Enhanced Temporal Review

Overview View Extraction

Draft Continue to Step 4

Extraction Complete

Total Entities: 17

Actions: 6

Events: 3

Causal Chains: 3

Allen Relations: 4

Timeline: 9

Timeline Overview

Note: The timeline includes only actions and events with clear temporal markers that could be sequenced chronologically.

Timeline Elements: 9

Actions on Timeline: 6 (of 6 extracted)

Events on Timeline: 3 (of 3 extracted)

Temporal Markers

Before report submission 1 elements
Before design submission 1 elements
Project start 1 elements
After Engineer B's retirement 1 elements
During document preparation 1 elements
During AI software use 1 elements
At submission 1 elements
After submission 1 elements
During client review 1 elements

Temporal Consistency Check

Valid

Extracted Actions (6)

Volitional professional decisions with intentions and ethical context

Description: Engineer A decided to use open-source AI software for both report writing and design document creation instead of seeking alternative human review or mentorship.

Temporal Marker: After Engineer B's retirement

Mental State: deliberate

Intended Outcome: Complete deliverables efficiently without traditional mentorship

Fulfills Obligations:

Meeting client deadlines

Guided By Principles:

Innovation
Efficiency

Required Capabilities:

AI tool evaluation Quality assessment

Within Competence: Yes

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Faced with loss of mentorship support and deadline pressure, sought technological solution to maintain productivity and meet client expectations

Ethical Tension: Professional autonomy vs. duty of care - balancing independence with responsibility for quality deliverables

Learning Significance: Critical teaching moment about when to seek human expertise vs. relying on AI tools, especially after losing mentorship support

Stakes: Project quality, professional reputation, client trust, and potentially public safety if design flaws occur

Decision Point: Yes - Story can branch here

Alternative Actions:

Seek new mentorship or peer review arrangement
Request project timeline extension
Decline the assignment due to insufficient support

Narrative Role: inciting_incident

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Use_AI_Software_Decision",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Seek new mentorship or peer review arrangement",
    "Request project timeline extension",
    "Decline the assignment due to insufficient support"
  ],
  "proeth-scenario:characterMotivation": "Faced with loss of mentorship support and deadline pressure, sought technological solution to maintain productivity and meet client expectations",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have maintained quality oversight",
    "Could have allowed time for proper human review",
    "Would have avoided quality issues but potentially damaged client relationship"
  ],
  "proeth-scenario:decisionSignificance": "Critical teaching moment about when to seek human expertise vs. relying on AI tools, especially after losing mentorship support",
  "proeth-scenario:ethicalTension": "Professional autonomy vs. duty of care - balancing independence with responsibility for quality deliverables",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "inciting_incident",
  "proeth-scenario:stakes": "Project quality, professional reputation, client trust, and potentially public safety if design flaws occur",
  "proeth:description": "Engineer A decided to use open-source AI software for both report writing and design document creation instead of seeking alternative human review or mentorship.",
  "proeth:foreseenUnintendedEffects": [
    "Potential quality risks",
    "Loss of human oversight"
  ],
  "proeth:fulfillsObligation": [
    "Meeting client deadlines"
  ],
  "proeth:guidedByPrinciple": [
    "Innovation",
    "Efficiency"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Technology adoption vs Traditional oversight",
    "proeth:resolutionReasoning": "Prioritized technological solution over seeking human alternatives"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Complete deliverables efficiently without traditional mentorship",
  "proeth:requiresCapability": [
    "AI tool evaluation",
    "Quality assessment"
  ],
  "proeth:temporalMarker": "After Engineer B\u0027s retirement",
  "proeth:violatesObligation": [
    "Responsible charge",
    "Competent practice"
  ],
  "proeth:withinCompetence": "uncertain",
  "rdfs:label": "Use AI Software Decision"
}

Description: Engineer A conducted comprehensive review of AI-generated report including cross-checking facts and verifying originality.

Temporal Marker: Before report submission

Mental State: deliberate

Intended Outcome: Ensure report quality and accuracy

Fulfills Obligations:

Due diligence
Quality assurance
Professional competence

Guided By Principles:

Public safety
Professional integrity

Required Capabilities:

Technical review Fact verification

Within Competence: Yes

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Recognized importance of accuracy and originality for report credibility and wanted to maintain professional standards

Ethical Tension: Efficiency vs. thoroughness - balancing time constraints with professional duty to deliver quality work

Learning Significance: Demonstrates proper AI integration practices and the importance of human oversight in professional work

Stakes: Report accuracy, professional credibility, and factual integrity of deliverable

Decision Point: Yes - Story can branch here

Alternative Actions:

Apply same thorough review to all deliverables
Use lighter review for time efficiency
Outsource review to external consultant

Narrative Role: rising_action

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Thorough_Report_Review",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Apply same thorough review to all deliverables",
    "Use lighter review for time efficiency",
    "Outsource review to external consultant"
  ],
  "proeth-scenario:characterMotivation": "Recognized importance of accuracy and originality for report credibility and wanted to maintain professional standards",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have caught design errors early",
    "Could have missed critical report errors",
    "Would have provided expert validation but increased costs"
  ],
  "proeth-scenario:decisionSignificance": "Demonstrates proper AI integration practices and the importance of human oversight in professional work",
  "proeth-scenario:ethicalTension": "Efficiency vs. thoroughness - balancing time constraints with professional duty to deliver quality work",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "rising_action",
  "proeth-scenario:stakes": "Report accuracy, professional credibility, and factual integrity of deliverable",
  "proeth:description": "Engineer A conducted comprehensive review of AI-generated report including cross-checking facts and verifying originality.",
  "proeth:foreseenUnintendedEffects": [
    "Time consumption"
  ],
  "proeth:fulfillsObligation": [
    "Due diligence",
    "Quality assurance",
    "Professional competence"
  ],
  "proeth:guidedByPrinciple": [
    "Public safety",
    "Professional integrity"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Time efficiency vs Quality assurance",
    "proeth:resolutionReasoning": "Prioritized quality verification for report deliverable"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Ensure report quality and accuracy",
  "proeth:requiresCapability": [
    "Technical review",
    "Fact verification"
  ],
  "proeth:temporalMarker": "Before report submission",
  "proeth:withinCompetence": true,
  "rdfs:label": "Thorough Report Review"
}

Description: Engineer A conducted only superficial review of AI-generated design documents without thorough verification.

Temporal Marker: Before design submission

Mental State: deliberate

Intended Outcome: Meet submission deadline efficiently

Fulfills Obligations:

Timely delivery

Guided By Principles:

Efficiency

Required Capabilities:

Design verification Technical analysis

Within Competence: Yes

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Overconfidence in AI capabilities for technical work and time pressure led to shortcuts in quality assurance

Ethical Tension: Efficiency vs. safety - trading thorough verification for faster delivery in technical documents with safety implications

Learning Significance: Key lesson about consistent quality standards and the critical nature of technical document review

Stakes: Technical accuracy, design safety, structural integrity, and potential harm if errors go undetected

Decision Point: Yes - Story can branch here

Alternative Actions:

Apply same thorough review as used for report
Seek peer review for technical validation
Use traditional design methods instead of AI

Narrative Role: rising_action

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Cursory_Design_Review",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Apply same thorough review as used for report",
    "Seek peer review for technical validation",
    "Use traditional design methods instead of AI"
  ],
  "proeth-scenario:characterMotivation": "Overconfidence in AI capabilities for technical work and time pressure led to shortcuts in quality assurance",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have identified technical errors before submission",
    "Would have provided expert technical validation",
    "Would have relied on proven methods but taken longer"
  ],
  "proeth-scenario:decisionSignificance": "Key lesson about consistent quality standards and the critical nature of technical document review",
  "proeth-scenario:ethicalTension": "Efficiency vs. safety - trading thorough verification for faster delivery in technical documents with safety implications",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "rising_action",
  "proeth-scenario:stakes": "Technical accuracy, design safety, structural integrity, and potential harm if errors go undetected",
  "proeth:description": "Engineer A conducted only superficial review of AI-generated design documents without thorough verification.",
  "proeth:foreseenUnintendedEffects": [
    "Higher risk of technical errors"
  ],
  "proeth:fulfillsObligation": [
    "Timely delivery"
  ],
  "proeth:guidedByPrinciple": [
    "Efficiency"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Efficiency vs Safety verification",
    "proeth:resolutionReasoning": "Prioritized deadline over comprehensive technical review"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Meet submission deadline efficiently",
  "proeth:requiresCapability": [
    "Design verification",
    "Technical analysis"
  ],
  "proeth:temporalMarker": "Before design submission",
  "proeth:violatesObligation": [
    "Responsible charge",
    "Public safety",
    "Professional competence"
  ],
  "proeth:withinCompetence": "questionable",
  "rdfs:label": "Cursory Design Review"
}

Description: Engineer A decided not to cite or disclose AI usage in either the report or design documents.

Temporal Marker: During document preparation

Mental State: deliberate

Intended Outcome: Maintain professional appearance and avoid disclosure complications

Guided By Principles:

Convenience

Required Capabilities:

Ethical judgment Transparency assessment

Within Competence: No

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Avoided potential client concerns about AI usage and maintained appearance of traditional engineering work

Ethical Tension: Transparency vs. client confidence - balancing honest disclosure with concerns about client acceptance of AI-assisted work

Learning Significance: Critical teaching point about professional transparency, informed consent, and honest representation of work methods

Stakes: Professional integrity, client trust, informed consent, and truthful representation of professional services

Decision Point: Yes - Story can branch here

Alternative Actions:

Fully disclose AI usage with quality assurance explanation
Seek client permission before using AI tools
Provide disclaimer about AI assistance methods

Narrative Role: rising_action

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Non-disclosure_AI_Usage",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Fully disclose AI usage with quality assurance explanation",
    "Seek client permission before using AI tools",
    "Provide disclaimer about AI assistance methods"
  ],
  "proeth-scenario:characterMotivation": "Avoided potential client concerns about AI usage and maintained appearance of traditional engineering work",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have maintained transparency but risked client concerns",
    "Would have ensured informed consent and avoided ethical issues",
    "Would have provided honest disclosure while maintaining confidence"
  ],
  "proeth-scenario:decisionSignificance": "Critical teaching point about professional transparency, informed consent, and honest representation of work methods",
  "proeth-scenario:ethicalTension": "Transparency vs. client confidence - balancing honest disclosure with concerns about client acceptance of AI-assisted work",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "rising_action",
  "proeth-scenario:stakes": "Professional integrity, client trust, informed consent, and truthful representation of professional services",
  "proeth:description": "Engineer A decided not to cite or disclose AI usage in either the report or design documents.",
  "proeth:foreseenUnintendedEffects": [
    "Transparency violations",
    "Professional credibility risks"
  ],
  "proeth:guidedByPrinciple": [
    "Convenience"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Professional image vs Transparency",
    "proeth:resolutionReasoning": "Prioritized avoiding disclosure over transparency requirements"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Maintain professional appearance and avoid disclosure complications",
  "proeth:requiresCapability": [
    "Ethical judgment",
    "Transparency assessment"
  ],
  "proeth:temporalMarker": "During document preparation",
  "proeth:violatesObligation": [
    "Honesty",
    "Transparency",
    "Professional integrity"
  ],
  "proeth:withinCompetence": false,
  "rdfs:label": "Non-disclosure AI Usage"
}

Description: Engineer A uploaded Client W's confidential information into open-source AI interface without obtaining client permission.

Temporal Marker: During AI software use

Mental State: deliberate

Intended Outcome: Utilize client data for AI-assisted work product development

Guided By Principles:

Convenience

Required Capabilities:

Data protection assessment Client communication

Within Competence: No

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Prioritized work efficiency and AI tool effectiveness over data security protocols and client confidentiality

Ethical Tension: Operational efficiency vs. confidentiality - balancing tool effectiveness with duty to protect sensitive client information

Learning Significance: Essential lesson about data security, client confidentiality, and professional responsibility for protecting sensitive information

Stakes: Client confidentiality, data security, professional trust, potential legal liability, and competitive information protection

Decision Point: Yes - Story can branch here

Alternative Actions:

Obtain explicit client permission for data upload
Use AI tools with anonymized/sanitized data only
Use secure, private AI platforms with confidentiality protections

Narrative Role: rising_action

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Confidential_Data_Upload",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Obtain explicit client permission for data upload",
    "Use AI tools with anonymized/sanitized data only",
    "Use secure, private AI platforms with confidentiality protections"
  ],
  "proeth-scenario:characterMotivation": "Prioritized work efficiency and AI tool effectiveness over data security protocols and client confidentiality",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have maintained client trust and legal compliance",
    "Would have protected confidentiality while still using AI assistance",
    "Would have balanced efficiency with security requirements"
  ],
  "proeth-scenario:decisionSignificance": "Essential lesson about data security, client confidentiality, and professional responsibility for protecting sensitive information",
  "proeth-scenario:ethicalTension": "Operational efficiency vs. confidentiality - balancing tool effectiveness with duty to protect sensitive client information",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "rising_action",
  "proeth-scenario:stakes": "Client confidentiality, data security, professional trust, potential legal liability, and competitive information protection",
  "proeth:description": "Engineer A uploaded Client W\u0027s confidential information into open-source AI interface without obtaining client permission.",
  "proeth:foreseenUnintendedEffects": [
    "Confidentiality breach",
    "Public domain exposure"
  ],
  "proeth:guidedByPrinciple": [
    "Convenience"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Tool effectiveness vs Data protection",
    "proeth:resolutionReasoning": "Prioritized AI functionality over confidentiality obligations"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Utilize client data for AI-assisted work product development",
  "proeth:requiresCapability": [
    "Data protection assessment",
    "Client communication"
  ],
  "proeth:temporalMarker": "During AI software use",
  "proeth:violatesObligation": [
    "Client confidentiality",
    "Data protection",
    "Informed consent"
  ],
  "proeth:withinCompetence": false,
  "rdfs:label": "Confidential Data Upload"
}

Description: Engineer A decided to seal and submit both deliverables despite conducting different levels of review and using undisclosed AI assistance.

Temporal Marker: At submission

Mental State: deliberate

Intended Outcome: Complete professional obligation and deliver work to client

Fulfills Obligations:

Client delivery

Guided By Principles:

Completion

Required Capabilities:

Professional judgment Quality assurance

Within Competence: Yes

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Character Motivation: Felt professional obligation to deliver on schedule and believed work was adequate despite inconsistent review processes

Ethical Tension: Professional accountability vs. delivery pressure - tension between thorough validation and meeting client expectations for timely delivery

Learning Significance: Fundamental lesson about professional seal responsibility, consistent quality standards, and the gravity of professional certification

Stakes: Professional liability, public safety, engineering credibility, regulatory compliance, and potential legal consequences

Decision Point: Yes - Story can branch here

Alternative Actions:

Delay submission for thorough review of all documents
Submit with clear disclaimers about AI assistance and review limitations
Withdraw professional seal until proper validation completed

Narrative Role: climax

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Action_Professional_Seal_Application",
  "@type": "proeth:Action",
  "proeth-scenario:alternativeActions": [
    "Delay submission for thorough review of all documents",
    "Submit with clear disclaimers about AI assistance and review limitations",
    "Withdraw professional seal until proper validation completed"
  ],
  "proeth-scenario:characterMotivation": "Felt professional obligation to deliver on schedule and believed work was adequate despite inconsistent review processes",
  "proeth-scenario:consequencesIfAlternative": [
    "Would have prevented quality issues but delayed project",
    "Would have provided transparency while maintaining delivery schedule",
    "Would have maintained professional integrity but potentially breached contract"
  ],
  "proeth-scenario:decisionSignificance": "Fundamental lesson about professional seal responsibility, consistent quality standards, and the gravity of professional certification",
  "proeth-scenario:ethicalTension": "Professional accountability vs. delivery pressure - tension between thorough validation and meeting client expectations for timely delivery",
  "proeth-scenario:isDecisionPoint": true,
  "proeth-scenario:narrativeRole": "climax",
  "proeth-scenario:stakes": "Professional liability, public safety, engineering credibility, regulatory compliance, and potential legal consequences",
  "proeth:description": "Engineer A decided to seal and submit both deliverables despite conducting different levels of review and using undisclosed AI assistance.",
  "proeth:foreseenUnintendedEffects": [
    "Professional liability",
    "Quality misrepresentation"
  ],
  "proeth:fulfillsObligation": [
    "Client delivery"
  ],
  "proeth:guidedByPrinciple": [
    "Completion"
  ],
  "proeth:hasAgent": "Engineer A (Environmental Engineer)",
  "proeth:hasCompetingPriorities": {
    "@type": "proeth:CompetingPriorities",
    "proeth:priorityConflict": "Project delivery vs Professional standards",
    "proeth:resolutionReasoning": "Prioritized delivery over maintaining consistent professional standards"
  },
  "proeth:hasMentalState": "deliberate",
  "proeth:intendedOutcome": "Complete professional obligation and deliver work to client",
  "proeth:requiresCapability": [
    "Professional judgment",
    "Quality assurance"
  ],
  "proeth:temporalMarker": "At submission",
  "proeth:violatesObligation": [
    "Responsible charge",
    "Professional seal integrity",
    "Honest representation"
  ],
  "proeth:withinCompetence": "questionable",
  "rdfs:label": "Professional Seal Application"
}

Extracted Events (3)

Occurrences that trigger ethical considerations and state changes

Description: Engineer B's retirement removes mentorship support for Engineer A, leaving them without experienced guidance.

Temporal Marker: Project start

Activates Constraints:

Competence_Verification_Required

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Emotional Impact: Anxiety and uncertainty for Engineer A; concern about increased responsibility without safety net; potential isolation

Stakeholder Consequences:

engineer_a: Loss of trusted advisor, increased professional vulnerability
client_w: Potential quality risks due to reduced oversight
engineering_firm: Knowledge transfer gap, continuity risks

Learning Moment: Highlights importance of mentorship in professional development and need for succession planning

Ethical Implications: Reveals vulnerability in professional support systems; raises questions about duty to seek adequate guidance when facing challenging work

Discussion Prompts:

What responsibilities do organizations have for ensuring continuity of mentorship?
How should engineers assess their own competence when losing guidance?
What alternative support systems should be established?

Tension: medium Pacing: slow_burn

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Event_Mentor_Loss_Event",
  "@type": "proeth:Event",
  "proeth-scenario:crisisIdentification": false,
  "proeth-scenario:discussionPrompts": [
    "What responsibilities do organizations have for ensuring continuity of mentorship?",
    "How should engineers assess their own competence when losing guidance?",
    "What alternative support systems should be established?"
  ],
  "proeth-scenario:dramaticTension": "medium",
  "proeth-scenario:emotionalImpact": "Anxiety and uncertainty for Engineer A; concern about increased responsibility without safety net; potential isolation",
  "proeth-scenario:ethicalImplications": "Reveals vulnerability in professional support systems; raises questions about duty to seek adequate guidance when facing challenging work",
  "proeth-scenario:learningMoment": "Highlights importance of mentorship in professional development and need for succession planning",
  "proeth-scenario:narrativePacing": "slow_burn",
  "proeth-scenario:stakeholderConsequences": {
    "client_w": "Potential quality risks due to reduced oversight",
    "engineer_a": "Loss of trusted advisor, increased professional vulnerability",
    "engineering_firm": "Knowledge transfer gap, continuity risks"
  },
  "proeth:activatesConstraint": [
    "Competence_Verification_Required"
  ],
  "proeth:causesStateChange": "Engineer A now operating without experienced mentorship; increased responsibility for independent decision-making",
  "proeth:createsObligation": [
    "Ensure_Adequate_Supervision",
    "Maintain_Professional_Standards"
  ],
  "proeth:description": "Engineer B\u0027s retirement removes mentorship support for Engineer A, leaving them without experienced guidance.",
  "proeth:emergencyStatus": "medium",
  "proeth:eventType": "exogenous",
  "proeth:temporalMarker": "Project start",
  "proeth:urgencyLevel": "medium",
  "rdfs:label": "Mentor Loss Event"
}

Description: Client W identifies significant quality differences between the report and design documents, revealing inconsistent work standards.

Temporal Marker: After submission

Activates Constraints:

Professional_Credibility_At_Risk
Client_Relationship_Damaged

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Emotional Impact: Embarrassment and professional concern for Engineer A; disappointment and suspicion from Client W; potential loss of trust

Stakeholder Consequences:

engineer_a: Professional reputation damaged, credibility questioned, potential loss of client
client_w: Project quality concerns, potential delays, trust in engineer compromised
engineering_profession: Public confidence in professional standards potentially affected

Learning Moment: Demonstrates how inconsistent quality control creates obvious professional credibility issues; shows client expectations for uniform excellence

Ethical Implications: Reveals tension between efficiency and thoroughness; demonstrates how shortcuts become visible to competent clients; raises questions about professional integrity in AI usage

Discussion Prompts:

How do quality inconsistencies affect professional trust relationships?
What does this reveal about the engineer's approach to AI assistance?
How should professionals maintain consistent standards across all deliverables?

Crisis / Turning Point Tension: high Pacing: escalation

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Event_Quality_Discrepancy_Discovery",
  "@type": "proeth:Event",
  "proeth-scenario:crisisIdentification": true,
  "proeth-scenario:discussionPrompts": [
    "How do quality inconsistencies affect professional trust relationships?",
    "What does this reveal about the engineer\u0027s approach to AI assistance?",
    "How should professionals maintain consistent standards across all deliverables?"
  ],
  "proeth-scenario:dramaticTension": "high",
  "proeth-scenario:emotionalImpact": "Embarrassment and professional concern for Engineer A; disappointment and suspicion from Client W; potential loss of trust",
  "proeth-scenario:ethicalImplications": "Reveals tension between efficiency and thoroughness; demonstrates how shortcuts become visible to competent clients; raises questions about professional integrity in AI usage",
  "proeth-scenario:learningMoment": "Demonstrates how inconsistent quality control creates obvious professional credibility issues; shows client expectations for uniform excellence",
  "proeth-scenario:narrativePacing": "escalation",
  "proeth-scenario:stakeholderConsequences": {
    "client_w": "Project quality concerns, potential delays, trust in engineer compromised",
    "engineer_a": "Professional reputation damaged, credibility questioned, potential loss of client",
    "engineering_profession": "Public confidence in professional standards potentially affected"
  },
  "proeth:activatesConstraint": [
    "Professional_Credibility_At_Risk",
    "Client_Relationship_Damaged"
  ],
  "proeth:causedByAction": "http://proethica.org/cases/7#Action_Cursory_Design_Review",
  "proeth:causesStateChange": "Client confidence undermined; professional credibility questioned; relationship strain evident",
  "proeth:createsObligation": [
    "Address_Quality_Concerns",
    "Explain_Discrepancies",
    "Restore_Client_Confidence"
  ],
  "proeth:description": "Client W identifies significant quality differences between the report and design documents, revealing inconsistent work standards.",
  "proeth:emergencyStatus": "high",
  "proeth:eventType": "outcome",
  "proeth:temporalMarker": "After submission",
  "proeth:urgencyLevel": "high",
  "rdfs:label": "Quality Discrepancy Discovery"
}

Description: Client W discovers technical errors in the design documents that require revision, indicating compromised technical quality.

Temporal Marker: During client review

Activates Constraints:

Public_Safety_Concern
Professional_Seal_Integrity_Violated

Scenario Metadata

Pedagogical context for interactive teaching scenarios

Emotional Impact: Alarm and professional crisis for Engineer A; serious concern from Client W about safety and competence; potential public safety anxiety

Stakeholder Consequences:

engineer_a: Potential license suspension, legal liability, career-ending consequences
client_w: Project safety concerns, potential liability, need for complete design review
public: Safety risk from flawed engineering design, loss of confidence in professional oversight
regulatory_bodies: Investigation required, potential disciplinary action, precedent-setting case

Learning Moment: Demonstrates severe consequences of inadequate review of AI-generated technical content; shows how professional seal creates legal and ethical accountability

Ethical Implications: Reveals critical tension between AI efficiency and professional competence requirements; demonstrates how professional certification creates accountability for all work regardless of generation method; raises fundamental questions about AI integration in safety-critical engineering work

Discussion Prompts:

What level of review is required when using AI for technical design work?
How does professional seal application create accountability for AI-assisted work?
What are the potential safety and legal consequences of this situation?

Crisis / Turning Point Tension: high Pacing: crisis

RDF JSON-LD

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  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "proeth-scenario": "http://proethica.org/ontology/scenario#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/cases/7#Event_Technical_Error_Detection",
  "@type": "proeth:Event",
  "proeth-scenario:crisisIdentification": true,
  "proeth-scenario:discussionPrompts": [
    "What level of review is required when using AI for technical design work?",
    "How does professional seal application create accountability for AI-assisted work?",
    "What are the potential safety and legal consequences of this situation?"
  ],
  "proeth-scenario:dramaticTension": "high",
  "proeth-scenario:emotionalImpact": "Alarm and professional crisis for Engineer A; serious concern from Client W about safety and competence; potential public safety anxiety",
  "proeth-scenario:ethicalImplications": "Reveals critical tension between AI efficiency and professional competence requirements; demonstrates how professional certification creates accountability for all work regardless of generation method; raises fundamental questions about AI integration in safety-critical engineering work",
  "proeth-scenario:learningMoment": "Demonstrates severe consequences of inadequate review of AI-generated technical content; shows how professional seal creates legal and ethical accountability",
  "proeth-scenario:narrativePacing": "crisis",
  "proeth-scenario:stakeholderConsequences": {
    "client_w": "Project safety concerns, potential liability, need for complete design review",
    "engineer_a": "Potential license suspension, legal liability, career-ending consequences",
    "public": "Safety risk from flawed engineering design, loss of confidence in professional oversight",
    "regulatory_bodies": "Investigation required, potential disciplinary action, precedent-setting case"
  },
  "proeth:activatesConstraint": [
    "Public_Safety_Concern",
    "Professional_Seal_Integrity_Violated"
  ],
  "proeth:causedByAction": "http://proethica.org/cases/7#Action_Professional_Seal_Application",
  "proeth:causesStateChange": "Design integrity compromised; professional seal credibility damaged; potential safety implications activated",
  "proeth:createsObligation": [
    "Immediate_Error_Correction",
    "Investigate_All_AI_Generated_Content",
    "Report_To_Professional_Board"
  ],
  "proeth:description": "Client W discovers technical errors in the design documents that require revision, indicating compromised technical quality.",
  "proeth:emergencyStatus": "critical",
  "proeth:eventType": "outcome",
  "proeth:temporalMarker": "During client review",
  "proeth:urgencyLevel": "critical",
  "rdfs:label": "Technical Error Detection"
}

Causal Chains (3)

NESS test analysis: Necessary Element of Sufficient Set

Causal Language: Engineer A conducted only superficial review of AI-generated design documents without thorough verification, which directly led to Client W discovering technical errors that required revision

Necessary Factors (NESS):

AI-generated design documents with potential errors
Inadequate review process by Engineer A
Professional seal applied without proper verification

Sufficient Factors:

Combination of AI-generated content + superficial review + professional certification

Counterfactual Test: With thorough design review, technical errors would likely have been caught and corrected before client discovery

Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:

Use AI Software Decision
Engineer A decides to use open-source AI software for design document creation
Cursory Design Review
Engineer A conducts only superficial review without thorough verification
Professional Seal Application
Engineer A seals and submits design documents despite inadequate review
Technical Error Detection
Client W discovers technical errors requiring revision

RDF JSON-LD

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  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/cases/7#CausalChain_78a207d3",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Engineer A conducted only superficial review of AI-generated design documents without thorough verification, which directly led to Client W discovering technical errors that required revision",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer A decides to use open-source AI software for design document creation",
      "proeth:element": "Use AI Software Decision",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A conducts only superficial review without thorough verification",
      "proeth:element": "Cursory Design Review",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A seals and submits design documents despite inadequate review",
      "proeth:element": "Professional Seal Application",
      "proeth:step": 3
    },
    {
      "proeth:description": "Client W discovers technical errors requiring revision",
      "proeth:element": "Technical Error Detection",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Cursory Design Review",
  "proeth:counterfactual": "With thorough design review, technical errors would likely have been caught and corrected before client discovery",
  "proeth:effect": "Technical Error Detection",
  "proeth:necessaryFactors": [
    "AI-generated design documents with potential errors",
    "Inadequate review process by Engineer A",
    "Professional seal applied without proper verification"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Combination of AI-generated content + superficial review + professional certification"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Engineer B's retirement removes mentorship support, leaving Engineer A without experienced guidance, which contributed to inconsistent quality control between deliverables

Necessary Factors (NESS):

Loss of experienced oversight
Engineer A's inexperience with AI tools
Different review approaches for different deliverables

Sufficient Factors:

Combination of lost mentorship + inconsistent review practices + AI tool usage

Counterfactual Test: With continued mentorship, consistent quality standards would likely have been maintained across both deliverables

Responsibility Attribution:

Agent: Engineer A
Type: indirect
Within Agent Control: No

Causal Sequence:

Mentor Loss Event
Engineer B's retirement removes experienced guidance and quality oversight
Use AI Software Decision
Engineer A proceeds with AI tools without experienced guidance on quality standards
Inconsistent Review Approaches
Thorough report review vs cursory design review creates quality discrepancy
Quality Discrepancy Discovery
Client W identifies significant quality differences between deliverables

RDF JSON-LD

{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/cases/7#CausalChain_81220132",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Engineer B\u0027s retirement removes mentorship support, leaving Engineer A without experienced guidance, which contributed to inconsistent quality control between deliverables",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer B\u0027s retirement removes experienced guidance and quality oversight",
      "proeth:element": "Mentor Loss Event",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A proceeds with AI tools without experienced guidance on quality standards",
      "proeth:element": "Use AI Software Decision",
      "proeth:step": 2
    },
    {
      "proeth:description": "Thorough report review vs cursory design review creates quality discrepancy",
      "proeth:element": "Inconsistent Review Approaches",
      "proeth:step": 3
    },
    {
      "proeth:description": "Client W identifies significant quality differences between deliverables",
      "proeth:element": "Quality Discrepancy Discovery",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Mentor Loss Event",
  "proeth:counterfactual": "With continued mentorship, consistent quality standards would likely have been maintained across both deliverables",
  "proeth:effect": "Quality Discrepancy Discovery",
  "proeth:necessaryFactors": [
    "Loss of experienced oversight",
    "Engineer A\u0027s inexperience with AI tools",
    "Different review approaches for different deliverables"
  ],
  "proeth:responsibilityType": "indirect",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Combination of lost mentorship + inconsistent review practices + AI tool usage"
  ],
  "proeth:withinAgentControl": false
}

Causal Language: Engineer A uploaded Client W's confidential information into open-source AI interface without obtaining permission, which compromised data security and contributed to quality control issues

Necessary Factors (NESS):

Confidential client data
Open-source AI platform with potential security risks
Lack of client permission for data sharing

Sufficient Factors:

Unauthorized upload of confidential data to unsecured AI platform

Counterfactual Test: Without uploading confidential data or with proper permission and security protocols, ethical breach would have been avoided

Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:

Use AI Software Decision
Engineer A decides to use open-source AI software for client work
Confidential Data Upload
Engineer A uploads Client W's confidential information without permission
Non-disclosure AI Usage
Engineer A fails to disclose AI usage, compounding transparency issues
Quality Discrepancy Discovery
Client discovers quality issues, potentially leading to discovery of unauthorized data use

RDF JSON-LD

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  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/cases/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/cases/7#CausalChain_f9f55c39",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Engineer A uploaded Client W\u0027s confidential information into open-source AI interface without obtaining permission, which compromised data security and contributed to quality control issues",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer A decides to use open-source AI software for client work",
      "proeth:element": "Use AI Software Decision",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A uploads Client W\u0027s confidential information without permission",
      "proeth:element": "Confidential Data Upload",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A fails to disclose AI usage, compounding transparency issues",
      "proeth:element": "Non-disclosure AI Usage",
      "proeth:step": 3
    },
    {
      "proeth:description": "Client discovers quality issues, potentially leading to discovery of unauthorized data use",
      "proeth:element": "Quality Discrepancy Discovery",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Confidential Data Upload",
  "proeth:counterfactual": "Without uploading confidential data or with proper permission and security protocols, ethical breach would have been avoided",
  "proeth:effect": "Quality Discrepancy Discovery",
  "proeth:necessaryFactors": [
    "Confidential client data",
    "Open-source AI platform with potential security risks",
    "Lack of client permission for data sharing"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Unauthorized upload of confidential data to unsecured AI platform"
  ],
  "proeth:withinAgentControl": true
}

Allen Temporal Relations (4)

Interval algebra relationships with OWL-Time standard properties

From Entity	Allen Relation	To Entity	OWL-Time Property	Evidence
Engineer B retirement	before Entity1 is before Entity2	Engineer A needing to deliver report and design documents	`time:before` http://www.w3.org/2006/time#before	But Engineer B recently retired and was no longer available to Engineer A in a work capacity. Faced ...
Engineer A's reliance on Engineer B's guidance	before Entity1 is before Entity2	Engineer B's retirement	`time:before` http://www.w3.org/2006/time#before	Previously, Engineer A had relied on guidance and quality assurance reviews by their mentor and supe...
Site observation	overlaps Entity1 starts before Entity2 and ends during Entity2	Client W retention for report preparation	`time:intervalOverlaps` http://www.w3.org/2006/time#intervalOverlaps	This work required Engineer A to perform an analysis of groundwater monitoring data from a site Engi...
BER Case 90-6	before Entity1 is before Entity2	Current AI case discussion	`time:before` http://www.w3.org/2006/time#before	Almost 35 years ago, in BER Case 90-6, the BER looked at a hypothetical involving an engineer's use ...

About Allen Relations & OWL-Time

Allen's Interval Algebra provides 13 basic temporal relations between intervals. These relations are mapped to OWL-Time standard properties for interoperability with Semantic Web temporal reasoning systems and SPARQL queries.

Each relation includes both a ProEthica custom property and a time:* OWL-Time property for maximum compatibility.

PASS 3: Temporal Dynamics

Extraction Complete

Timeline Overview

Temporal Markers

Temporal Consistency Check

Extracted Actions (6)

Use AI Software Decision Engineer A (Environmental Engineer)

Fulfills Obligations:

Guided By Principles:

Required Capabilities:

Scenario Metadata

Thorough Report Review Engineer A (Environmental Engineer)

Fulfills Obligations:

Guided By Principles:

Required Capabilities:

Scenario Metadata

Cursory Design Review Engineer A (Environmental Engineer)

Fulfills Obligations:

Guided By Principles:

Required Capabilities:

Scenario Metadata

Non-disclosure AI Usage Engineer A (Environmental Engineer)

Guided By Principles:

Required Capabilities:

Scenario Metadata

Confidential Data Upload Engineer A (Environmental Engineer)

Guided By Principles:

Required Capabilities:

Scenario Metadata

Professional Seal Application Engineer A (Environmental Engineer)

Fulfills Obligations:

Guided By Principles:

Required Capabilities:

Scenario Metadata

Extracted Events (3)

Mentor Loss Event

Activates Constraints:

Scenario Metadata

Quality Discrepancy Discovery

Activates Constraints:

Scenario Metadata

Technical Error Detection

Activates Constraints:

Scenario Metadata

Causal Chains (3)

Cursory Design Review Technical Error Detection

Necessary Factors (NESS):

Sufficient Factors:

Responsibility Attribution:

Causal Sequence:

Mentor Loss Event Quality Discrepancy Discovery

Necessary Factors (NESS):

Sufficient Factors:

Responsibility Attribution:

Causal Sequence:

Confidential Data Upload Quality Discrepancy Discovery

Necessary Factors (NESS):

Sufficient Factors:

Responsibility Attribution:

Causal Sequence:

Allen Temporal Relations (4)

About Allen Relations & OWL-Time