Authored by:
Pooja Rangarajan
Rutgers University
School of Public Health
Piscataway NJ
pmr117@sph.rutgers.edu
Jacob Boyle
MARCo Health Inc.
Newark NJ
jacob@marcohealthtech.com
The escalating demand for personalized AI chatbot interactions, capable of dynamically adapting to user emotional states and real-time requests, has highlighted critical limitations in current development paradigms. Existing methodologies, which rely on baseline programming, custom personalities, and manual response adjustments, often prove difficult to maintain and are susceptible to errors such as hallucinations, erratic outputs, and software bugs. This paper hypothesizes that a framework rooted in human psychological principles, specifically therapeutic modalities, can provide a more
robust and sustainable solution than purely technical interventions. Drawing an analogy to the simulated neural networks of AI mirroring the human brain, we propose the application of Dialectical
Behavior Therapy (DBT) principles to regulate chatbot responses to diverse user inputs. This research investigates the impact of a DBT-based framework on AI chatbot performance, aiming to ascertain its
efficacy in yielding more reliable, safe, and accurate responses, while mitigating the occurrence of hallucinations, erratic behaviors, and other systemic issues.
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