These learning modules use real or realistic cases to teach moral reasoning as a practical skill. They do not supply a predetermined conclusion. Instead, each module asks learners to identify relevant facts, distinguish descriptive claims from moral judgments, clarify competing values and duties, and defend a conclusion that remains open to criticism and revision.
The modules are grounded in the Holcombe Ethics Framework Suite. The Holcombe Case-Based Moral Reasoning Framework guides case analysis; the Moral Disagreement Diagnostic Model helps locate the source of disagreement; the Empirical Moral Reasoning Integration Model connects moral judgment to relevant evidence; the Justice-Without-Politics Rawlsian Reinterpretation examines fairness under uncertainty; and the Applied AI Ethics Risk and Governance Framework is used where technology creates institutional risks and responsibilities.
Each module is designed to make the connection between ethical theory, empirical evidence, and real-world judgment visible. Learners are expected to do the reasoning themselves.
The Learning Modules apply a connected set of ethical-reasoning tools. Each guide explains one part of the method. Together, they move from a real case, through evidence and disagreement, to justice and institutional responsibility.
The Holcombe Case-Based Moral Reasoning Framework teaches ethical reasoning through concrete dilemmas rather than abstract theory alone. Learners identify the relevant facts, distinguish facts from moral judgments, map affected stakeholders, clarify competing values and duties, and defend a conclusion that can withstand criticism.
HCBMR is the core method used throughout the Learning Modules. It does not tell learners what to think. It gives them a disciplined way to reason about what they should do.
The Empirical Moral Reasoning Integration Model connects moral psychology with normative ethical reasoning. It examines how intuitions, value priorities, and cognitive patterns shape moral disagreement, while refusing to treat those psychological facts as moral proof.
EMRIM helps learners ask two different questions: Why do people respond differently to this case? And which response is ethically justified after the evidence and competing principles have been examined?
The Moral Disagreement Diagnostic Model identifies whether people disagree about facts, moral priorities, ethical standards, or acceptable tradeoffs. It replaces shallow argument with diagnosis.
MDDM is useful when a disagreement appears intractable. It helps learners see whether the conflict can be resolved through better evidence, whether it requires ethical evaluation, or whether reasonable disagreement may remain.
The Justice-Without-Politics Rawlsian Reinterpretation returns Rawlsian reasoning to its moral core: fairness, reciprocity, uncertainty, and the moral arbitrariness of unchosen advantage. It separates those questions from the assumption that one political program automatically follows.
JWPR allows learners to take justice seriously while examining competing institutional responses, including libertarian and pluralist critiques. The point is not to avoid disagreement. It is to make the moral principles and tradeoffs explicit.
The Applied Ethics Risk and Governance Framework addresses the question that follows an ethical judgment: who is responsible for acting on it? It identifies ethical risks, affected stakeholders, competing values, decision authority, safeguards, and continuing review.
AERGF is an organizational ethics and governance framework, applicable wherever institutions must identify ethical risks, assign responsibility, implement safeguards, and review outcomes.
AERGF treats AI ethics as a governance problem, not a public-relations statement or a one-time compliance exercise. It is particularly useful when AI systems affect access, opportunity, privacy, autonomy, safety, or the distribution of institutional power.
The heat wave neighbor case This investigation asks whether people merely have duties not to harm others or also have duties to help when a vulnerable person faces severe danger and assistance would require a limited sacrifice. Level Estimated time Textbook connection Intermediate 60 to 80 minutes Chapter 4 pp. 75 to 77 and Chapter […]
Read MoreThe Doing Is the Thinking Generative AI can produce a polished answer before a learner has developed the ability to produce, evaluate, or defend it. This investigation asks when AI assistance extends human agency and when it transfers the appearance of capability from the system to the user. Level Estimated time Textbook connection Intermediate 75 […]
Read MoreObservation socialization and developing agency This constructed case asks when an observer may criticize a gender-asymmetric family practice without confusing unfamiliarity, inference, and evidence. The case does not describe an identified family or establish facts about Muslim families generally. Level Estimated time Textbook connection Introductory to intermediate 75 to 95 minutes Chapter 7 pp. 179 […]
Read MorePolitical influence and procedural justice in sport This investigation uses a 2026 FIFA World Cup controversy to examine whether a person who receives an advantage through an extraordinary procedure has a moral responsibility to decline it. The case concerns institutional process and personal responsibility, not support for or opposition to any political figure. Level Estimated […]
Read MoreThe Thirlwall Inquiry and institutional responsibility This investigation compares direct wrongdoing with failures to investigate, escalate, and protect. It asks how moral responsibility should be allocated when one person causes harm and other people or institutions fail to respond to credible danger. Level Estimated time Textbook connection Advanced 60 to 80 minutes Chapter 4 pp. […]
Read MoreThis learning module explores the intersection of AI infrastructure governance & environmental justice through a deep dive into "Project Marvel"- a proposed $14.5 billion hyperscale data center in Bessemer, Alabama. By moving beyond simple "legal vs. illegal" framing, the guide provides a rigorous Critical Moral Reasoning framework to evaluate the ethical implications of large-scale technology projects on local communities. Inside the Module Defining Digital Redlining: Learn how the siting of resource-intensive data centers can mirror historic patterns of systemic exclusion and environmental burden in marginalized communities. The "Project Marvel" Case Study: Analyze the specific impacts of an 18-building, 700+ acre...
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