Moving Beyond the Stigma: Understanding and Overcoming the Resistance to the Acceptance and Adoption of Artificial Intelligence Chatbots

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DOI:

https://doi.org/10.61838/kman.najm.1.2.4

Keywords:

Academic AI, AI Ethics, AI Misconceptions, AI Stigma, Algorithmic Bias, Chatbot Adoption, Research Innovation, Technology Resistance

Abstract

Artificial intelligence chatbots may fundamentally transform academic research, automate mundane tasks, and enhance productivity. However, the integration of artificial intelligence chatbots (AIc) is impeded by a complex stigma deeply rooted in individuals’ misconceptions and apprehension, including concerns about academic integrity, job displacement, data privacy, and algorithmic bias. The aim of this study was to scrutinize the origins and impacts of the stigma associated with artificial intelligence chatbots within the realm of academic research and to propose strategies to mitigate such stigmas. This study draws parallels between the reception of artificial intelligence chatbots and previous transformative technologies, presenting case studies illustrating the spectrum of responses to the integration of artificial intelligence chatbots into academic research. This study identifies the need for a shift in mindset from perceiving artificial intelligence chatbots as threats to recognizing them as facilitators of efficiency and innovation. It also underscores the importance of understanding these models as tools that aid researchers but do not replace the need for human expertise and judgment. We further highlighted the role of education, transparency, regulation, and ethical guidelines in overcoming the stigma associated with artificial intelligence chatbots. Given how adaptable people are, the surrounding stigma will likely fade with time. We support a cooperative strategy with continuing education and discussion to maximize the benefits of artificial intelligence chatbots while minimizing their drawbacks, hopefully paving the way for their ethical and successful application in scholarly research.

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Published

2023-12-11