating transformers with other computational psychometric methods, such as predictive ensembles with embeddings as inputs.
Part 5: Critical perspectives
Limitations, ethical and privacy concerns, including geographic/cultural differences in expectations, as well as future directions.
References
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