Who are you? 

INFERable is a public benefit corporation with a mission to make a material positive impact on systems of education, training, and human development.

What do you offer? 

We offer modularized AI as a service that can predict and guide optimization of learning and performance and a Skills Architecture based on international standards. We support teams and organizations implementing learning engineering and a Modern Open Systems Approach (MOSA) to learning technology. We're giving creators and users of online education/training content a way to "plug in" to powerful artificial intelligence and learning analytics, like the AI inference detectors used in sophisticated intelligent tutoring systems.

How is it done today, and what are the limits of current practice?

Many researcher-developed single-purpose models are hidden in academic papers, often locked behind the paywalls of academic journals and encoded in academic jargon. Most learning content and platforms don’t benefit from these methods. 

What is new in your approach? Why will it be successful?

We make precision AI as a service available to any creator of online learning content, to work with any online learning platform, and to support a variety of operations, education, or training functions. Our technology leverages an international standards for data instrumentation and competency definitions as linked data, eXperienceAPI (IEEE 92741.x) and Shared Competency Definitions (IEEE 1484.20.3). These standards are already widely used in the military and commercial training sectors and by our world class team of learning engineering partners.

Who cares? What difference will it make?

Our unique approach to any-platform learning and performance analytics as a service democratizes access to AI for use with online learning content and performance enhancing systems. 

Learning engineering teams now have the power to create adaptive content, and to use research-based methods and  data-informed decision-making for iterative improvement of learning experiences. Learners at all levels can now have better feedback loops, like those in sophisticated intelligent tutoring systems. Developers of systems supporting readiness and performance optimization can "plug in" precision detectors that adapt and personalize content and feedback. This also enables other applications of AI for predictability of readiness and performance. 

With plug-in inference detection the static content can be personalized with data-informed scaffolding for desirable difficulty, spaced repetition, application of motivating operations and other variables rooted in learning sciences. This technology lowers the bar for more creators of educational content to practice learning engineering. Learning will be better for millions of people.