For more than 100 years, formal education has followed the same model. Everyone participates for the same length of time, and because some people learn better (or are more capable) than others, learning outcomes vary. From kindergarten to college, the story is the same: Some people are “A” students, while others fail. Some grasp the material within a fixed time allotment and achieve competency, while others, to varying degrees, do not.
If building competence is the objective, particularly in corporate learning and development (L&D), does it make sense to have one group of learners who “gets” the material and another group who does not — with a bell curve of variations in competence in between? (Not to mention, most organizations assume that everyone is proficient at the end of a program, despite what assessment results and cognitive research tell us.)
Adaptive Learning in the 21st Century
Adaptive learning, with its personalized approach, changes the picture, starting with the concept of a performance distribution. Instead of passing or failing, learners are viewed as either proficient or incomplete in their skill-building and in need of additional development. Artificial intelligence (AI)-enabled adaptive learning embraces the assumption that, with enough time and the right support, virtually anyone can be proficient. In this model, performance is fixed, and the variable is time.
If our goal is to enable all workers to be successful in the 21st-century workplace, we need to fundamentally rethink the constraints we impose on learning. Adaptive learning programs have demonstrated that faster learners achieve proficiency twice as quickly as learners at the median, and learners who need the most help are roughly twice as slow as those at the median. With adaptive learning, time to complete will vary from learner to learner, but proficiency is achieved by all.
Personalized Learning Levels the Playing Field
With adaptive learning and its personalized approach, the result is a more level playing field in which all workers can reach their full potential. Most current L&D models, however, are not set up to realize universal proficiency, because they do not support learners with personalized assistance. When L&D programs do use a self-paced online learning approach that allows learners to work at their own speed, they fail to provide help to the ones who struggle. Without personalized support, providing ample learning time does not meaningfully close the skills gap.
In theory, an instructor in a classroom provides that personalized support, but from a practical standpoint, personalization in a group setting is usually compromised because of class size and the volume of content. The unfortunate truth about the current training approach is that the major objective, in the classroom or online, is getting through the content rather than ensuring that every learner achieves proficiency and mastery.
This traditional approach will not suffice given today’s urgent need to reskill workers. According to McKinsey research, as many as 375 million workers, or about 14% of the global workforce, may need to change occupations (requiring new skills) due to digitization, automation and AI. Moreover, as “intelligent machines” become increasingly common in the workplace, the way nearly everyone works could change.
Retraining not only helps workers, but it is also beneficial for employers that have previously relied on hiring new people who have the knowledge and specialized skills to be productive from day one. Now, a pervasive skills shortage is making it more advantageous and cost-effective for organizations to retrain their current workers. According to a recent Bloomberg editorial, more than seven million job openings remain unfilled in the U.S., a shortfall that the writers say we cannot attribute solely to a tight labor market. As the editorial notes, one in five employers told Manpower Group researchers that applicants lack the necessary skills for open jobs — not just in digital technology but also in communication and problem-solving.
Adaptive learning is uniquely suited to training in both the technical “hard” skills required to perform a job as well as “soft” skills, such as communication, critical thinking, collaboration and creativity. By taking a personalized approach, adaptive learning systems adjust to each learner (not the other way around). Adaptive learning focuses on where each person needs to become competent. This approach is not only more engaging for the learner, but it is also more efficient and cost-effective for the organization. For corporate L&D, that efficiency translates to the ability to enable workers to increase their skills and achieve proficiency with less time off the job.
Most important for workers facing a significant job change, personalized learning improves median performance and reduces performance variation. In other words, everyone has a greater opportunity to become proficient in a new role, increasing everyone’s chance of success with a wider range of job opportunities.
To meet the demands of the 21st-century workplace, competence must be achieved by everyone — not just some. As machines make greater inroads, taking over tasks in most jobs and eliminating some jobs completely, we must equip workers across the board with higher-level skills. It’s a tall order but completely within our grasp, thanks to adaptive learning’s ability to deliver a personalized approach at scale.