PROFESSIONAL LEARNING CATALOG
Browse published courses and clearly labeled catalog directions. Available, in-development, and planned offerings remain visibly distinct so the catalog never implies a credential that Logos cannot yet issue.
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AI & Technology · Courses
CATALOG TAXONOMY
Only Education & Learning contains published courses today. The broader structure guides responsible expansion.
Use, build, and govern contemporary technology with practical evidence and accountable judgment.
Available nowDesign learning, assessment, and educational practice that make reasoning and growth visible.
PlannedEvaluate evidence, surface assumptions, weigh alternatives, and explain consequential decisions.
PlannedLead with clarity, communicate across differences, and make professional judgment inspectable.
PlannedBuild transferable capabilities through authentic work, reflection, and portfolio-ready evidence.
PROGRAM HIERARCHY
Names describe the scope of the learning and evidence. Issuance still requires the published policy for the specific program.
Focused learning experiences with defined outcomes, application, and completion requirements.
MicrocredentialsAssessed demonstrations of a specific, professionally relevant capability.
Professional CertificatesCoherent programs combining multiple capabilities and independently reviewed evidence.
Professional CredentialsLarger, governed programs integrating multiple certificates, substantial practice, and a capstone or portfolio review.
COMPLETE DRAFTS · NOT YET ENROLLING
Each course has a complete source, instruction, evidence, and rubric draft. Preview is open; enrollment and credential issuance remain unavailable until accountable review and publication.
Understand model capabilities, limitations, prompting, verification, privacy, and appropriate professional use.
A documented workflow that verifies an AI-assisted output and explains its use boundaries.
Design and evaluate a bounded generative-AI workflow for a real professional task.
A working application or workflow, evaluation record, and risk-control rationale.
Build an agent that uses tools under explicit permissions, failure handling, and human oversight.
A functioning agent, tool contract, trace evidence, and documented approval boundaries.
Create a source-grounded retrieval workflow and evaluate whether its answers remain supported.
A working RAG system with a test set, citations, retrieval analysis, and failure review.
Connect AI literacy, applied development, evaluation, governance, and accountable deployment decisions.
An AI impact and governance plan with risk measures, controls, monitoring, and withdrawal rules.
Develop the Python foundations needed to inspect data, call models, test outputs, and automate workflows.
A tested Python project that calls or evaluates an AI system and documents its results.
PUBLISHING MODEL
Research, source curation, competencies, instruction, assessment, independent review, version control, credential governance, and public claims remain inspectable and separately approved.