AI in Practice: Case Studies

Assignment created by David Hicks (School of Education), using prompts with ChatGPT

Other Assignments In This Series

This assignment is the second week of a five-week series on generative AI.

Rationale

This assignment provides students with concrete examples of how AI is used in different disciplines, helping them to understand its practical applications and limitations.

Objectives

  • Analyze real-world case studies of AI applications.
  • Evaluate the effectiveness and ethical implications of AI solutions.
  • Identify appropriate AI tools for specific problems.

Activities

  1. Case Study Analysis:
    1. Read available case studies:
    2. Form groups and select a case study relevant to your discipline.
    3. Research the case study, focusing on the AI application, benefits, limitations, and ethical considerations.
      Example Case Study Analysis:

      “Our case study examines AI in healthcare, specifically AI-driven diagnostic tools. Benefits include increased diagnostic accuracy and efficiency. Limitations involve potential biases in training data and the need for extensive data privacy measures. Ethically, it’s vital to ensure AI decisions are transparent and patient data is securely handled.”

    4. Create a presentation summarizing your findings.
    5. Assessment: Each group will present their analysis to the class.
  2. Group Discussion and Peer Feedback:
    1. Present your case study analysis to the class.
    2. Provide and receive feedback from peers, focusing on the clarity, depth, and ethical considerations of the presentations.
    3. Assessment: Participation and the quality of feedback will be assessed.
      Example Feedback:

      “Your presentation on AI in finance was thorough. I appreciated the detailed analysis of risk assessment tools. One suggestion is to delve deeper into the ethical implications of AI-driven financial decisions, particularly regarding potential biases.”

  3. Individual Report:
    1. Choose a case study of interest.
    2. Conduct in-depth research, examining the AI application, its impact, and ethical concerns.
    3. Write a detailed report, including personal reflections.
      Example Report Excerpt:

      “AI in autonomous vehicles presents significant advancements in transportation. Enhanced safety and efficiency are key benefits. However, ethical issues like decision-making in critical situations and data privacy must be addressed. Ensuring robust ethical guidelines and transparency in AI algorithms is essential.”

    4. Assessment: Reports will be evaluated based on depth of analysis, critical thinking, and ethical considerations.

Example Individual Report

AI in Healthcare: Enhancing Diagnostic Accuracy and Efficiency

Introduction

Artificial Intelligence (AI) has the potential to revolutionize healthcare by improving diagnostic accuracy and efficiency. This report examines the application of AI in healthcare, focusing on IBM Watson’s Oncology Diagnosis system, which exemplifies the integration of AI in medical diagnostics.

AI in Healthcare

IBM Watson’s Oncology Diagnosis leverages natural language processing and machine learning to analyze vast amounts of medical literature and patient data, providing oncologists with evidence-based treatment recommendations. This AI system enhances the diagnostic process by quickly processing and correlating information that would be time-consuming for humans to analyze manually.

Benefits

The primary benefit of AI in healthcare is its ability to improve diagnostic accuracy. By analyzing diverse data sources, AI can identify patterns and correlations that might be overlooked by human practitioners. This leads to more precise diagnoses and personalized treatment plans, ultimately improving patient outcomes. Additionally, AI systems can process information rapidly, reducing the time required to diagnose and treat patients.

Limitations

Despite its advantages, AI in healthcare faces several limitations. One significant challenge is the quality and variability of input data. Incomplete or biased data can lead to incorrect or suboptimal recommendations. Moreover, AI systems require extensive training on high-quality datasets, which may not always be available. Another limitation is the need for interpretability; medical professionals must understand and trust AI recommendations, which requires transparent and explainable AI models.

Ethical Considerations

Ethical concerns are paramount when implementing AI in healthcare. Data privacy is a critical issue, as AI systems require access to sensitive patient information. Ensuring robust data protection measures is essential to maintain patient trust and confidentiality. Additionally, addressing biases in AI models is crucial to prevent discriminatory outcomes. This involves continuously monitoring and updating AI systems to ensure fairness and accuracy.

Conclusion

AI has the potential to significantly enhance healthcare by improving diagnostic accuracy and efficiency. IBM Watson’s Oncology Diagnosis demonstrates how AI can support medical professionals in making evidence-based decisions. However, addressing data quality, interpretability, and ethical concerns is vital to fully realize the benefits of AI in healthcare. By doing so, we can ensure that AI systems contribute positively to patient care and outcomes.

Assessment Rubric

Criteria Excellent
(A)		 Good
(B)		 Acceptable
(C)		 Needs Improvement
(D–F)
Depth of Exploration
Demonstrates a thoughtful, nuanced exploration of the AI tool.		 Insightful, original exploration that clearly explains tool function and offers deep reflection. Clear explanation of the tool with thoughtful reflection; mostly original analysis. Basic explanation and surface-level reflection; some generalizations or gaps. Limited or unclear explanation; minimal reflection or insight.
Specificity of Details
Supports claims with concrete details and examples.		 Detailed and specific examples that clearly support insights and claims. Several clear examples, though some may lack full integration or depth. Some relevant examples, but may be vague or underdeveloped. Few or no specific examples; unclear or unsupported claims.
Critical Thinking
Evaluates the tool’s uses, benefits, and drawbacks.		 Offers insightful, balanced analysis with strong awareness of context and implications. Addresses both strengths and weaknesses with some depth. Touches on strengths or weaknesses; may be imbalanced or superficial. Little to no evaluation; lacks critical thought or awareness.
Organization & Coherence
Presents ideas clearly with logical structure.		 Ideas are well-organized, transitions are smooth, and the overall structure is effective. Generally clear structure; transitions are adequate. Some disorganization or weak transitions; may affect clarity. Disorganized or hard to follow; lacks clear structure.
Mechanics & Style
Uses appropriate tone, grammar, and mechanics.		 Polished, professional writing free from errors. Minor errors that do not affect readability. Noticeable errors that occasionally distract the reader. Frequent or serious errors that interfere with meaning.

 

Last Updated on July 7, 2025