When learning for long-term goals, the tasks of discovering and assimilating relevant resources from the Internet are time consuming, counter-productive and biased. Particularly, for millennials, the fastest growing workforce population, the Internet constitutes more than 70% of their learning and information sources. We propose a personalized conversational agent, called CatalystBot, that engages with the learner to help navigate their evolving learning context – historical, present and future – in two key ways. First, our solution provides a learning-focused search strategy that is more efficient than trial-and-error habits. Second, it highlights concepts from journaled resources that are likely to drive learning outcomes for the learner, as it pertains to their personal and organizational context. Our approach uses a neural language model to bring the 'thought process' of expert learners to the novice learner's discovery and assimilation habits.
We introduce a learning-focused ontology with a Situational Learning Graph - a chronological network of why and what you learn.
When it comes to learning, it's time to think 1:1. You:You.