A Faceted Approach to Language in OLab scenarios

How is the approach we are taking with conversational agents in OLab different from ChatGPT? Over the past decade, we have been exploring a variety of different approaches for incorporating natural language understanding into OLab.(1–3) Indeed, there is a long history in virtual patients of trying to introduce natural language. Our stance is that, while this is apparently engaging (and cute) at first sight, there are generally only a few areas in any given scenario where constructed responses are important. (https://olab.ca/constructed-responses-in-olab/ ) Our work with TTalk since 2013 has shown just what can be done with a simple chat-based interface, linked to the powerful virtual scenario engine in OLab. This has been shown to be cost-effective, scalable, extensible and with high learning impacts. But it does depend on a human element to a degree, which is both a strength and a limitation. More recently in our DFlow-related work, we have been incorporating more intelligent conversational agents in a manner that is limited in both scope and risk. And given the most recent developments with Microsoft’s AI Bing and ChatGPT, we are glad we have been cautious.(4) It would have been disastrous to unleash an unfettered ChatGPT in certain high risk scenarios. Part of what has made OLab and TTalk so effective in the past ten years is our success in creating scenarios that present a safe space, or more accurately a brave space (somewhere you can be brave enough to try new things), that shields learners from toxic risks and outcomes.