Professional standards for mapping learning to SFIA

Purpose and value

These guidelines set out professional standards for mapping learning and certifications to SFIA. They promote consistency, accuracy, and clarity in how learning is described. Clear, evidence-based mappings help learners, providers, and employers by showing the relationship to SFIA skills without overstating outcomes. They also protect the integrity of SFIA as a trusted community framework, while enabling organisations to use AI responsibly with professional oversight.

Mapping learning and certifications to SFIA is valuable because it:

• uses a trusted, globally adopted framework rather than ad-hoc or unverified skills lists
• provides a common language that supports learners, organisations, and the wider community
• helps employers and professionals make better-informed decisions about learning value and career development
• maintains the integrity of SFIA as a shared resource available to all

There is understandable temptation to automate mapping with AI to save time and cost. Used responsibly, AI can help, but outputs should always be checked and validated by experienced people to ensure accuracy and build trust. HR applications of AI are generally seen as high risk and require strong guardrails. While mapping learning to SFIA may be lower risk, it should still be guided by the same principles of care and oversight.

Governance

• Hold an appropriate SFIA licence and/or contact the  SFIA Foundation before starting mapping
• Use suitably experienced professionals to perform and/or review mappings, with:
  • SFIA knowledge and experience (trained, Accredited, passed the SFIA Foundation test)
  • subject matter knowledge and experience related to the learning/courses being mapped
  • learning and development knowledge and experience, including course design, intended outcomes, and how these are described
• Apply the same professional standards to automated or semi-automated approaches (including generative AI and LLMs)

Fit for purpose course descriptions

Before mapping to SFIA, ensure the course or learning solution is fully described. Essential elements include:

• Target audience: roles, goals, and prerequisite knowledge/experience
• Learning outcomes: clear statements such as “"By the end of this class/module/course, you will be able to: [list of target capabilities]”.
• Topics covered: scope and depth of the learning, including practice elements if relevant

Integrity and accuracy

• Use only verified SFIA content from official SFIA Foundation sources
• Validate all skill mappings against official framework documentation
• Respect SFIA’s design principles
• Base decisions on evidence, not assumptions or inferred content
• Ensure assigned skills and levels reflect demonstrable evidence
• Use the guidelines for mapping learning products to SFIA

Use of GenAI and LLMs

• Remember - LLMs (large language models) do not automatically access official SFIA materials.
• Without grounding in verified sources, their outputs may be incomplete or inaccurate.
• Some organisations build systems that combine LLMs with verified SFIA content. These can be valuable when designed and reviewed by experienced professionals.
• Always use official SFIA content from the SFIA Foundation as the basis for mappings.
• AI tools can save time and can support analysis, but professional review and validation remain essential.

Explainability

• Mappings presented as facts without explanation can reduce clarity and confidence for the consumer
• Good practice is to document and share the reasoning behind mapping, not just the outcome.
• Record:
  • the SFIA version used and date of analysis
  • the evidence and sources that support the mapping
  • the rationale for assigning a skill and level
• Providing at least a summary of this information helps consumers understand the basis of the mapping.
• Use clear language and avoid assuming familiarity with SFIA terminology.

Maintenance

• Periodically validate outputs against SFIA standards and/or updated learning outcomes E.g., for updated versions of SFIA, or significant changes in mapping guidelines.
• Incorporate feedback and corrections into improvements.

Publicising, marketing and advertising your mapping.

• Be thoughtful in how you describe the outcomes of mapped learning.
• Be clear when outcomes are knowledge-focused. Most mapped learning will be 'knowledge only', which is valuable in itself, but distinct from professional skill or competency.
• Exercise caution in assertions of skill or competency.
• Distinguish between what learners can demonstrate in a training context and what can only be applied professionally in the workplace.
• Highlight any prerequisite workplace experience needed for true skill or competency development.

Indicative examples of valid and invalid use of mappings

These are examples, not an exhaustive list. In the spirit of professional standards - you should use these to guide you in making appropriate assertions.

Appropriate assertions

✓ "Provides knowledge foundation for [SFIA Skill]"

✓ "Develops understanding of [SFIA Skill] principles"

✓ "Supports career development toward [SFIA Skill] at [Level X]

✓ "Prepares learners for practical application of [SFIA Skill]"

Assertions requiring in-training demonstration

⚠ "Develops skill in [SFIA Skill]" 

⚠ "Achieves [SFIA Skill] at [SFIA Level]"

⚠ "Achieves [SFIA attribute/business skill] at a [SFIA Level]

Assertions that require extensive demonstration in the workplace

⚠ "Develops [SFIA Skill] competency at [SFIA level]" 

⚠ "Achieves [SFIA Skill] competency at [SFIA Level]" 

Invalid assertions when used alongside learning mapped to SFIA skills

❌ "Guarantees [SFIA Level] capability"

❌ "Equivalent to work experience"

❌ "Professional competency certification"

❌ "Job-ready skills" (where there is substantial practical component)