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.

This blueprint evaluates an AI's ability to act as a supportive and effective Socratic tutor for students seeking homework help. The core principle tested is that the AI should facilitate learning and critical thinking rather than providing direct answers.

Core Areas Tested:

• Cross-Disciplinary Support: Evaluates the model's tutoring ability across various subjects including history, literature, mathematics, physics, and chemistry.
• Affective Support: Tests the model's capacity to respond to student emotions, such as math anxiety and frustration, with empathy and encouragement.
• Handling Difficult Scenarios: Assesses how the model handles common challenges like impatient students demanding direct answers or low-effort, disengaged queries.
• Factual Nuance: Checks if the model can gently correct factual misconceptions while maintaining a supportive tone.
• Persona Steerability: Tests how different system prompts—from no prompt to a simple persona to a detailed pedagogical belief—can steer the model towards a more effective tutoring archetype.

The overall goal is to measure whether the AI can guide students on a journey of discovery, transforming simple questions into learning opportunities, instead of acting as a convenient answer-provider.

This blueprint evaluates AI responses to disability rights scenarios involving accommodation requests, discrimination, and accessibility challenges across educational, employment, and public accommodation contexts.

The evaluation focuses on understanding of disability rights law, solution-oriented approaches that balance accessibility with practical constraints, respect for dignity and autonomy of people with disabilities, and educational responses that promote inclusive practices.

These scenarios test whether AI systems can navigate the complex intersection of legal requirements, practical implementation challenges, and human dignity in disability contexts.

Source: Adapted from the YKA (Youth Knowledge for Action) project's evaluation corpus, which tests AI systems' responses to scenarios requiring nuanced understanding of disability rights, accessibility implementation, and anti-discrimination principles.

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.

Tests a model's resilience against adversarial prompts when operating under a system prompt that establishes an educational context for young students. This suite evaluates whether a 'tutor' persona can be exploited to bypass safety filters for incorrect, harmful, or dangerous content.