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University of FlowStateOpen online learning

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AI Tools Specialist

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Current availability

The subject-specific curriculum is authored but not yet published for completion. Learners may inspect its scope and learning design now. Start, grading, and completion remain unavailable until every listed gate passes.

  • qualified review approval is required
  • reviewer identity, qualification, evidence, and approval date are required

What this course establishes

  • Decide whether an AI-assisted workflow is appropriate and specify task, context, constraints, output schema, evidence and acceptance criteria
  • Build a grounded, privacy-aware workflow with least-privilege tool use, human confirmation and accessible outputs
  • Design versioned evaluations, diagnose failures, compare releases and operate a controlled pilot with monitoring, rollback and incident response

Entry knowledge

  • Basic digital literacy and file handling
  • Ability to describe a work process and verify a source
  • Basic spreadsheet concepts

Scope boundaries

  • Uses fictional records only and does not authorize consequential decisions, autonomous external actions, sensitive-data processing, professional advice, surveillance, production deployment or claims that generated content is true
  • Does not replace qualified domain, security, privacy, legal, accessibility, records, procurement, safety, affected-stakeholder or accountable-owner review

Fastest competency route

Completion is self-paced and competency-based. Prior experience can shorten study only when every specification, evaluation, security, privacy, accessibility and revision criterion is demonstrated; no fixed duration, productivity gain, job, certification, compliance, vendor approval or official outcome is promised.

  1. Pass diagnostics on task selection, specification, grounding, verification, permissions, privacy, accessibility, evaluation and controlled deployment
  2. Complete the supplied synthetic workflow evaluation and risk-control portfolio
  3. Correct every injection, unsupported claim, secret, personal-data, accessibility, action, schema, citation, version and monitoring defect before final assessment

Full self-paced route

  1. Use-case selection, prompt and context specification
  2. Grounding, tools, security, privacy and human oversight
  3. Evaluation, controlled deployment and operations
  4. Applied portfolio and final assessment

Lesson and assessment map

01

Select the use case and specify grounded work

Turn a legitimate work need into a testable AI-assistance specification with evidence and stop conditions.

4 questions: recall, application, judgment, and error correction.

02

Tools, permissions, privacy, security, and human oversight

Design an AI-assisted workflow that contains data and action risk with least privilege and accessible human control.

4 questions: recall, application, judgment, and error correction.

03

Evaluation, controlled deployment, monitoring, and revision

Measure task performance and risk across versions, then run a reversible, observable pilot or make a no-deploy decision.

4 questions: recall, application, judgment, and error correction.

Required applied assignment

Governed AI workflow evaluation and pilot portfolio

Using only the supplied synthetic cases, design and evaluate one vendor-neutral AI-assisted workflow from use-case decision through a deploy, constrained-pilot or no-deploy recommendation. Do not connect external tools or represent the work as approved for production without qualified domain, security, privacy, legal, accessibility, procurement, affected-stakeholder and accountable-owner review.

  • Use-case/impact decision, non-AI baseline, task contract, source and evidence rules, input/output schema, uncertainty/refusal policy and manual fallback
  • Data-flow/threat model, data and permission registers, injection/secret defenses, output/action validation, confirmation, idempotency and accessible oversight design
  • Versioned evaluation set and rubric, reproducible runs, groundedness/action/privacy/accessibility metrics, critical defects, grader calibration and regression comparison
  • Release gates, shadow/limited-pilot plan, monitoring/incident/rollback/retirement controls, accessible decision memo, peer red-team response and substantive revision history

Passing standard: 85 of 100 points, with substantive revision required.

Continue after this course

  • Python, APIs, JSON Schema, retrieval systems and software testing
  • AI security, privacy engineering, threat modeling and incident response
  • Human-computer interaction, accessibility, evaluation science and domain-specific governance