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

Jeff Dieffenbach

human-ai-brain

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).

Learner

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.

Instruction

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.

Policy

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.

SUMMARY

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.

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Panel: What makes learning effective?

MIT-Brain-Rhythms_0

I had the honor of participating in two panel discussions at the Reimagine Education conference in Philadelphia on December 4 and 5. This panel’s central question: What makes learning effective? In preparing for the panel, I put together these notes.

Introduction

  • Jeff Dieffenbach, lead staff director of the MIT Integrated Learning Initiative
  • Our mission is to fund, connect, and disseminate learning effectiveness research
  • Our scope is birth to pK-12, higher education, and workplace learning
  • (The research addresses learning effectiveness questions at the learner, instruction, and policy levels.)
  • (My background includes 10 years as a school board member and 15 years in educational publishing strategy, sales, marketing, and product management.)

Lots of things make learning effective

  • We frame those things in terms of a learner-instruction-policy “triangle”
    • Learner: prior knowledge, motivation, interest, physiological readiness
    • Instruction: content, delivery, assessment
    • Policy: law, access, funding, management, measurement

We know things about attention span, spacing and interleaving, value of expressive vs. receptive, learning with peers, and much more.

Echoing a question from the Jack Lynch session Monday morning, while there’s an incredible amount that we don’t know about what makes for effective learning, there’s a lot that we DO know but don’t apply.

What we know about learning

Learning revolves around the movement of information from sensory receptors to short term memory to long term memory back to short term memory and out to our various ways of expressing ourselves.

What do we know?

  • We forget things … and we can minimize that forgetting by taking advantage of reinforcement over time
  • Long blocks of monolithic learning don’t work … so we can shorten the blocks and interleave subjects
  • Pre-testing improves outcomes
  • Interspersed testing/retrieval learning improves outcomes
  • Peer learning improves outcomes
  • Worked examples improve outcomes
  • Grit is real and improves outcomes
  • Growth mindset is real and improves outcomes
  • Preferred “learning styles” (visual, auditory, kinesthetic) don’t translate to better outcomes

The reading blip

books

Our eyesight has evolved over time. Same for our hearing. And our other senses. But a skill like reading? Natural selection simply hasn’t had enough time to do its work.

According to estimates compiled by Bruce L. Gary in his book “Genetic Enslavement,” only 5% of the global population was literate 500 years ago. The rate hit 10% in 1650, 20% in 1750, 50% in 1850, and currently sits at a 60% plateau first reached in 1900.

What will it take to jolt the system and restore the climb toward 100%? My bet is nothing.

In fact, I’ll double down and bet that a hundred years from now, fewer people … perhaps substantially fewer … will be reading in any sense that we currently associate with the term.

I won’t be surprised if reading eventually takes its place alongside arts like calligraphy as a practice of hobbyists but not a path to learning. As a means of receiving information, reading will give way to high speed video, immersive VR simulation, skill-on-a-pill, direct neural implant, or mechanisms we can’t yet imagine.

Don’t get me wrong–I love to read and have spend a considerable portion of my professional career helping educators help children learn to read. If I’m right and around long enough to see the decline, I’ll shed a tear.

But if you want to know what I think about the development, don’t come back here expecting an update. Instead, just tune your neuroport’s ultrabluefli scanner to //dieffenblog{history>arcane>reading}//.

Elements of effective learning

elements-of-effective-learning

Multiple conditions contribute to successful learning. Intuition alone is insufficient to guide the understanding of these conditions—they must be backed by evidence. A key element of the mission of the MIT Integrated Learning Initiative (MITili) is to develop, collect, and share this evidence to improve learning.

“Making learning more effective” is too broad a challenge. To make the collection of evidence practical, MITili frames learning at three levels as shown in the illustration above: learner, instruction, and policy. Each of these levels can be broken down further to suggest research questions that can be asked and answered—the breakdown is not so much definitive as it is directional.

The learner, the instruction, the policy

The more that the learner, the instruction, and the policy are each set up to succeed, the more likely it is that learning will be effective.

learner1. The learner

 

If a learner has the right prior knowledge, motivation, interest, and physiological readiness, he or she will be in a good position to learn.

  • Prior knowledge
  • Motivation
    • Motivation as used here reflects external reasons for learning—to earn a credential, receive a promotion, or otherwise further a career. The topic might not interest the learner, but it could still be motivating.
  • Interest
    • Interest as used here regards internal reasons. There may be no external professional value to the topic, but it might still be intrinsically interesting.
  • Physiological readiness
    • The learner needs to be well-rested, well-fed, otherwise in good physical shape, and as important, in a good mental state that includes such traits as grit and growth mindset.

Having the right learner conditions in place isn’t sufficient for effective learning, however. Instruction must be right as well.

instruction2. The instruction

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

  • Content
    • Content includes the breadth, depth, and accuracy of the subject matter and the production value with which the subject matter is configured.
  • 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.
  • 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.

Having the right learner and instruction conditions in place also isn’t sufficient for effective learning. Policy must be right as well.

policy3. The policy

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

  • Law
    • Laws and regulations must be conducive to learning.
  • Access
    • Learners must have access to learning. This isn’t access via funding, but rather, access via circumstance. For instance, a K-12 student who is prohibited from going to a better school by virtue of geography/school/district boundaries might have an access problem.
  • Funding
    • Funds have to be available to pay for the learning experience. These funds may be provided by the learner or the provider of instruction.
  • Leadership
    • Leaders of those providing instruction must have a philosophy conducive to/supportive of learning.
  • 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.

This working paper outlines a framework—one that’s a work in progress—with which to think about specific research questions that can be asked and answered to improve learning effectiveness.

For instance:

Q: What impact does prior learning have?
Q: How can motivation be increased?
Q: What’s the role of better content?
Q: What’s the right duration for learning?
Q: How much funding is needed?
Q: What influence does leadership’s embrace of learning have?