Learning Systems That Learn — Visualizing Design Intelligence for Every Course
modulr blueprint dashboard
Course
Responsible AI Use in Client-Facing Work: Decision-Making for FutureTech Consultants
Organization
FutureTech Global
Status
Ready
DESIGN RATIONALE — WHAT DID WE PRIORITIZE AND WHY
content priority
instructional priority
design constraint
Confidentiality & Compliance — Central to FutureTech’s risk posture; protects client trust and data integrity.
Scenario-based judgment under pressure — Builds decision confidence in realistic, high-stakes contexts.
Hybrid, time-limited global teams — Required concise, mobile-optimized modules and reusable templates.
Operational
Fear and uncertainty about what AI use is “compliant” or “safe.”
Tight client deadlines and hybrid, multi-region collaboration.
Inconsistent tool use and lack of approved-tool knowledge.
Overreliance on AI without fact-checking or review.
Limited budget for development; required modular reuse.
who is this for (Audience Snapshot)
Consultants, project managers, and technical analysts with 3–7 years’ experience.
Operate in hybrid environments across New York, London, and Singapore.
Digitally fluent but new to structured AI governance; constrained by confidentiality and client timelines.
what it covers (content coverage)
Mapped 100% to core content basis: Confidentiality, Tool Approval, Attribution, AI Governance, Literacy.
Tool Approval, Attribution, AI Governance, Literacy.
Five focused modules + two virtual workshops.
SME validation pending on policy excerpts, tool lists, and disclosure language.
how it’s designed
Across all five modules, Knowles and Bloom remain strongest, supporting authentic, high-stakes decision-making. Merrill, Gagné, and CLT appear at moderate intensity to structure practice and feedback. Modulr.Context anchors every judgment prompt in FutureTech’s operational reality, ensuring scenario relevance and policy linkage. Modules 3–5 slightly elevate Bloom-level evaluation and creation tasks through drafting and triage exercises.
Heavier on contextual realism and trade-off decision practice; lighter on procedural policy explanation.
DESIGN INSIGHTS — WHAT THIS TELLS US
Strength: Strongest performance in applied judgment and relevance to real work (Knowles, Bloom).
Gap: Pending SME inputs (policy excerpts, approval registry) increase extraneous load risk.
Action: Embed worked examples and final job aids before launch to strengthen Merrill/Gagné alignment.
Guidance: Facilitators should emphasize escalation and tool-approval pathways in workshops.
design drivers
behavioral
operational
technical
Behavioral
what learners will achieve (Objectives
Learners will:
Apply AI use policies in real deliverables.
Evaluate confidentiality and data risks.
Choose tools responsibly.
Attribute AI outputs transparently.
Balance speed and accuracy under pressure.
Objective → Module Map:
1–2 → M1 (Confidentiality)
3 → M2 (Tool Appropriateness)
4 → M3 (Attribution)
1,4 → M4 (Policy Application)
5 → M5 (Speed vs Accuracy)
Design Emphasis: How This Course Differs from Generic Training
Design emphasis distributes as follows — Knowles’ Andragogy 25%, Bloom’s Taxonomy 25%, Merrill’s First Principles 17%, Gagné’s Nine Events 17%, Cognitive Load Theory 16%. This blend emphasizes real-world autonomy, higher-order judgment, and clear task scaffolding within manageable cognitive load.
MODULE HIGHLIGHTS
M1 — Confidentiality under deadline; “segment-before-send” risk map.
M2 — Tool appropriateness; approval-first checklist for summarization tasks.
M3 — Attribution and transparency; human-in-loop quality review.
M4 — Governance in workflows; manager-focused memo and approval chain.
M5 — Speed vs accuracy; live triage under client pressure.
DELIVERABLES / DOWNLOADS
NEXT STEPS
Review → Approve → Build → Integrate into LMS.
Option: Request Dashboard Walkthrough for SME artifact alignment.
Team Rating (Pending): ⭐⭐⭐⭐☆
Final feedback summary to be added once implementation is complete.
Metadata and Footer
Generated By: Modulr.Outline v1.4.1
Based On: Modulr.Context v2 and Modulr.AI Behavioral Rules v1.2.5
Compliance Check: ✔ Tier 1 Behavioral Rules
Review Notes: [auto-filled or left blank]
Blueprint Version: 1.0