Written by: Con Gottfredson, Ph.D., Rw.E.
I’m an organizational learning and job-performance strategist with a doctorate in Instructional Psychology and Technology. My Ph.D. program included the equivalent of a master’s degree in measurement and evaluation (with an emphasis on product evaluation and human performance measurement). This schooling has since been informed by 40 plus years of real-world experience. All of that has taught me that whenever any new technology bursts onto the scene, the wisest course of action is to evaluate its potential contribution, role, and functional benefits through the lens of one or more capability frameworks. A capability framework provides the context for shaping the evolution and guiding the implementation of that technology.
With all emerging technologies we run the risk of getting caught in hyper-marketing quicksand. While quicksand isn’t deadly, it’s possible for us to get severely stuck, and our industry has certainly done that before. We’ve made costly mistakes in the past when we’ve jumped in, headfirst, without looking carefully through the lens of relevant frameworks. As we consider which frameworks should guide us in our AI pursuit, here are a couple of potential quicksand areas where we ought to be cautious:
- Institutional vs. Organizational Learning Frameworks: It is critical that we recognize the differences between AI’s role in institutional (academic) learning and its role in organizational learning. It would be a grave mistake to build an organizational learning strategy around AI based upon institutional learning frameworks.
- Self-Sponsored vs. Organization-Sponsored Learning & Development: When individuals devote their personal time and effort outside of work to their own development, there are no constraints regarding use of time—other than those imposed by the learners themselves. But when an organization invests its resources to help a person grow and develop as an employee, stopping work to learn is costly. The efficiency of time and effort must be considered in any learning framework sponsored by an organization.
With these cautions in mind, here’s an organizational learning and job-performance framework worth considering. Let’s sort out AI’s ability to help us produce, deliver, maintain, and measure the impact of the solutions we create within our existing technology platforms. It makes sense to start here because there isn’t a learning technology vendor who isn’t looking to AI to enhance their offerings. What’s needed is an intentional way of consolidating, prioritizing, and guiding all that’s happening.
The following table provides a high-level view of five technology platforms aligned to three capability areas that represent the work we do. This is an example of a framework that can provide meaningful context for shaping the evolution and guiding the implementation of AI.
Within each of these platforms there are multiple technology systems orchestrated together to address the broader functionality of that platform. The following table shows the primary systems for each platform:
PLATFORM: Learning Measurement | |||
| Learning Measurement System | A set of features and tools that measure and report on the organizational impact of learning and job-performance support solutions. | ||
PLATFORM: Knowledge and Content Management | |||
| Learning Content Management System (LCMS) | Provides single-source content authoring and maintenance of reusable content objects, which automatically assembles learning and job-performance solutions. | ||
| Translation Management System | All-in-one localization and translation management platform that controls, collaborates, and automates the whole translation process. | ||
| Knowledge Management System (KMS) | Stores and retrieves knowledge management assets and provides the ability to search for, capture, update, and maintain relevant information. | ||
PLATFORM: Learning Experience/Delivery | |||
| Learning Management System (LMS) | Provides role- and permissions-based assignments and completion tracking, tests and assessments, hosting and administration, and reporting and analytics of educational courses, training programs, and materials. | ||
| Virtual Learning/Coaching System | Provides a collaborative, live, virtual environment with video conferencing, polling, shared whiteboard, emoticons, virtual break-out rooms, and more. | ||
| eLearning System | Provides self-paced interactive instruction and can include: · Microlearning modules · Interactive media/video · Assessment Enhances the functionality of eLearning solutions in concert with the LCMS. | ||
| Adaptive Learning System | Pushes out smart microlearning experiences in the form of questions and automatically adapts those experiences based on a personalized assessment of each reviewer’s level of mastery. Data gathered in these activities can also be pushed into the measurement system and the LMS (to the degree it’s warranted). | ||
| Learning Experience Platform (LXP) | Personalizes the educational journey for each user. It recommends learning content based on individual preferences and patterns, much like a smart/interactive guide for learning. LXPs offer a variety of formats, including interactive courses, videos, and social learning opportunities. | ||
PLATFORM: Performance Support (Workflow Learning) | |||
| Job-Task Performance Support System | Provides personalized job-task-level contextual support with scaffolded degrees of support that provides 2-3-click access to steps, supporting knowledge, and knowledge/learning/people resources. | ||
| Digital Adoption Platform | Provides contextual help within and across software systems. | ||
PLATFORM: Collaborative Work | |||
| Communities of Practice System | Environment for threaded discussions and sharing of experiences and tacit knowledge around a subject | ||
Most certainly, technology champions are in hot pursuit of AI capabilities in each of these technology systems. What’s missing is a consolidated view of those current and future state capabilities in the context of the three key methodology areas shown in Figure 1 above. This is where you come in, and there are three ways you can help:
- Join us at the 5 Moments of Need Summit 2024, where we are focusing on consolidating the wisdom and experience of practitioners, thought leaders, and technology vendors, who will share their organizational experience with AI.
- If you can’t join us for the Summit, sign up to be a contributor to our “Harness the Power of AI” initiative! Those who sign up as contributors will have the option to complete the three questionnaires that Summit participants will be completing in the virtual Summit sessions. By participating, you will receive a consolidated summary of all responses.
- If this is an area of interest but you only have time to be an observer, sign up to listen in on a series of conversations with AI experts and practitioners to help further validate these and possible additional frameworks.
Here’s more detail about what we’re doing to systematically address AI in the context of organizational learning:
We have planned our annual Summit using the above technology framework in the context of
- Developing and Maintaining Solutions,
- Delivering and Optimizing those solutions, and
- Tracking, Measuring, and Reporting the impact of those solutions.
Specifically, for each of these three areas, participants will gather in three virtual pre-conference sessions to identify and prioritize specific challenges their organizations are facing. They will also document their organizational experience with AI within their technology stacks, along with any AI functional opportunities they are pursuing.
We will summarize the data from these three sessions for participants to draw upon during the in-person Summit experience in Charlotte, North Carolina from April 16-17th. At the Summit, participants will:
- Share breakthrough experiences within the learning and job-performance framework.
- Roll up their sleeves and collaborate in the daily hackathons to brainstorm innovative solutions to the top challenges identified during the three virtual preparation gatherings.
- Envision AI possibilities that could revolutionize learning and job-performance practices.
Following the Summit, we will consolidate the experience and insights of all our participants and then continue to host meaningful conversations around this and other frameworks.
To put a bow around our AI efforts and my thinking on the subject, I’d like to share that just over a year ago, I bought my grandparents’ home in the small town of Circleville, Utah where I was born and raised. My grandparents built the home in 1945, so I have been busy restoring it. When I am at the house, on the desk where I work, I have the original phone my grandparents used. It’s connected and working. Underneath it is a small telephone book for the towns of Circleville, Kingston, and Junction. On the other side of my desk, I have my smart phone. The two phones remind me of where we are today and where AI can take us. When it comes to organizational learning, the old 1940s rotary dial phone represents where we are today. And my smart phone represents where AI could take us. If we’re smart about how we look at and approach what AI offers, I’m confident we’ll quickly get to a miraculous place. If not, we’ll have to endure a longer journey with inferior applications along the way (like those first big clunky cell phones).
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