• Ensure broad participation by non-profit organizations, including research partners in the design of standards, tools, and pilots. 

• Highlight the use-case of LERs for increasing equitable access to opportunity.

• Work with employers and education and training providers to build skills-based career paths.

• Develop holistic support services that help learners and earners to leverage LERs in order to advance their aspirations and goals.

• Conduct usability studies that track attitudes and adoption over time, and across different user groups.

• Develop competency-mapping efforts across HR, workforce development, and education technology organizations.

• Carry out outcomes tracking on effectiveness.

• Work across the career navigation spectrum to contribute a workforce development perspective to R&D, including the perspective of workers directly.

• Provide outreach and education for users, support learners and earners understanding of their credential data.

• Identify and implement effective tools and supports for learners and earners to discover and apply for jobs and opportunities.

• Provide information and support to learners and earners on how to take advantage of skills-based career paths.

• Offer wraparound career support and mentorship.

• Develop and provide oversight of key standards (e.g. interoperability, portability) 

• Map and harmonize data standards and create and align on standards for data infrastructure: integrity, ownership, protection and privacy.

• Encourage the use of open standards and open APIs (application programming interface).

• Close data standards gaps that hold back adoption and uptake.

• Provide test suites and other mechanisms for software vendors to demonstrate that their products adhere to specific standards.

• Identify and engage with the ecosystem of state/ federal governance that implements relevant policies. 

• Develop policies related to the use and adoption of LERs at various levels of government, and including data infrastructure questions related to integrity, ownership, protection and privacy.

• Expand capability of state systems to gather and link employment-, credential-, and skills-related data.

• Center equitable access to economic opportunity in governance processes and policies. 

• Engage with employers to clarify the benefits of LER systems across all stakeholders.  

• Advocate for change (towards skills-based hiring and adoption of solutions that enable more equitable access to opportunities).

• Develop sustainable business and governance models.

• Encourage and support related business and educational ‘verticals’ to develop governance guidelines that build relevant validation services.

• Create national and regional frameworks to recognize learning.

• Ensure universal access to LER solutions. 

• Work with education providers and issuers on qualifications frameworks.

• Explore policy-maker role in supporting verification processes and adding layers of trust in credential content and quality, e.g. by exploring ‘trust registries.’

• Support infrastructure development to ensure high-quality solutions are accessible and appropriate to all stakeholders.

• Champion data-driven insights on impact and outcomes.

• Incentivize adoption across the workforce.

• Track exchange and/or credential transfers.

• Ensure the use of interoperable standards for LERs. 

• Support usability studies to identify the features and services needed in various segments of the LER ecosystem and include a wide variety of user perspectives in the design of solutions. 

• Create shared resources for capacity building.

• Invest in understanding the expanded use-cases that are enabled by AI.

• Stimulate collaborations between employers and solution-builders to increase learner and earner confidence, and strengthen the link to employer utility, etc.

• Support implementation and demonstration efforts, including more digital literacy upskilling for learners and earners, etc.

• Document impact by funding data collection, research and analysis in order to better understand the efficacy, economic uplift, and other impact of LERs.

• Engage with research and pilots, highlighting challenges to adopting LERs in existing products, and finding ways to address them. 

• Focus on working with the data available.

• Support development of open data standards and their adoption.

• Build open standards-based APIs (application programming interface) to enable easier integration of HR systems. 

• Focus R&D on tools for direct engagement with individuals who are passive job seekers, active applicants, or existing employees who will receive issued LERs from their employers.

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• Advocate for issuing and using LERs in skills-based hiring and advancement.

• Support issuing employment records.

• Build support for LERs into hiring systems, and make it possible to match skills profiles to open positions.

• Streamline integration of LER software into existing ATS / HRIS programs.

• Process LER data in accordance with how applicants want their information to be shared, aggregated and reused (including looking at terms of service in LER/VC data model to support those preferences).

• Track data on effectiveness of LERs for applicants and employers.

• Contribute to development of recognized standards that enable interoperability of credentials across education providers and employers, credential and skills transparency, and individual data control.

• Work with policymakers and standard-setters on data infrastructure and expectations.

• Build platforms for issuing LERs that adopt recognized standards for interoperability.

• Expand capabilities to allow link learning and credential data with employment data.

• Embed skills data in the digital credentials where available.

• Gather feedback from learners, earners, and employers to continuously improve solutions.

• Develop managed or hosted issuing solutions for issuers who don’t require operating their own issuing service.

• Integrate with employer and education provider systems.

• Consider the interoperability use cases where education and employment data are used (e.g. background check firms, scholarship organizations, state common college applications, FAFSA, state licensure boards, apprenticeships, etc.).

• Partner in research and pilots — to better understand LER ROI for institutions and their learners, curriculum development systems, etc.

• Align curriculum and learning outcomes to learner and employer needs.

• Engage stakeholders across the education/training institution, including leadership, faculty, student affairs, registrars, continuing education, career counselors, etc.

• Ensure participation of a broad range of institutions, in particular including community colleges, in pilots.

• Issue digital versions of academic credentials.

• Explore a competency-based approach to designing curriculum, learning, and assessment. 

• Develop policies and practices to validate and document skills, use skills in credit for prior learning, admissions, and audits for program completion.

• Define governance and infrastructure to ensure quality assurance of issued credentials (influenced by industry standards, accreditation bodies, local employers and educators).

• Work with policymakers (including input from Commonwealth countries and EU) on qualifications frameworks for transparent learning progressions.

• Ensure that credential and skills data are issued and used in an interoperable manner.

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• Undertake change management including: training staff on LERs.

• Implement curriculum with skills and competency-based approaches to assessment for publishing to LER.

• Use performance-based, criterion referenced, authentic assessments to validate mastery of skills.

• Include support for LERs in  admissions systems and decision making processes.

• Adopt new practices to use LERs in the learning journey, including but not limited to, the curriculum, advising, scholarships and career services.

• Expand prior learning recognition to offer credits for  experiences in other settings and institutions.

• Track data on effectiveness for institutions and learners — including competencies, skills and their associated rubrics within the curriculum.

• Develop 'evidence' supporting skill/competency acquisition that has predictive validity.

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• Partner in research and pilots — on data collection, alignment of skills to business needs, systems integration, etc.

• Drive employer adoption of digital credentials and skills data.

• Advocate to HR software providers to explore which standards they comply with and communicate their desire to explore LERs.

• Make a commitment to skills-based hiring and advancement, which requires discussion about what evidence the organization values and how LERs are interpreted by those making hiring decisions. 

• Adopt a skills-based approach to training, talent development, and retention.

• Adopt or map to skills frameworks.

• Issue credentials / LERs to document employees’ skills development, achievements, and employment experience. 

• Train supervisors on how to utilize LER tools to issue LERs for incumbent workers and those participating in work-based learning opportunities.

• Train hiring managers and supervisors on how to use LERs and skills-based approaches.

• Work with internal teams to develop and standardize skills-based job descriptions and postings and to validate skill requirements through job analysis (and leverage emerging tools that support this process).

• Work with education, training, and credentialing partners to integrate LERs into talent pipelines.

• Use LERs in skills-based hiring and advancement.

• Adopt new HR systems, HR processes and hiring behaviors to use LERs for employment opportunities. 

• Work with HR vendors to adopt shared standards so HR systems can process resumes as machine actionable LER data when assessing qualifications for a job.

• Track metrics regarding successful placement and data on effectiveness for company and users.

• Consult in research and pilot efforts — providing critical user perspective.

• Contribute experience and feedback to R&D to ensure continuous user-centered design.

• Ask their institutions to issue credentials as verifiable credentials that can be incorporated into LERs.

• Learn more about skills-based approaches to learning and career advancement and how LERs might support the shift.

• Curate and share skills.

• Discover career opportunities using LERs.

• Determine eligibility for benefits and other applications.

• Apply for jobs and continuing education opportunities.

• Submit LERs as part of  application processes for jobs and education opportunities.

• Receive a job or learning offer based on LERs.

• Take part in data tracking studies to share longitudinal impact stories.