MAP Multi-AI Peer Review Control Study: Final Report

Although the MAP Peer Review control study utilized a small control group (n = 4), the findings can still be considered internally valid under certain conditions. Internal validity refers to whether the study accurately measures the intended outcomes without interference from confounding variables (Shadish, Cook, & Campbell, 2002). Because all papers were evaluated using the same standardized rubric, weighting system, and multi-AI consensus, the study design minimized internal threats to validity.

However, external validity, or the ability to generalize findings to a broader population, is naturally limited in small-sample studies (Creswell & Creswell, 2018). A sample of four papers provides pilot-level evidence.

Key Factors Supporting Validity:

Consistency of Results – The control study produced narrow score variance (SD = 2.86%) and a score range of only 6.15%, which supports reliability despite the small sample size.

Controlled Conditions – Standardized criteria, uniform weighting, and AI model consensus enhance internal validity, even with a limited sample (Shadish et al., 2002).

Pilot Study Role – Small studies are appropriate for testing methods or frameworks before scaling to larger samples, as they allow researchers to identify strengths and refine processes (Gall, Gall, & Borg, 2014).

Conclusion on Sample Size

The small control group in this study produced valid internal results, confirming that the MAP Peer Review method functions reliably under controlled conditions. As pointed out by Creswell & Creswell (2018), external generalization will require a larger sample size and replication across more disciplines to strengthen confidence in its universal applicability.

Introduction

This final report presents the findings of a Multi-AI Peer Review (MAP) control study conducted on four academic papers across multiple disciplines:

Veterinary socio-economics
Agronomy/Nutritional science
Educational psychology
Philosophical/Theological research

The purpose of this control study was to evaluate the consistency, reliability, and validity of the MAP Peer Review method in providing objective, actionable academic evaluations across diverse disciplines. The study also measured whether papers met the requirements for the AI Accuracy Seal, which signifies 100% factual accuracy and ≥90% AI Accuracy Across 6 Criteria.

Universities

Stanford University (USA) – Globally elite and top-ranked across multiple disciplines.
University of Michigan (USA) – A globally elite public research university with historic strength in philosophy and research.
University of Ibadan (Nigeria) – Highly respected in Africa, especially in medicine and agriculture.
FUNAAB (Nigeria) – Regionally respected in agricultural and veterinary sciences, though not globally known.

Methodology

The MAP Multi-AI Peer Review system uses:

10 Core Criteria:

Factual Accuracy
Thesis & Clarity
Logical Coherence
Evidence & Support
Source Credibility
Citation Accuracy
Depth of Analysis
Originality & Insight
Relevance to Prompt/Thesis Question
Interpretive Fairness

Weighting: 85% content score + 15% writing mechanics score.
AI Accuracy Seal Requirement:

100% Factual Accuracy
≥90% AI Accuracy Across 6 Criteria

Multi-Model Review: Each paper is evaluated by multiple AI models to increase objectivity and reduce single-model bias (Creswell & Creswell, 2018; Shadish, Cook, & Campbell, 2002).

Seal logic: Granted when factual accuracy = 100% and AI Accuracy Across 6 Criteria ≥ 90%.

Statistical Summary

Mean Score 88.89%

Median Score 89.95%

Standard Deviation (Sample) 2.86%

Minimum Score 84.75%

Maximum Score 90.9%

Score Range 6.15%

AI Accuracy Seal Success Rate 75% (3/4 papers)

Key Statistical Insights:

Low SD (2.86%) indicates strong inter-model scoring consistency.
Score range (6.15%) demonstrates generally uniform scoring across four diverse disciplines.
75% Seal success rate confirms that most works met rigorous academic standards.

Analysis & Discussion

Strengths of MAP Peer Review:

Produced consistent, objective scores across STEM, social sciences, and humanities.
Identified key strengths and weaknesses in methodology, evidence quality, and citations.
Achieved 100% factual accuracy in all cases, meeting the primary Seal requirement.

Weaknesses Identified:

Citation verification remains challenging in theoretical/humanities research, requiring human oversight.
Writing mechanics varied widely, lowering overall scores for otherwise strong content (Case 3: 84.75%).
Localized focus in some papers reduced broad global applicability.

Cross-Disciplinary Effectiveness:

MAP Peer Review Findings

MAP Peer Review functions reliably across multiple research disciplines.
MAP Peer Review provides data-driven and actionable evaluations.
MAP Peer Review supports pre-publication peer review as a complementary AI-assisted method, with human verification needed for archival or complex theoretical sources (Shadish et al., 2002; Gall, Gall, & Borg, 2014).

Validity of Small Sample Size

Although the study used only four papers, the low score variance and standardized rubric provide high internal validity. According to Creswell and Creswell (2018), small-sample pilot studies can reliably measure method consistency under controlled conditions.

Internal Validity: Supported by uniform scoring methods and 100% factual accuracy.
External Validity: Limited due to small sample; additional studies with larger and more diverse sets are recommended.
Pilot Role: As Shadish et al. (2002) note, pilot studies are key to confirming method functionality before broad implementation.

Conclusion

The MAP (Multi-AI) Peer Review Method:

Demonstrated cross-disciplinary consistency and high internal validity.
Granted the AI Accuracy Seal to 3/4 papers, with one denial due to citation verification thresholds.
Identified actionable feedback on writing, evidence, and methodology.

Final Verdict

MAP Peer Review is a practical, standardized, and objective multi-disciplinary academic evaluation tool.
It supports pre-publication quality assurance and can enhance research credibility when paired with targeted human oversight for theoretical and citation-heavy works.

References (APA 7th Edition)

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Gall, M. D., Gall, J. P., & Borg, W. R. (2014). Applying educational research: How to read, do, and use research to solve problems of practice (7th ed.). Pearson.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

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