AI Ethics Case Study

AI-generated rating with detailed analysis provided.

AI-generated rating with detailed analysis provided.

1. Alignment with Outcomes Rating: 2 - Strengths: Clear mapping to ethical understanding and framework development. - Weaknesses: Vague on transferable skills beyond the specific topic. - Improvement: Explicitly link to broader professional competencies. ## 2. Cognitive Demand Rating: 2 - Strengths: Requires analysis and creation. - Weaknesses: AI can replicate this output easily. - Improvement: Add personal reflection or specific constraints. ## 3. Authenticity/Context Rating: 2 - Strengths: Real-world cases. - Weaknesses: Abstract framework development lacks professional constraints. - Improvement: Simulate a specific organizational role. ## 4. Accessibility/Equity Rating: 2 - Strengths: Multiple case options. - Weaknesses: Heavy workload, no AI access equity addressed. - Improvement: Offer alternative formats, clarify AI policy. ## 5. Process Visibility Rating: 1 - Strengths: None. - Weaknesses: No drafts or logs required. - Improvement: Require annotated drafts or process journals. ## 6. AI Transparency & Ethical Literacy Rating: 1 - Strengths: Topic is relevant. - Weaknesses: No mandate on AI usage or citation. - Improvement: Require AI usage statement and critical evaluation of AI tools. ## 7. Teaching Methods Rating: 1 - Strengths: Presentation adds engagement. - Weaknesses: No active learning methods described. - Improvement: Include peer review or in-class workshops.

1. Alignment with Outcomes Rating: 2 - Strengths: The assignment clearly identifies learning goals related to ethical reasoning and framework development. - Weaknesses: The connection to transferable skills beyond the specific topic is not explicit. - Improvement: Explicitly state how the framework developed applies to future professional scenarios. ## 2. Cognitive Demand Rating: 2 - Strengths: Requires analysis, synthesis, and creation of a decision-making framework. - Weaknesses: The 1500-2000 word essay is highly vulnerable to AI generation without specific constraints. - Improvement: Require specific personal reflections or in-class drafting to ensure student voice. ## 3. Authenticity/Context Rating: 2 - Strengths: Uses real-world case studies with identifiable stakeholders. - Weaknesses: The context is academic rather than professional; the "framework" audience is undefined. - Improvement: Specify a target audience (e.g., a policy board or corporate ethics committee). ## 4. Accessibility/Equity Rating: 2 - Strengths: Offers multiple case study options allowing student choice. - Weaknesses: Does not address equity regarding AI tool access or alternative submission formats beyond presentation. - Improvement: Clarify policies for students without AI access and ensure presentation rubrics are accessible. ## 5. Process Visibility Rating: 1 - Strengths: None explicitly stated in the prompt. - Weaknesses: No requirement for drafts, brainstorming logs, or reflection on the thinking process. - Improvement: Mandate submission of initial outlines or AI usage logs to make thinking visible. ## 6. AI Transparency & Ethical Literacy Rating: 1 - Strengths: Topic focuses on AI ethics. - Weaknesses: Does not mandate disclosure of AI use during the assignment or critical evaluation of AI tools. - Improvement: Require a statement on AI usage and a critique of AI-generated ethical suggestions. ## 7. Teaching Methods Rating: 1 - Strengths: Presentation component allows for interaction. - Weaknesses: No description of instructional support or scaffolding provided. - Improvement: Include peer review sessions or workshops on ethical frameworks prior to submission.

Alignment with Outcomes The assignment partially aligns with learning outcomes. While it asks students to analyze and develop a framework, the connection to broader, transferable skills isn't explicitly stated. It's unclear if the goal is simply to know ethical frameworks or to apply ethical reasoning in various contexts. * Improvement: Explicitly state how the assignment builds skills like critical thinking, problem-solving, and communication, and how these skills are transferable to other disciplines or professional settings. ## Cognitive Demand The assignment requires some higher-order thinking, specifically analysis and creation (developing a framework). However, the assessment criteria heavily weight 'understanding of ethical frameworks' (30%), which could be achieved through memorization rather than deep analysis. The 'analysis depth' component (35%) is good, but needs to be more focused on justification of decisions. * Improvement: Increase the weight of the 'analysis depth' and 'framework development' components. Require students to explicitly justify their choices within the framework, explaining why certain ethical principles are prioritized over others in the chosen case study. ## Authenticity/Context This is the assignment's strongest point. The use of real-world case studies immediately grounds the assignment in authentic contexts. The topics chosen (facial recognition, hiring algorithms, autonomous vehicles, healthcare) are all current and relevant, requiring students to grapple with genuine ethical dilemmas. * Strength: The assignment effectively uses authentic constraints (the complexities of the case studies) and implicitly involves stakeholders (those affected by the AI systems). ## Accessibility/Equity The assignment is reasonably accessible. Providing a choice of case studies allows some student agency. However, it doesn't explicitly address potential barriers to access, such as differing levels of prior knowledge about AI or access to resources for research. The presentation component could be challenging for students with presentation anxiety. * Improvement: Offer alternative formats for demonstrating understanding (e.g., a detailed policy brief instead of a presentation). Provide resources or scaffolding to help students with varying levels of AI knowledge. ## Process Visibility This is a significant weakness. The assignment is a 'deliverable-based' assessment, with only the final written analysis and presentation submitted. There's no requirement for drafts, revisions, or documentation of the student's thinking process. This makes it easy for AI to generate a complete response without demonstrating genuine learning. * Improvement: Implement a multi-stage submission process. Require students to submit an initial outline, a literature review, a draft of the analysis, and a reflection on their research process. This makes the process visible and allows for formative feedback. ## AI Transparency & Ethical Literacy The assignment completely lacks explicit attention to AI use. It doesn't address the possibility of students using AI tools to generate content, nor does it require them to critically evaluate AI-generated information. This is a major flaw in the context of AI-proof assessment. * Improvement: Explicitly address AI use in the assignment instructions. Require students to document any AI tools they used, critically evaluate the AI-generated content, and clearly distinguish their own contributions from those of AI. Consider asking students to identify potential biases in AI-generated responses. ## Teaching Methods The assignment description doesn't mention any specific teaching methods. It assumes students will simply be given the case studies and asked to complete the analysis. This is insufficient in the age of AI. Active learning strategies are needed to help students develop the skills to critically evaluate AI and apply ethical reasoning. * Improvement: Incorporate interactive activities, such as debates, role-playing exercises, or group discussions, to engage students in AI-aware learning. Use case studies as a springboard for exploring the ethical implications of AI in a collaborative setting.