A comprehensive evaluation suite testing AI tutoring and teaching capabilities against evidence-based pedagogical practices from global education research. This blueprint operationalizes decades of teaching effectiveness research into specific, testable criteria for AI systems.

Core Research Foundation:

• Explicit Instruction: Based on Rosenshine's (2012) Principles of Instruction, requiring step-by-step teaching, worked examples, and guided practice before independence
• Formative Assessment: Implements Wiliam & Thompson's (2008) framework for checking understanding through targeted questioning and immediate feedback loops
• Cognitive Load Management: Applies Sweller's (2011) Cognitive Load Theory to prevent information overload through chunking and scaffolding
• Socratic Dialogue: Follows Alexander's (2018) dialogic teaching principles from the EEF randomized trial, emphasizing structured questioning over guess-what-I'm-thinking
• Retrieval Practice: Incorporates Dunlosky et al.'s (2013) high-utility learning techniques, particularly spaced repetition and testing effects
• Adaptive Teaching: Implements Teaching at the Right Level (TaRL) methodology from Banerjee et al.'s (2007) India RCTs, requiring diagnostic assessment and differentiated instruction
• Quality Feedback: Applies Hattie & Timperley's (2007) feedback framework, distinguishing actionable guidance from vague praise
• Academic Integrity: Follows Kirschner, Sweller & Clark's (2006) guided instruction principles, refusing to provide answers while maintaining learning engagement

Key Distinctions Tested:

• Effective AI Tutoring: Structured, scaffolded, formative, diagnostic, with productive struggle and spaced practice
• Ineffective AI Responses: Answer-giving, overwhelming, dependency-creating, coverage-focused, with minimal guidance for novices

Global Evidence Base: Synthesizes research from multiple educational contexts including Harvard AI tutoring RCTs, EEF Teaching & Learning Toolkit meta-analyses, World Bank TEACH classroom observation framework, Japanese Lesson Study collaborative inquiry, and cross-cultural validation from OECD Global Teaching InSights video studies.

Practical Application: Each probe tests specific teaching behaviors that correlate with student learning gains across diverse contexts, ensuring AI systems demonstrate pedagogical competence rather than mere content knowledge.

A configuration to assess LLM understanding of the Constitution of India, covering its Preamble, fundamental rights, directive principles, governmental structure, judicial system, local governance and more, based on the text as it stood on 9 December 2020.