Portfolio 2: Training a Global Population

Overview

The Human Engineering Challenge

A new interface innovator at your company has developed a new idea that has inspired leadership and revealed a vast market. The VP leading the project has aggressive global adoption targets:

• Year 1: 10% Adoption (Early Adopters)
• Year 2: 30% Adoption (Early Majority)
• Year 3: 60% Adoption (Late Majority)

Constraints:

• Scale: With millions of users, human-to-human instruction is impossible.
• Risk: Future regulatory scrutiny and potential liability for user harm are significant. The system must minimize users harming themselves or others, physically or otherwise.
• Length: Your final proposal must not exceed 1,500 words (excluding appendices).

Objective: Design a Training Architecture that uses technology to train the user to proficiency without large-scale human intervention. This system must scale globally, adapt to users with lower skills, be acceptable to the user, and provide your company with a good-faith argument that a substantive effort was made to protect users from harm.

Input Data: Use the Item Description provided by your training partner.

Assignment Requirements

Submit a formal engineering proposal detailing the rollout strategy. Ground all decisions in the cours e readings: Lee et al. (Textbook), Kaplan et al. (2021), and Sawyer et al.

Part 1: Training Needs Analysis (TNA)

Reference: Lee Chapter 17 Analyze the Item Description to define the learning gap.

• Define the KSAs: Break down device operation into Declarative Knowledge (facts/labels), Procedural Skills (physical/cognitive sequences), and Strategic Abilities (judgment calls in dynamic situations).
• Gap Analysis: Compare these KSAs against the User Population defined in the Item Description. Identify specific friction points.
• Critical Safety Failures: Identify the single most dangerous user error that results in injury or catastrophic system failure.

Part 2: Technological Intervention

Reference: Kaplan et al. (2021) & Lee Chapter 11 Select a training modality that functions without a human instructor.

• Modality Selection: Choose a platform (e.g., Mobile AR/VR, Embedded Training, Computer Aided Instruction).
• Justification: Defend this choice using real data (e.g., the Kaplan et al. meta-analysis). Explain why this modality is superior for the specific KSAs identified in Part 1.
• Feedback Loop: Describe the machine-to-human feedback mechanism. Address Detection (how the system knows a mistake was made), Signaling (visual/audio/haptic alerts), and Qualification (the specific metric for passing).

Part 3: Trust & Human-Autonomy Teaming

Reference: Lee Chapter 10 & Sawyer et al. Address the risks of Disuse (lack of trust) and Misuse (complacency).

• Preventing Disuse (Building Trust): Describe how the training interface demonstrates reliability to a skeptical new user in Year 1.
• Preventing Misuse (Fighting Complacency): Describe a specific training feature designed to force the user to remain attentive and not over-rely on the system in Year 2.

Part 4: Global Rollout Strategy

Reference: Lee Chapter 18 & Week 3 Contextual Inquiry Design the phased expansion, providing an exemplar for subpopulations.

• Year 1 (The Beachhead): Focus upon the specific sub-segment of the population easiest to train. Explain why fthis group is the logical starting point.
• Year 2 (The Wave): Focus upon a challenging subpopulation within your main population. Explain why this group is necessary and how you will accommodate them.
• Year 3 (The Late Majority): Select one specific geographic region outside of the United States that is presently unfamiliar to you. Explain how the training interface must change to accommodate this culture and locale.

Part 5: Regulatory & Safety Defense

Reference: Lee Chapter 1 & General Standards The training architecture serves as the primary defense against liability.

• Safety Shield (Gatekeeping): Explicitly describe how the training addresses the Critical Safety Failures identified in Part 1. How does the system technically prevent a user from operating the device until they master safety-critical tasks?
• Regulatory Alignment: Identify a relevant standard (ISO, OSHA, FDA, or Standard of Care). Explain how your training design meets the legal duty to warn and train under this standard.
• Unintended Consequences: Identify how users might try to work around your training and safety features. Explain your mitigation strategy.

Part 6: Evidence-Based Heuristics

Identify the Human Engineering heuristics underpinning your global automated training systems. Prove to regulatory bodies that your plan is grounded in sound evidence.

• Format: Use imperative, directive language structured as: [Trigger Condition] -> [Required Action] -> [Reason].
• Example: "When a safety-critical error occurs, feedback must be immediate and multimodal (Visual + Haptic) to ensure detection in high-workload environments."

Deliverable Format

• Submission: Single PDF file.
• Filename: [YOURSURNAME]_Portfolio_2.pdf
• Style: Professional Engineering Report.
• Word Limit: Strictly 1,500 words maximum for the Executive Summary and Main Report combined. Appendices do not count toward this limit.

Required Structure:

1. Title Page
2. Executive Summary (1 paragraph)
3. Main Report (Parts 1–6)
4. Appendix A: Item Description + interview notes (provided by partner)
5. Appendix B: Hierarchical Task Analysis (HTA) diagram
6. Appendix C: Citations in IEEE Format
7. Appendix D: Generative AI Statement and Transcript

Writing Standards: Your company requires writing that is dense, clear, and actionable. To succeed within the 1,500-word limit:

• No Filler: Get straight to the analysis. You do not have space to be vague.
• Scannability: Use bullet points to organize KSAs, feedback loops, and heuristics.
• Directness: Use active voice and imperative language.
• Efficient Citations: Use brief parenthetical citations (e.g., Kaplan et al., 2021) in the text instead of summarizing the readings.

Evaluation Criteria & Policies

• Linkage: Design decisions in Part 2 must directly address the specific gaps found in Part 1.
• Evidence: Choices must be justified using specific concepts from readings (especially Chapters 7, 8, and 9).
• Feasibility: The rollout strategy must be realistic for the described population.

Strategic Warning: This assignment is the foundational infrastructure for much of the remainder of the course. You will reuse many components here in Portfolio 3 and the Final Exam. A weak or vague Portfolio 2 will objectively make the remainder of the course more difficult and require extensive rework.

Use of Generative AI: Generative AI may be used on this assignment. However, the ideas and arguments must reflect your own engineering judgment, and you are entirely responsible for understanding all submitted content. You must provide a brief statement of how you used Gen AI, its contribution to your work, and your entire prompt/response transcript in Appendix D.