Artificial Intelligence, or AI, has become a buzzword. Many label practically any decision made by machines as AI, and it becomes difficult to discern the difference between true AI innovation based on Deep Learning from the clever relabeling of existing capabilities. Many of these pseudo-AI efforts require massive investments of time and money to simulate an AI-like experience. This makes it vital for us to know how to spot true innovation.
Seeking True Solutions
Learners benefit from a pedagogy that identifies their unique learning requirements and aligns just-in-time content and feedback to match—resulting in a high engagement learning experiences. As an eLearning solution, Deep Learning AI needs to assess learners and their learning essentially on its own. It should be able to adapt the presentation of content according to that knowledge, not just navigate a pre-defined, static, path structure. The AI should deliver personalization autonomously without requiring massive investments of instructor time to deliver this personalized learning experience.
Today’s learners want well-defined, tailored career paths that fit their future aspirations. You see this in the recent explosion of competency-based programs focused on a more personalized learning. Southern New Hampshire University and Western Governors University built large and specialized online competency-based programs tailored to adult learners. The key came from focusing the needs of adult learners with jobs, families, and busy schedules—and how the university could support those needs with as little trouble for students as possible.
Competency-based programs customize the path a learner takes through a curriculum and personalize the learning experience. Most competency-based programs use test results as the basis for dictating learning paths. Assessments, however, only tell a small part of the story, and by the time an assessment suggests poor learning results, the learning experience has already failed.
Still, competency-based learning systems are cost and labor-intensive to configure and deploy. Most require humans to reauthor existing courses by manually segmenting, indexing, and tagging content. In addition, humans must try to anticipate the “if-this-then-that” pathing logic. They must predict and create all the variants of possible content that can be presented for each learning path.
AI Based Deep Learning
AI based on Deep Learning ingests existing content and autonomously personalizes the learning experiences based on learning strategies and knowledge level of each learner. This AI leverages smart algorithms and machine power to do most of the work, so already over-taxed ID/SME teams don’t have to invest 100s or 1,000s of hours on existing courses.
Armed with a granular understanding of course content and assessments, plus behavioral data and autonomous personalization of eLearning content Deep Learning AI can:
- Identify a learner’s unique learning strategy from their behavioral sequences as they interact with the content
- Correlate and predict knowledge transfer from these sequences of behavioral indicators with incredible accuracy
- Autonomously generate remediated content with existing courses to align the learner’s unique learning strategy and ID teams do not have to pre-author remedial content
- Quantify learner engagement with course material in real time
- Identify preferred content delivery modalities and personalized to each learner’s preference
Deep Learning AI allows Instructional Design teams to move beyond the reactive, historical statistics that describe the past—into proactive, prescriptive analytics that influence the future.
Have you explored AI-enabled learning paths? Let us know.
Zoomi.ai. (n.d.). New generation AI for learning [White paper]. Retrieved March 4, 2019, from
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