1 of 34

C3 Webinar Series�Connecting Competency Communities

Advancing Skills-Based Hiring with Data Standards & AI �September 20, 2023

2 of 34

Welcome!

The C3 Webinar Series aims to improve communications between those involved with creating, maintaining, discovering, distributing, and using competency data.

The C3 monthly webinar connects interested persons to experts and futurists building the systems, processes, and technologies to ease the burden of creating, maintaining, discovering, distributing, and using competencies.

Eric Shepherd �President at

Foundation for Talent

Jeanne Kitchens

Chief Technology Services Officer at Credential Engine�Linkedin

3 of 34

Elliot Robson, CEO with Eduworks�(LinkedIn)

Jim Ireland, Executive Director at HR Open Standards �(LinkedIn)

Elliot Robson is a leader in human performance and human machine interaction. He is currently the CEO at Eduworks Corporation where he has led transformative projects in AI and the Future of Work for the National Science Foundation, skill digitization with Eduworks TIDES product, human performance data reuse with the US DoD-backed Competency and Skills System, and more. He is a respected leader in advanced technologies and AI applied to human performance and training.��He began his career at the NYC Department of Education and Wireless Generation (now Amplify Learning) where he was a founding member of the research and analytics department. He holds a Masters in Public Policy from the Ford School at the University of Michigan and a BS in Urban Design and Linguistic Anthropology from Yale University.

Jim Ireland is the Executive Director of HR Open Standards Consortium, the non-profit organization dedicated to developing and promoting open standards for the HR industry. He has over 25 years of experience leading and building non-profits, and a passion for using technology to improve the efficiency and effectiveness of HR processes. He is also co-chair of the T3 Innovation Network’s Jobs and Workforce Data Network.

At HR Open, Jim oversees the organization's strategic planning, financial management, and operations. He works with the HR Open Board of Directors and world-wide member community to advance the organization's mission of developing and promoting standards for HR.

Before joining HR Open, Jim served as Vice President of Member Engagement and Digital Strategy at the Allegheny County Medical Society. In this role, he led the development and implementation of data-driven strategies.

4 of 34

Learning and Employment Record Resume Standard (LER-RS) Overview

September 20, 2023

5 of 34

Key Terms

VCs - Verifiable Credentials (standard exists, W3C)
LERs - Learning and Employment Records (concept)
CLRs - Comprehensive Learner Record (standard exists, 1EdTech)
OBs - Open Badges (standard exists, 1EdTech)
ATS - Applicant Tracking System (business software)

6 of 34

Skills-based Hiring Ecosystems

HROpen

Position Opening

HROpen/JDX

Job Posting

Schema.org

1EdTech

CLR 2.0 / OB 3.0

HROpen

LER Resume Standard

Employers

Candidate / Person

W3C Verifiable Credential

Skills / Competencies

Demand Side

Supply Side

7 of 34

Demand Side: HR Systems

Typically Applicant Tracking System (ATS)
Where the Recruiters and Hiring Manager create work offer, review candidates who apply, select candidate to hire
Challenges

Limited support for Verifiable Credentials
Limited support for skills-based hiring today

Work Offer (Position Opening, JDX Job Posting)

Candidates

(Candidate, LER-RS)

8 of 34

Demand Side: Work Offer (Job Posting)

Today: Represented as a job posting or a position opening, can be full or part time, permanent, contract, etc.
Typically generated in an Applicant Tracking System (ATS), becomes advertisement on Company Career site, may be found on Job Boards or via search engines (i.e. Google, Bing)
Challenges

Work offers are not defined or stored as skills-based or structured data. Supporting systems often cannot support highly structured data
Many work offers are not easily machine readable, unstructured or ambiguous requirements

HROpen

Position Opening

HROpen/JDX

Job Posting

Schema.org

9 of 34

Supply Side: Digital Wallets

Verifiable Credentials (i.e. CLRs, OpenBadges, etc)

Resume/Application

LER Resume (LER-RS)

Typically a wallet app on a person’s phone or a web-based app
Person has control over collection of data (i.e. CLR, Open Badges, other VC) from providers (i.e. education institution, background screener, assessment provider) into their wallet
Challenges

Learning and Career Wallets have limited adoption
Very few issued VCs related to learning and employment for people to curate into their wallet
Individual understanding of wallet and LERs is lacking

10 of 34

Supply Side: LERs

Learning and Employment Records - a framing concept in early stages of adoption and understanding
Closest analogy

Education: CLR issued by an education institution for a formal education degree program
Employment: proof of employment letter for employment insurance or proof of employment (not a VC)

Challenges

LERs are concepts not often used today
Often not fully aligned to skills taxonomies to support skills-based hiring

11 of 34

Intersection: Resume/Profile

Typically created by a candidate, today often in Microsoft Word or PDF (file attachment)
May be generated on social profile platform (i.e. LinkedIn) or on a Job Board (i.e. Indeed)
Often Submitted to ATS as part of job application

Challenges

Inconsistent formats
Often not focused on skills-based hiring, not structured
Often not trustworthy or verifiable�

Solution: LER Resume Standard (LER-RS)

12 of 34

Challenges with Today’s Resume

Today’s Resume is typically electronic MSWord/PDF document, but:

It's not easily machine readable, inconsistent formats and data
Not trusted, information can’t easily be verified
Lack of connections with trusted data, including learning records and verifiable credentials

13 of 34

A new Learning and Employment Record Resume Standard (LER-RS) to enhance the traditional resume and used in hiring and advancement.

Introducing - LER Resume Standard (LER-RS)

14 of 34

Verifiable, Machine-readable, promotes Trust
Self-Sovereign, Selective Disclosure
Ease of Integration
Consensus-based open standard, royalty-free and open to use
Progressive Adoption with HR Tech and employers, allow for evolution of solutions
Support Consumer Usability, Portability

LER Resume Standard - Why Use?

The mission of the LER-RS Resume Standard is to enable people to easily exchange their trusted, structured, curated, and linked information with organizations to further their career goals and aspirations.

To support this mission, the following are key goals of this LER-RS Resume Standard:

Trust - LER-RS allows for exchange of machine-readable verifiable digital proof of the data, which may be self-assessed or produced by a third-party
Self-Sovereign - LER-RS supports a person's right and ability to control the identity of self
Disclosure - LER-RS supports a person's choice of selective disclosure and curation of their data to third-party organizations
Ease of Integration - LER-RS allows for organizations and systems to easily integrate and use the machine-readable data
Open Standard - LER-RS will be royalty-free and available to be used by any entity
Evolution - LER-RS allows for progressive adoption of capabilities of the standard, allowing different HR systems to produce and ingest more and more structured data and verifiable data over time.
Portable - LER-RS allows compatibility with digital wallet design for control by the person and can be made portable through those applications. Allows for curation of data associated with their person. Movable, accepted, under control of individual.
Consumer Usability - LER-RS makes it easy for a person to collect, maintain, and disclose their Resume with organizations

15 of 34

LER-Resume Standard - Progressive Adoption

LER-RS designed to allow for progressive adoption towards the ideal state of skills-based hiring

Allow existing HR Vendors to adopt in existing process “as is”
Allow for adoption of VCs and credentials as suppliers begin to adopt�

HR Open Recruiting Workgroup plans to work closely with adopters to evolve standard to better support

Wallet Vendors and Data Disclosure
Data privacy policy adoption

16 of 34

Contact:

Jim Ireland, Executive Director

jim@hropenstandards.org

Learn More:

https://www.hropenstandards.org/standards-downloads

LER Resume Standard - Learn More

17 of 34

Q&A + Discussion

18 of 34

Emerging AI for a human-first job ecosystem

Eduworks

19 of 34

The primary objective of the job ecosystem is to bring together organizational needs and individual ability.

To succeed, we have to communicate about ourselves and our world.

Human communication has intent and purpose.

It’s nuanced, personal, and directed.

�

This often leads to confusion, miscommunication, and mistrust �…especially when jobs are on the line.

20 of 34

Historical solutions meet these needs to differing degrees.

- Guild systems - Personal networks

- Resumes - Certifications, Transcripts, & Credentials

- Audition/Direct Evidence

An effective job ecosystem aligns employers and employees.

- describes the individual - aligns expectations

- provides trust - is scalable

- is fair & evidence based

21 of 34

Digital systems can improve the talent ecosystem.

Usually address scale, trust, evidence.

“LERs … have the potential to improve education and hiring outcomes. What makes LERs unique is their ability to be fully transferable and recognized across student information, learning management, employer HR, and military systems.” – T3 Innovation Network

“Credentials provide consistent information and fuel the creation of resources that empower individuals to find the best pathways.” – Credential Engine

“DIGITAL KSAs enable the creation, discovery, management, and sharing from multiple sources, via custom development, and from job posting tracking tools.” – Eduworks

22 of 34

Standards enhance our ability to align expectations.

“data model for describing, referencing, and sharing competency definitions, and frameworks of competency definitions and rubrics in the context of online and distributed learning and in employment and work.” – IEEE 1484.20.3

“machine-readable, searchable data that include the context behind a skill, giving users a common definition for a particular skill and help to make it understandable and transferable across the learning-earning landscape.” – OSN

“captures data in a consistent format about a person or group’s activities from many technologies. Very different systems are able to securely communicate by capturing and sharing this stream of activities using xAPI’s simple vocabulary.” – xAPI

23 of 34

Where are we?

How can we express ourselves in a standardized and digitized world?

24 of 34

Emerging human-first technology lets us talk about ourselves.

Breakthroughs leveraging large language models, standards, and digital systems allow people and organizations to communicate their lived experience and local needs in modern job ecosystems.

25 of 34

“There was a shooting in my neighborhood, and I was like. This is not how I pictured my life. I can’t do this. I just kinda took the reins on my life instead of getting overwhelmed by my surroundings. Then I gave myself a booking link, and um, that’s how my business really started.

I have a lot of ideas. I just need help making them happen. I just need a mentor, maybe. An advisor or something. A console – a consultant. Something.” - Working

26 of 34

How does it work?

LLM layers translate human speech into machine actions on datasets.
AI Alignment links relevant data from the job seeker and the employer.
Translation layers allow users, both job seekers and employers, to have conversations with underlying data.

This advances the state of the art in job ecosystems. It puts our communication first, allowing technology to work behind the scenes.

27 of 34

AI Translation for job ecosystems

Language models are often grouped into the category of generative AI. However, this label overshadows their ability interpret human concepts.�
AI translation layers are capable of linking content together, while maintaining the context and intent behind them. �
This enhances existing competency-based communication. It allows organizations to use their natural language without losing the ability to align outcomes.

28 of 34

LLM

Translation

Learning Objectives

Training Data

Competencies

Syllabus

Content

Outlines

Training goals

Degree requirements

Career Paths

Organizational Goals

Industry relationships

…

Job Descriptions

Job Data

Skills Frameworks

Requirements

Regulations

Job Openings

…

LLM

Standards-based AI Alignment

Translation

Needs

Career Paths

Organizational Constraints

Desires

Perspectives

…

Educational Inst.

Employer

29 of 34

LLM

Translation

Documentation

Human Data

Resumes

LERs

Badges

Credentials

Stories

Experiences

Life Constraints

Desires

Perspectives

…

Job Descriptions

Job Data

Skills Frameworks

Requirements

Regulations

Job Openings

…

LLM

Standards-based AI Alignment

Translation

Needs

Career Paths

Organizational Constraints

Desires

Perspectives

…

Job Seeker

Employer

30 of 34

Existing standards-based alignment services

KSA Extraction: Extracts KSAs and relationships in unstructured text.

KSA Generation: Prioritized list of most relevant KSAs for a job title or description.

Alignment: Scores alignment between KSAs, job descriptions, and course materials.

Partners

31 of 34

Human-first technology enhances our productivity, improves our quality of life, and unlocks our potential.

Elliot Robson, CEO

Elliot.Robson@eduworks.com

Learn More

32 of 34

Q&A + Discussion

33 of 34

Next C3 Webinar: October 18, 2023��

We encourage reuse and remix of this resource with attribution

34 of 34

Resources

See complete schedule https://wiki.t3networkhub.org/wiki/C3_Webinar_Series
See the archived webinars https://wiki.t3networkhub.org/wiki/C3_Webinar_Series#Archived_Webinars
Register for the C3 Webinar Series https://us06web.zoom.us/meeting/register/tZYvduyqrDMiEtN6kugAWIv7By3eTn7Im-4q
Join the OC Network and AOC Workgroup https://www.t3networkhub.org/
Join LinkedIn Group: https://www.linkedin.com/groups/13926066/

Invite others to join these conversations!

We encourage reuse and remix of this resource with attribution