Microcredential Rubric

Competency Domain

Beginning (1)

Competent (2)

Accomplished (3)

1. Understand What AI Is and Isn’t

Defines artificial intelligence (AI) simply (“technology mimicking human thinking”) and lists examples of generative AI (e.g., ChatGPT, Gemini, Copilot). Identifies basic AI vocabulary (e.g., machine learning, supervised learning).

Clearly explains differences between types of AI: machine learning, predictive AI, generative AI. Recognizes how AI learns from data. Uses terminology such as “hallucination” and “prompt engineering” accurately.

Effectively uses advanced AI vocabulary (LLM, embeddings, transformer architecture) in relevant contexts. Critically evaluates AI capabilities and limitations through examples from academic or personal experiences, referencing specific AI tools (Digital Promise AI Literacy Framework).

2. Recognize AI in the Real World

Identifies common platforms utilizing AI (Netflix recommendations, Spotify playlists, Google Maps navigation, Grammarly suggestions).

Describes how AI functions in at least three key sectors (e.g., resume filtering, healthcare diagnostics, personalized advertisements). Provides thoughtful reflection on how AI affects everyday interactions and decisions.

Conducts a detailed analysis of an AI application in a specific sector (healthcare, finance, education), explaining its function, impact, and significance clearly and convincingly.

3. Use AI Effectively

Uses AI tools for basic tasks (e.g., generating summaries or ideas) but may rely heavily on default prompts or outputs. Struggles to identify AI errors or limitations.

Chooses appropriate AI tools for the task. Demonstrates basic prompt engineering skills (clarification, specificity). Evaluates outputs for relevance and basic accuracy. Revises prompts to improve results.

Strategically selects and uses AI tools to support varied academic and career goals. Crafts layered, contextual prompts and anticipates potential AI limitations (e.g., hallucinations, bias). Evaluates outputs using multiple criteria (accuracy, tone, credibility) and applies corrections. Demonstrates tool fluency across contexts (Student Guide to AI, 2025; UMD Course; Digital Promise).

4. Use AI Responsibly

Recognizes basic ethical risks associated with AI, including privacy concerns, misinformation, and data bias. Understands AI outcomes can reflect biases in training data.

Explains clearly how data bias impacts AI outputs, using real-world examples such as gender bias in resume screening (Digital Promise Framework). Understands ethical guidelines for AI usage as articulated by UNESCO.

Applies ethical reasoning effectively to analyze a complex real-world scenario (e.g., AI healthcare triage, predictive policing). Clearly identifies improper AI use (bias reinforcement, data misuse, plagiarism, etc.) and articulates responsible, ethical alternatives. Briefly addresses AI’s environmental impacts across its lifecycle (UNEP Issues Note).

5. Prepare for the Future with AI

Recognizes employer expectations regarding basic AI literacy (NACE) and identifies ways AI is shaping the future workplace. Understands AI may create new roles and alter existing ones significantly.

Clearly connects AI applications to at least one personal career interest area, describing specific tools (Copilot, Gemini) relevant to typical tasks (writing, analysis, planning). Demonstrates reflective understanding of how AI literacy enhances career preparedness.

Engages effectively in career-based AI simulations, clearly outlining best practices and pitfalls in AI use for tasks such as email drafting, project planning, and communication. Proactively identifies methods for maintaining current AI knowledge to remain competitive and ethically informed in the evolving job market.