We want to lay out exactly what we’re trying to do at Mindsmith. So here it is:
The Mindsmith Mission
Our prime directive as a company is to use generative AI to make instructional design faster, cheaper, and more productive. This sounds very alarming if you’re an instructional designer and/or trainer. Here’s why we believe that large language learning models and other generative AI technologies are an enormous benefit to members of the industry as well as the most important stakeholders—the learners:
- Artificial intelligence radically lowers the opportunity cost of creating new trainings, courses, and other content. This means designers can spend more time as architects rather than “bricklayers”—more time surveying members of the organization, such as instructors and learners, to better understand instructional needs. Because of this, instructional “architects” don’t have to be so conservative about designing new content. They can innovate, test, and quickly iterate new instructional strategies.
- As easy as it is to imagine a future wherein IDer’s (along with basically everyone else in white collar industries) are radically displaced by AI, we can work towards creating a future where this technology, which Mindsmith has already produced, makes instructional designers orders of magnitude more productive. Entry-level designers trying to snag that first job become more enticing to employers if they can demonstrate mastery of highly productive AI toolkits. HR departments can command more funding if they can demonstrate how their team of instructional architects is using AI to target and address measurable skill deficits.
- Similar argument for trainers and managers. This group will spend less time worrying about tweaking or individualizing training material because adaptive learning systems, which Mindsmith is actively developing, will handle those functions. Trainers will devote more of their time to building people, not lessons—getting to know their team members, coaching them through difficult situations, and attending to them with a personal touch.
- Learning gets astronomically better for learners: with an adaptive AI assistant, learners experience deeply personalized training. A golf junkie will be able to inform their AI assistant that they’d rather be on the green than in the office—the AI will immediately tailor the learner’s training to this hobby, using golfing analogies and imagery to drive home important concepts. This level of personalization is what Mindsmith aims to produce in the near to middle future.
You might’ve caught onto this throughout the explication of those three groups, but if not, here’s the basic outline of our Master Plan:
- Lift static trainings (e.g. PowerPoints, Word documents, internal company materials) into a dynamic experience. We’ve already accomplished this minus a few bug fixes.
- Make these dynamic trainings customizable by enabling it to mold lessons around basic categories like the purpose of a lesson, context, and organizational role of the learner. This is the part of the technology we’re building right now.
- Deepen these customized experiences with a real-time adaptive AI assistant that tailors every aspect of the learning content to the individual’s identity. This is the capability we hope to develop by early 2024.
We’re using generative AI in learning and development because we have a strong intuition it’ll benefit instructional designers, trainers, and learners together. Our plan is to perfect the ability to convert static documents into dynamic trainings; make these dynamic trainings customizable according to learners’ organizational roles; and eventually deepen these experiences with an AI system that actively tailors content to individual learners’ needs.
Catch you in 2024.
-The Mindsmith Team