Institute For The Future

Signaling Work and Learning Readiness in 2030: The Future of Assessment

A Summary of the Colloquium

On February 19-20, Institute for the Future partnered with Lumina Foundation to assemble a colloquium of educators, entrepreneurs, and philanthropic leaders to re-envision the ways that learners in the future will signal their skills and competencies for work—and the ways that the marketplace will signal its changing needs for those skills and competencies. The discussion focused on five zones of change and innovation, which are outlined below.

You can download a conceptual infographic representing our findings, here. 

Changing Labor Economy

IFTF presented a forecast for the future of work that challenges us to think beyond present-day debates about automation and AI—beyond questions of teacher pay and the burden of debt for learners—to understand the fundamental shifts taking place in the workplace. This shift can be described as a transition from a marketplace of steady blue collar, white collar, and professional jobs to one where micro-tasks increasingly replace long-term employment. In this labor market, machine coordination via platforms like UpWork and Uber take over many traditional management tasks, and long-standing institutions are disrupted by digital platforms that focus on building the efficiency of a small staff rather than hiring, training, and managing a large full-time staff.

New Generation of Learners and Workers

Already this shift is giving rise to new kinds of learners and workers. IFTF shared its recent research to understand the skills, competencies, and aspirations of this emerging workforce. The research interviewed 60 lead learners from 6 global cities to understand the emerging learning and working patterns of the next generation. (These lead learners are comparable to lead users of new technologies: they are the first signals of trends that may quickly be picked up by early adopters whose practices then spread to the mainstream.) This study of Global Youth Skills sets the stage for re-envisioning a new infrastructure for signaling skills—as a learner/worker seeking employment or employer seeking particular kinds of talent.

Paths to Achieving Individual Potential

While the good jobs of the past century provided a target for talent development, they also created many of the inequalities we see today. They rank people based on standardized assessments that fill the slots in hierarchical organizational charts of traditional institutions. Increasingly, in the digital coordination economy, we have the tools to match individuals more effectively—and equitably—with work+learn opportunities that will lead individuals to strong lifelong paths. 

Over the next decade, new kinds of services will emerge to assess, document, and validate skills and talent in this new work marketplace. The colloquium explored four scenarios for these new services:

Gaming the future: The games of tomorrow, together with emerging brain science, will build competencies and assess matches to work tasks. Some work tasks will actually be embedded in games, and earnings will grow as “players” level up their skills in the game environment.



Recommending paths to the future: Netflix-like playlists of courses and tasks will suggest personalized work+learn pathways and signal knowledge and skills of users via their profiles. AI-driven analysis of playlists and profiles will help uncover the best paths for an individual, based on the patterns of the crowd.



Social learning: Social media will help learners and workers find mentors and build their reputations with “fans and followers.” Fostering a fan base will become a strategy not only for building a reputation in everything from crafts to coding, but also creating an income stream in learning pyramids where everyone is a both a mentor and learner.



Graphing our work+learn networks, with so-called graph IDs that assess the value of learning in networks. Using a combination of network analysis and blockchain-style tokens that attest to learning “transactions,” these graph IDs will offer a profile of all the past work+learn connections for an individual and project potential for growth based on as-yet untapped resources in the larger network.

Building a New Signaling Infrastructure

These shifts in the way we assess work readiness will require a new a signaling infrastructure, with new assessment tools, new institutional forms, and new kinds of standards. Here is what the new infrastructure could look like:

• Tools for recording, measuring, and validating learn+work readiness
  Work+learn experiences would be continuously updated. They would be captured by everything from video attestations by teachers and immutable blockchain transactions to algorithmic analysis of immersive learning in augmented and virtual reality simulations. They could be captured with a browser plug-in that also enables users to shape their own work+learn stories from all this data. Learners in this future environment would also need trusted tools to help secure their data for their personal use—and possibly for profit. The flip side of this personal signaling toolset could be tools for visualizing the larger work+learn context, such as dynamic maps of changing labor markets and their skill requirements. Learners could use these maps to plot their personal paths, again with the assistance of algorithmic analysis.

• New institutional forms for signaling across a global labor economy
  The emerging marketplace for work is already driving a shift from traditional institutions to a platform-based economy, where platforms continuously innovate services using so-called application programming interfaces (APIs). APIs provide “handles” that allow people or services to ask for something—whether it’s a data point about an individual or a stream of digital actions in a game. These APIs are new gatekeepers, creating the new rules and rights of way in the networked world.
  
  Over the next decade, the global work+learn economy might be expected to transition from traditional gatekeepers (who have used standardized tests, curricula, and credentials to manage the flows of talent) to API-driven services that create different kinds of organizations. Already, we see companies like Uber, AirBnB, and even Amazon providing massive services at scale with many fewer employees. While significantly cutting full-time jobs, these platform-based organizations often redeploy the talent from traditional organizations. So we might expect to see today’s university professors engaged in micro- assessments via diverse platforms. Crypto currencies could be used to measure and reward contributions in open source learning environments. Think Gitcoin, for example, and its formula for connecting developers to online jobs, based on their contributions to the Github community of programmers.
  
  Even traditional human-to-human services like counseling, coaching, and curating work+learn experiences will use digital platforms and their analytical capacities to provide a more human touch in individual, personalized services. Some will likely gain “trusted status” by emphasizing local community connections and face-to-face interactions in local spaces, with shared civic objectives. Others will use digital profiles to find the best teaching and counseling matches.

• Standards for an era of global micro-learning and micro-working
  Just as the digital coordination economy calls on us to rethink our traditional institutions, the new global labor economy seems to demand that we rethink what we mean by standards—and how we use them in a volatile labor market where requisite skills are rapidly changing to meet often unprecedented social, economic, environmental, and political challenges.
  
  The emerging digital coordination economy is nothing if not a standards-based economy. The tools and protocols that allow digital coordination across organizations, platforms, and geographies all build on foundational standards that assure interoperability. The same will likely be true of standards for assessing and validating work-readiness, but these standards will focus less on specific content (such as recommended reading level) and more on procedures for identifying learning (such as patterns of leveling up in a game or the growth of one’s graph ID on the blockchain). At the very least, taxonomies of skills for work tasks need to be created and updated from the real-world labor data as the digital coordination economy evolves.

A Decade of Innovation

For the past century, we have thought of learning—both education and training—as a way to build an efficient workforce while affording learners the opportunity to grow their earning power through learning. Now we’re shifting to a new kind of workforce focused less on predefined job categories and skill requirements and more on tapping the unique potential of billions of worker-learners for a rapidly evolving labor landscape. The next decade will not only challenge us to reinvent learning for this new kind of distributed, dynamic, and ultimately more creative workforce. It will also inspire us to re-envision the tools, practices, and standards of assessment for the infinity of pathways that tomorrow’s learners and workers will pioneer to create their uniquely meaningful lives.