🌐 Universal Competencies for AI
1. Introduction to Generative AI
Why It Matters
- Describe its growing relevance in education, research, and industry, and the potential impacts on jobs and the economy
- Explain how it can enhance creativity, productivity, and problem-solving skills while weighing timely issues.
- Core Concepts and Terminologies
- AI and Machine Learning Basics
- Define generative AI as a branch of artificial intelligence that creates new content based on patterns in data.
- Describe how AI and search engines differ.
- Provide examples of generative AI systems used for different purposes (e.g., ChatGPT for text generation, DALL·E for image generation, etc.).
- Key Terms
- Define key terms relevant to AI and machine learning (see glossary tab)
2. Ethical Considerations
- Institutional Standards
- Apply ACC’s Academic Integrity policy to AI usage, practicing responsible deployment to augment human thinking, not replace it.
- Environmental Impact
- Describe and recognize the environmental impact of AI usage and ways to mitigate the impact (e.g., energy consumption/carbon footprint, water usage, and electronic waste).
- Data Privacy
- Describe what data AI tools collect.
- Explain the hazards when entering personal or sensitive information into AI systems.
- Copyright and Intellectual Property
- Explain and address issues related to AI-generated content and copyright laws.
- Inclusivity and Bias
- Describe and discuss how AI algorithms can perpetuate biases and how to identify them.
3. Effective Prompt Writing
- Structure prompts to get the best results using frameworks (examples) and effective prompt-writing tools.
- Example: Instead of asking, "What is AI?" you can ask, "Explain AI in simple terms with examples relevant to education."
- Identify effective prompt writing tools.
4. Critical Evaluation of AI Outputs
- Assess the credibility and relevance of AI-generated content by verifying facts, checking citations, and otherwise evaluating the material for credibility and bias.
🎓 Competencies for Students
1. Understanding AI Capabilities and Limitations
- Explain the strengths of generative AI (e.g., brainstorming ideas, developing study materials, etc.)
- Explain the weaknesses of generative AI (e.g., using methods not in the curriculum to solve problems, undermining student learning, etc.)
2. Academic Integrity
- Explain the guidelines for disclosing AI use in assignments or projects.
- Apply critical evaluation skills of AI-generated content to avoid committing plagiarism.
3. Practical Applications
- Be able to employ generative AI for:
- Writing assistance (essays, reports, resumés).
- Brainstorming ideas.
- Learning complex concepts through simplified explanations.
- Creating study materials (flashcards, quizzes, study guides, etc)
- Aiding in research (Perplexity, You.com, Consensus, Explicit, etc.)
- Describe appropriate usage of AI in the discipline.
👩🏫 Competencies for Faculty
1. Understanding Generative AI’s Role in Education
- Explain the strengths of generative AI in supporting teaching (e.g., creating course material, developing assessments, answering student queries, etc.).
- Explain the weaknesses of AI in course instruction (e.g., grading assessments, detecting cheating, providing feedback, etc.)
- Develop AI literacy among students.
- Identify and explain appropriate usage of AI in the discipline.
2. Designing AI-Inclusive Assignments
- Create assignments that encourage critical thinking in the use of generative AI (e.g., compare AI-generated content with human-written content., analyze the ethical implications of AI in specific scenarios, etc.)
3. Evaluating Student Work
- Evaluate and employ detection tools (e.g., Turnitin) to identify AI-generated content, recognizing their value and potential errors
- Provide and explain clear policies on how AI can be used in coursework.
