Before the AI takes our jobs, can it help us learn?

Jeff Dieffenbach


AI in the Service of Learners and Learning

Mired in a problem with no obvious path toward a solution? Fear not, the conventional wisdom says, some combination of blockchain, the Internet of Things, and/or AI will bail you out.

Well, if that problem is learning, or more to the point, hurdles that block learning, AI may indeed help you navigate your way to better understanding. How might this work?

Right up front, let’s tackle the question of privacy that’s inevitably going to arise. To make the ideas that follow a reality, a hypothetical “learnerAI” is going to need to know about you. A LOT about you. For the purpose of this exercise, then, let’s stipulate that you, the learner, have complete awareness and control of your learnerAI. It’s there to HELP you, NOT to report ON you.

To aide your learning, what might your learnerAI need to know about you?

  • Your basic physiological information via biosensors
    • Heart rate, pulse, pupil dilation, galvanic skin response, attention, …
  • Your interests via direct survey
    • Technical topics, operational topics, financial topics, leadership topics, …
  • Your current work via job description and email, text, phone, and web monitoring
    • Roles, projects, tasks, …
  • Your career history via resumes, LinkedIn
    • Roles, projects, tasks, …
  • Your learning history via direct survey, calendars, stored credentials, and more
    • Degrees, programs, courses, conferences, books, articles, webinars, …
  • Your desired career future via direct survey
    • Roles, …
  • Your desired learning future via direct survey
    • Competencies, skills, …

Yes, your learnerAI needs to know a LOT about you.

With that information (and more, no doubt) in place, your learnerAI will be in a position to help you overcome problems at learner, instruction, and/or policy levels (a framework developed by the MIT Integrated Learning Initiative). We’ll address solutions at each of these levels in turn (in blue).


If a learner has the right prior knowledge, motivation, interest, and physiological readiness, he or she will be in a good position to learn. The learner isn’t always in the best position to judge the state of these conditions, however.

The learnerAI can help.

  • Prior knowledge: the learnerAI will use information about the learner’s prior learning experiences to make appropriate matches with content based on the learnerAI’s assessment of the level of that content.
  • Motivation: as used here, motivation reflects external reasons for learning—to earn a credential, receive a promotion, or otherwise further a career. The learnerAI will provide motivating context to the learner by mapping the content of a learning experience to current and future roles of importance to the learner.
  • Interest: in comparison with motivation, interest is intrinsic–typically, a learner either does or does not like a topic, although interest may certainly increase with familiarity and expertise. The learnerAI will draw on learner-expressed interests to match with the content in a learning experience when skill or process (and not content) is the primary learning objective.
  • Physiological readiness: The learner needs to be well-rested, well-fed, otherwise in good physical shape, and as important, in a good mental state. The learnerAI will detect these conditions and align learning experiences with the learner’s physiological receptiveness.


If the instruction has the right content, delivery, and assessment, the odds of effective learning improve.

The learnerAI can help.

  • Content: Content includes the breadth, depth, and accuracy of the subject matter and the production value with which the subject matter is configured. Content may cover knowledge and/or skills. The learnerAI, knowing a lot about what’s relevant to the learner, will uncover content addressing one of three needs: the learner knows that content is needed; the learner’s manager, peer, or subordinate knows that content is needed; and most important, none of the aforementioned parties knows that content is needed.
  • Delivery: Delivery variables include human vs. digital, synchronous vs. asynchronous, duration of learning, user-requested vs. pushed-to-user, and device through which the learning is consumed. The learnerAI will look at past successes and current needs to help deliver content when it’s most valuable. That timing might be in advance of a learning need (“learning before performing”) or in just-in-time fashion at the time of need (“learning while performing”).
  • Assessment: Assessment has to do with the frequency and depth with which the learner is assessed and the formative manner in which the responses are used to guide the next piece of content and delivery. A robust learnerAI will add fidelity and precision to the adaptive-branching inherent in effective “What’s next?” learning. Moreover, the learnerAI will track and record learning, both formal and informal based on the learner’s actions, calendar, and other input.


If the policy has the right law, access, funding, leadership, and measurement, the prospects for effective learning are better still. The learnerAI can help.

  • Law, access, and funding: Laws and regulations must be conducive to learning. Learners must have access to learning. This isn’t access via funding, but rather, access via circumstance. For instance, a job role that isn’t eligible for a particular type of training or isn’t in the right location for that training isn’t helped regardless of how good the training is. Finally, funds have to be available to pay for the learning experience. These funds may be provided by the learner or the provider of instruction. These three areas are included for completeness, but don’t really lend themselves to improvement via AI.
  • Leadership: Leaders of those providing instruction must have a philosophy conducive to/supportive of learning. Here, the learner AI WILL help–it can be tuned, and tune itself, to learning opportunities (content and delivery) in line with executive mission statements and vision and management implementations of those missions and visions.
  • Measurement: Measurement as used here is different from the formative level of assessment guiding the learner’s next instruction. Rather, it addresses the summative level of what learners come to know, how their behaviors change, and what organizational improvements those behavior changes drive. The learnerAI, through its tracking and reporting, will assist learners in demonstrating the value to their employer of the learning they undergo, thereby establishing a case to be made for future learning.


Learners will equip themselves with a learnerAI guide. Ideally, the learner will own this guide the way they own a LinkedIn account–it will not be provided by … and therefore accessible to … the learner’s employer. While learners may still feel pressure to share learnerAI data with employers and especially prospective employers, the learner ownership will mitigate the intrusiveness.

The learner will own, control, and tune the learnerAI to monitor physiology, job descriptions, to-do lists, communications, actions, and more. As a result, the learnerAI will assist with desired, assigned, and unanticipated learning. This learning will be delivered optimally: when the learner is ready to learn and/or when the learning is of the most value. In the background, the learnerAI will record learning for the learner to share when that sharing best benefits the learner.