Validation Results

Quantitative and qualitative results from MOTIVE's multi-domain validation study. Evaluation scores, expert feedback, and cross-model testing.

Study Demographics

Participants N = 30
Professional Domains 10
Experience Range 2-15 years
AI Models Tested GPT-4, Claude 3, Gemini
Evaluation Method Mixed-Methods

Study Design

The validation study employed a within-subjects design where each participant created prompts for domain-specific tasks both with and without the MOTIVE framework. Prompts and outputs were evaluated by domain experts and cross-model consistency was assessed.

Phase 1: Baseline prompt creation
Phase 2: MOTIVE training session
Phase 3: MOTIVE-structured prompt creation
Phase 4: Expert evaluation & interviews

Quantitative Results

Average scores on a 1-5 Likert scale across all domains and models.

Structural Completeness

Before
2.1
→
After
4.3
+105%

Output Relevance

Before
2.8
→
After
4.1
+46%

Actionability

Before
2.4
→
After
4.0
+67%

Cross-Model Consistency

Before
2.2
→
After
4.2
+91%

Domain Specificity

Before
2.5
→
After
4.1
+64%

Evaluation Criteria Presence

Before
1.4
→
After
4.4
+214%

Qualitative Themes

Key themes identified from participant interviews and feedback.

Reduced Cognitive Overhead

Participants reported that the structured format reduced the mental effort required to formulate comprehensive prompts.

Improved Confidence

Users expressed greater confidence in their prompt quality when following the MOTIVE structure.

Cross-Domain Transferability

Participants found the framework applicable across different professional contexts without significant adaptation.

Evaluation as Driver

The Evaluation component was cited as the most impactful addition, providing previously missing quality assurance.

Limitations

  • • Sample size (N=30) limits generalizability; larger-scale validation is planned.
  • • Participants received MOTIVE training, which may introduce a learning-effect bias.
  • • AI model outputs evolve rapidly; results may shift with model updates.
  • • Domain expert evaluation introduces subjectivity despite structured rubrics.