Students will: apply understanding of AI capabilities and limitations; practice design thinking; consider ethical implications; develop presentation skills; collaborate effectively in teams.
Task: Identify a healthcare problem that could benefit from AI
Guiding Questions:
Requirements: Problem must be specific and clearly defined; should affect an identifiable group; students must research prior solutions; include data on scope and impact.
Deliverable: One-page problem statement including description, who is affected, current approaches and limitations, and why AI might help.
Task: Design a conceptual AI solution with attention to feasibility and ethics
Requirements cover five areas:
Presentation Requirements: 5–7 minutes per group; visual aids; all team members participate; clear explanation of problem, solution, benefits, and ethics; respond to Q&A.
Structure: Hook (30 sec) → Problem with data (1 min) → AI solution (2 min) → Benefits (1 min) → Ethics and safeguards (1.5 min) → Conclusion (30 sec) → Q&A (3–5 min)
Team Members:
Problem Title:
1. Problem Description (What healthcare challenge are you addressing? 3–5 sentences)
2. Who Is Affected?
How many people are affected? (Provide data/estimates)
3. Current Approaches – How is this currently being addressed?
What are the limitations?
4. Why AI? (What AI capabilities are relevant?)
Has anyone tried AI for this before?
5. Significance – Why does this problem matter?
6. Research Sources (List 3–5 credible sources)
Team Members:
Project Title:
1. What type of AI will your solution use?
Machine Learning Computer Vision NLP Predictive Analytics Robotics Other:
2. Describe how your AI system works:
3. What data does your AI system need?
Data Type 1: Source:
Data Type 2: Source:
Data Type 3: Source:
4. How would you train this AI system?
5. What does the AI output or recommend?
6. Who would use this system? Doctors Nurses Patients Researchers Administrators Other:
7. Describe a typical use scenario:
Step 1:
Step 2:
Step 3:
Step 4:
Step 5:
10. Primary benefit to patients:
11. Additional benefits (2–3):
14. Privacy Concerns:
15. Bias Risks:
16. Accountability:
17. Equity and Access:
19. Testing Plan:
20. Human Oversight: Can the AI be overridden by humans? Yes No
23. Biggest challenge in designing this solution:
24. Aspect you’re most proud of:
Your Name:
Team Evaluated:
Project Title:
1. Problem Clarity (1–5): 1 2 3 4 5
Comments:
2. Solution Feasibility (1–5): 1 2 3 4 5
Comments:
3. Ethical Considerations (1–5): 1 2 3 4 5
Comments:
4. Potential Impact (1–5): 1 2 3 4 5
Comments:
5. Presentation Quality (1–5): 1 2 3 4 5
Comments:
6. What was the strongest aspect of this project?
7. What improvement should the team address?
8. What question do you still have about their solution?
9. Would you trust this AI system if it existed? Why or why not?
Students will: practice professional communication and interview skills; gain authentic insights; synthesize information from primary sources; create multimedia content; connect classroom learning to real-world practice.
Phase 1 – Finding an Interview Subject: Potential subjects include doctors, nurses, medical technicians, medical school faculty, healthcare IT professionals, medical researchers, bioinformatics specialists, or public health officials. Use personal connections, school nurse contacts, local hospitals, universities, professional associations, or LinkedIn.
Phase 2 – Preparing for the Interview: Research the subject’s role and organization. Prepare 10–15 open-ended questions across five categories: Background and role (2–3), Current AI tools (3–4), Benefits and challenges (3–4), Ethical considerations (2–3), Future perspectives (2–3).
Phase 3 – Conducting the Interview: Arrive on time, dress appropriately, express gratitude, take detailed notes, ask follow-up questions, be respectful of time, ask permission before recording, send thank-you email within 24 hours.
Phase 4 – Creating Deliverable: Choose one format: Written Report (3–4 pages), Video Documentary (5–7 min), Podcast-Style Audio (8–10 min), or Multimedia Presentation (10–12 slides). All must include attribution, connection to 3+ lesson concepts, analysis (not just summary), and personal reflection.
Known Cases to Consider:
Project Phases:
Recommended Sources: STAT News, Healthcare IT News, medical journals (JAMA, NEJM, Lancet), MIT Technology Review, Science Daily, Nature News
Analysis Questions: What AI technology is involved? What problem does it address? Who developed it? What are benefits? What concerns exist? How does it connect to what we learned?
Presentation Structure: Headline/hook (10 sec) → Explain development (45 sec) → Why it matters (30 sec) → Class connections (30 sec) → Discussion question (15 sec)
Assessment: Pass/Fail completion credit. Excellence indicators include particularly timely selection, exceptional analysis, engaging discussion, and professional delivery.
Suitable for classes covering genetics, molecular biology, or human biology.
Students explore how AI analyzes genetic data to predict disease risk or guide treatment. Activities include researching ML identification of genetic variants, understanding polygenic risk scores, analyzing case studies of AI-enhanced genetic counseling, considering ethical issues in genetic prediction, and connecting to genetics concepts.
Suitable for statistics, probability, or data analysis units.
Students calculate and interpret measures of diagnostic accuracy. Activities include defining sensitivity/specificity/PPV/NPV, working with confusion matrices from published AI studies, calculating accuracy metrics, and exploring how prevalence affects predictive values (Bayesian reasoning).
Suitable for units on argumentation, research writing, or contemporary issues.
Students read and analyze articles about healthcare AI, then write argumentative essays. Possible prompts: Should AI diagnostics require FDA approval? Is it ethical for insurers to use AI predictions? Will healthcare AI help or hurt health equity? Should patients have the right to refuse AI involvement?
| Category (Points) | Exemplary (A) | Proficient (B) | Developing (C) | Beginning (D/F) |
|---|---|---|---|---|
| Problem Identification (20) | Clear, specific, well-researched with data | Adequately defined with some research | Basic statement with limited research | Vague or inappropriate; minimal research |
| AI Solution Design (25) | Sophisticated understanding; detailed, technically sound | Solid understanding; appropriate, feasible | Basic approach; some feasibility concerns | Weak or inappropriate; not technically sound |
| Ethical Analysis (20) | Comprehensive; thoughtful safeguards; deep understanding | Identifies major concerns; adequate safeguards | Basic consideration; limited safeguards | Minimal analysis; inadequate attention |
| Expected Impact (15) | Compelling case; quantified benefits; realistic assessment | Clear benefits; adequate justification | Basic benefits; limited justification | Unclear or unrealistic claims |
| Presentation (15) | Highly professional; engaging; excellent visuals and coordination | Professional; clear; good structure | Adequate; some organizational issues | Poor; unclear; disorganized |
| Collaboration (5) | Equal contribution; excellent teamwork | Generally equal; adequate teamwork | Uneven contribution or process issues | Poor teamwork; significant disparities |
| Category (Points) | Exemplary (A) | Proficient (B) | Developing (C) | Beginning (D/F) |
|---|---|---|---|---|
| Preparation (15) | Excellent questions; thorough research; professional | Good questions; adequate research | Basic questions; limited research | Poor questions; inadequate preparation |
| Interview Quality (20) | Insightful questions; active listening; rich responses | Good questions; adequate follow-up | Basic questions; limited follow-up | Weak questions; poor technique |
| Content Synthesis (25) | Sophisticated synthesis; well-organized thematically | Solid synthesis; good organization | Basic synthesis; adequate organization | Poor synthesis; disorganized |
| Connection to Concepts (20) | Insightful connections to multiple concepts | Clear connections to 3+ concepts | Basic connections to 2–3 concepts | Weak or absent connections |
| Deliverable Quality (15) | Exceptional; highly professional; engaging | Professional; meets all requirements | Adequate; meets basic requirements | Poor quality; missing elements |
| Reflection (5) | Thoughtful, insightful reflection | Adequate reflection | Basic with limited depth | Minimal or absent |
| Category (Points) | Exemplary (A) | Proficient (B) | Developing (C) | Beginning (D/F) |
|---|---|---|---|---|
| Research Quality (20) | Multiple high-quality sources; thorough; accurate | Adequate sources; solid research | Limited sources; basic research | Poor sources; inadequate research |
| Case Understanding (15) | Comprehensive; nuanced description | Good understanding; clear description | Basic understanding; adequate description | Weak; confused or inaccurate |
| Root Cause Analysis (25) | Sophisticated; multiple causal factors; systemic understanding | Solid analysis; identifies major causes | Basic; identifies some causes | Superficial; misses key factors |
| Impact Assessment (15) | Comprehensive; multiple levels; both quantitative and qualitative | Good analysis; adequate data | Basic analysis; limited data | Weak assessment; minimal evidence |
| Solutions Evaluation (15) | Critical evaluation; considers adequacy; proposes improvements | Adequate evaluation | Basic; limited critical analysis | Weak or absent evaluation |
| Presentation (10) | Highly effective; professional; engaging | Professional; clear; well-organized | Adequate; meets requirements | Poor; unclear or disorganized |