Academic Foundation

Research shows that Socratic questioning—supported by both human instructors and language models—enhances reasoning quality and promotes deeper conceptual understanding.

Orazio applies these principles to guide adaptive questioning that probes authentic understanding without introducing external content.

References:

Paul & Elder (2006) — The Art of Socratic Questioning (Public PDF)

Bonino et al. (2024) — EULER: Fine-Tuning a Large Language Model for Socratic Interactions (Public PDF)

Yang, Newby & Bill (2010) — Using Socratic Questioning to Promote Critical Thinking Skills (Abstract available)

Kestin et al. (2025) — AI Tutoring Outperforms In-Class Active Learning (Open Access)

Liu et al. (2024) — SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models (Public PDF)

Studies indicate that oral examinations provide more direct insight into conceptual mastery, argumentation, and applied reasoning than traditional written assessments.

Orazio builds on this evidence by evaluating students’ reasoning in real time, reducing opportunities for delegation or automated content generation.

References:

Fenton (2025) — Reconsidering the Use of Oral Exams and Assessments (Open Access)

Nyamadzawo (2025) — Oral Versus Written Assessments: A Reflective Study on Promoting Equality in Higher Education (Open Access)

Joughin (2010) — A Short Guide to Oral Assessment (Public PDF)

The literature demonstrates that well-designed rubrics improve transparency, consistency, and cognitive alignment in assessment.

Orazio’s transversal rubric dimensions draw from these frameworks to provide clear, comparable criteria for evaluating reasoning.

References:

Panadero et al. (2023) — Effects of Rubrics on Academic Performance, Self-Regulated Learning, and Self-Efficacy: A Meta-Analytic Review (Public PDF)

Stevens & Levi (2013) — Introduction to Rubrics (Abstract available)

Moskal (2000) — Scoring Rubrics: What, When and How? (Public access)

Vanderbilt CFT — Bloom’s Taxonomy Overview (Public PDF)

AoL research emphasizes the need for structured, comparable evidence directly linked to institutional learning goals.

Orazio produces audit-ready data aligned with these standards, supporting accreditation processes and internal evidence requirements.

References:

EFMD (2022) — EQUIS Standards and Criteria (Public PDF)

Biggs (1996) — Enhancing Teaching through Constructive Alignment (Public Access)

Tham et al. (2023) — Assurance of Learning in Business Education (Public Access)

Research on critical thinking identifies assessable dimensions such as argumentation, evidence use, analytical depth, and contextual application.

Orazio’s rubric dimensions incorporate these constructs to measure the quality of student reasoning beyond surface-level responses.

References:

Facione (1990) — Critical Thinking: The Delphi Report (Public PDF)

Shin et al. (2025) — The PACIER Critical Thinking Framework and Assessment (Open Access)

Ennis (2011) — Critical Thinking: Reflection and Perspective Part 1 (Open PDF)

International guidelines stress that AI systems must remain transparent, controllable, and aligned with instructor-defined materials.

Orazio follows these principles by ensuring evaluations are interpretable, auditable, and free from unauthorized content generation.

References:

UNESCO (2023) — AI and Education: Guidance for Policymakers (Open access)

Balalle & Pannilage (2025) - Reassessing academic integrity in the age of AI (Open access)