Evaluation Metrics and Ethical Concerns in the Design of Adaptive AI Chatbots

Abstract

The proliferation of large language models in recent years has led to the widespread development of AI chatbots. AI chatbots are smart systems that enable conversations with users using natural language. Chatbots are used to automate tasks, offer real-time assistance, and enhance user experiences. Their increasing use in many domains, such as customer service, education and healthcare support, makes their reliable evaluation essential. To this direction, this paper presents a literature review on evaluation frameworks for adaptive <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A I$</tex> chatbots, highlighting the trends, gaps, and future research directions. It focuses on four critical dimensions of chatbot assessment: personalization and adaptability, information accuracy, response adequacy and speed, as well as bias and ethical concerns. The comparative analysis of related studies shows the importance of applying multidimensional evaluation approaches that integrate both technical features and user-centered metrics. Furthermore, it identifies major challenges, such as the lack of standardized evaluation criteria and the need for transparency and ethical accountability in chatbot deployment.

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