Abstraction and processing preferences, but not self-assessment, were significant predictors of perceived response complexity, abstraction, and processing style¶

Claim · All claims
Evidence · 1 study (2 entries) · causal r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A Bayesian ordinal mixed-effects model confirmed abstraction preference as a significant predictor of perceived complexity while self-assessment was not significant. → Saptarshi Basu 2026 q2 i? An ordinal mixed-effects model identified processing preference and Bloom's level as significant predictors of perceived processing style. → Saptarshi Basu 2026 (2)

Evidence¶

Saptarshi Basu 2026¶

Saptarshi Basu, Sandeep Kakar, Ashok Goel. (2026). A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant. https://arxiv.org/abs/2609.03402 doi:10.48550/arxiv.2609.03402

q2 · i? · causal · r1

Bayesian ordinal mixed-effects model on the five evaluators' perceived-complexity ratings (five-level ordinal scale); technical abstraction preferences were associated with higher perceived complexity (Fig. 6). Self-assessment was not significant.

"A Bayesianordinalmixed-effectsmodelconfirmedabstraction preferenceasasignificantpredictorofperceivedcomplexity, while self-assessment was not significant (Fig. 7)."

Saptarshi Basu 2026 (2)¶

Saptarshi Basu, Sandeep Kakar, Ashok Goel. (2026). A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant. https://arxiv.org/abs/2609.03402

q2 · i? · design · r2

Ordinal mixed-effects model on evaluators' perceived processing-style ratings (reflective, neutral, active) from the same 390-evaluation human study; fixed-effect estimates shown in Figure 9. Perceived abstraction ratings also aligned with intended preferences (Fig. 8).

"The ordinal mixed- effects model identified both processing preference and Bloom'slevelassignificantpredictorsofperceivedprocess- ing style (Fig. 9)."

Discussion¶

Related Claims¶

• In human evaluation, abstraction preference was associated with perceived response quality, and more complex questions generally received lower ratings — related
• Higher self-assessment, verbosity, reflective processing, and technical abstraction are associated with greater response complexity, and Evaluation and Analysis questions tend to yield more complex responses — related
• Prompt-based personalization of an LLM/RAG teaching assistant produces measurable variation in response expression, length, and complexity across personalization conditions — a broader claim this one bears on