Factors That Influence the Adoption of AI-enabled Conversational Agents (AICAs) as an Augmenting Therapeutic Tool by Frontline Healthcare Workers: From Technology Acceptance Model 3 (TAM3) Lens -- A Systematic Mapping Review
Rawan AlMakinah
TL;DR
This systematic mapping review uses the Technology Acceptance Model 3 (TAM3) to characterize frontline mental health practitioners' attitudes toward AI-enabled conversational agents as augmenting therapeutic care. Analyzing 27 studies from 2020–2024 in Web of Science, the authors map findings to TAM3 constructs, showing that perceived usefulness and perceived ease of use drive adoption while ethical, reliability, and empathy concerns temper enthusiasm. The study identifies moderating factors such as prior experience, with voluntariness as an underexplored area, and highlights gaps like computer playfulness and professional image. The work offers a TAM3-guided framework to inform the design, training, and governance of AI tools in mental health and outlines directions for future research to enable safe, effective human-AI collaboration in therapy.
Abstract
Artificial intelligent (AI) conversational agents hold a promising future in the field of mental health, especially in helping marginalized communities that lack access to mental health support services. It is tempting to have a 24/7 mental health companion that can be accessed anywhere using mobile phones to provide therapist-like advice. Yet, caution should be taken, and studies around their feasibility need to be surveyed. Before adopting such a rapidly changing technology, studies on its feasibility should be explored, summarized, and synthesized to gain a solid understanding of the status quo and to enable us to build a framework that can guide us throughout the development and deployment processes. Different perspectives must be considered when investigating the feasibility of AI conversational agents, including the mental healthcare professional perspective. The literature can provide insights into their perspectives in terms of opportunities, concerns, and implications. Mental health professionals, the subject-matter experts in this field, have their points of view that should be understood and considered. This systematic literature review will explore mental health practitioners' attitudes toward AI conversational agents and the factors that affect their adoption and recommendation of the technology to augment their services and treatments. The TAM3 Framework will be the lens through which this systematic literature review will be conducted.