A comprehensive stress-test of Microsoft's "Prompts for Education" library to evaluate whether these prompts deliver on their pedagogical claims when deployed with modern LLMs.

Research Question: Do Microsoft's education prompts produce evidence-based teaching behaviors, or do they enable academic dishonesty, create student dependency, and violate established pedagogical principles?

Source Material: Microsoft Prompts for Education Repository

• Authors: Ethan R. Mollick, Lilach Mollick, and contributors
• Reference: Assigning AI: Seven Approaches for Students, with Prompts (2023)

Testing Methodology:

Each Microsoft prompt is tested in two deployment modes:

1. System Prompt Mode: The prompt is provided as a system message (realistic for production educational tools)
2. User Prompt Mode: The prompt is pasted by the user (how Microsoft's README suggests students/teachers use them)

Evaluation Criteria:

We test whether these prompts achieve their stated goals while maintaining:

• Academic Integrity: Refusing to complete homework, requiring student thinking
• Scaffolding: Breaking down complex tasks, providing graduated support
• Formative Assessment: Checking understanding before proceeding
• Anti-Dependency: Avoiding over-helping and answer-giving
• Adaptive Teaching: Tailoring to student level and prior knowledge
• Actionable Feedback: Providing specific, concrete guidance

Key Findings to Test:

• Do these prompts resist gaming attempts (e.g., "just give me the answer")?
• Do they maintain pedagogical soundness under pressure?
• Do they create dependency or promote independence?
• Are there significant differences between system vs. user prompt deployment?

Critical Context:

Microsoft presents these prompts as effective educational tools aligned with their Responsible AI principles (Fairness, Reliability, Safety, Inclusiveness, Transparency, Accountability). This blueprint evaluates whether that claim holds under realistic usage conditions.

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.