English

A Maturity Assessment Framework for Conversational AI Development Platforms

Human-Computer Interaction 2020-12-23 v1

Abstract

Conversational Artificial Intelligence (AI) systems have recently sky-rocketed in popularity and are now used in many applications, from car assistants to customer support. The development of conversational AI systems is supported by a large variety of software platforms, all with similar goals, but different focus points and functionalities. A systematic foundation for classifying conversational AI platforms is currently lacking. We propose a framework for assessing the maturity level of conversational AI development platforms. Our framework is based on a systematic literature review, in which we extracted common and distinguishing features of various open-source and commercial (or in-house) platforms. Inspired by language reference frameworks, we identify different maturity levels that a conversational AI development platform may exhibit in understanding and responding to user inputs. Our framework can guide organizations in selecting a conversational AI development platform according to their needs, as well as helping researchers and platform developers improving the maturity of their platforms.

Keywords

Cite

@article{arxiv.2012.11976,
  title  = {A Maturity Assessment Framework for Conversational AI Development Platforms},
  author = {Johan Aronsson and Philip Lu and Daniel Strüber and Thorsten Berger},
  journal= {arXiv preprint arXiv:2012.11976},
  year   = {2020}
}

Comments

10 pages, 10 figures. Accepted for publication at SAC 2021: ACM/SIGAPP Symposium On Applied Computing

R2 v1 2026-06-23T21:12:08.792Z