Augmented Intelligence: Human-Ai Collaboration in the Era of Digital Transformation

The use of artificial intelligence (AI) tools, such as chatbots, has significantly increased in academia and research. The present study seeks to determine the key factors influencing chatbot adoption, as well as attempts to validate the unified theory of acceptance and use of technology (UTAUT) in the context of AI chatbots adoption among research scholars. The data for this study were collected through purposive sampling using a cross-sectional survey. The population of the study comprised research scholars enrolled in three public sector universities in Pakistan. The eight-factor proposed measurement model was estimated using confirmatory factor analysis (CFA) based on 30 valid items. The goodness of fit indices suggest a favourable fit for the model χ2 = 1.710, DF = 381; p = 0.000; IFI = .902; TLI = 0.886, CFI = 0.900, RMSEA = 0.056. Our research affirms that social influence, trust, and facilitating conditions play pivotal roles as primary predictors of behavioural intentions for AI chatbots adoption among scholars. The study suggests that the perceived risks associated with using AI chatbots due to their potential misuse can be minimized by effectively implementing AI user guidelines, and developing AI literacy among scholars. Information professionals and ethical libraries can play an important role in “building the bridge” between cutting-edge technology capabilities and information users’ needs and rights. The proposed eight-factor AI chatbots adoption model holds substantial potential in understanding the influence of performance expectancy, effort expectancy, social influence, trust, perceived risk, and facilitating conditions on behavioural intention to AI chatbots adoption. This study contributes to the limited body of research investigating the factors influencing AI chatbot adoption among research scholars using the UTAUT model with additional constructs for trust and perceived risk.

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AI-Augmented Medicine

Kaur

2024

Advances in Medical Technologies and Clinical Practice

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The integration of powerful artificial intelligence (AI) technologies alongside medical personnel has emerged as a transformational paradigm in the quickly expanding environment of healthcare. This study examines the several functions of AI-augmented medicine, with a particular focus on its capacity to transform multiple facets of healthcare provision. Artificial intelligence (AI) enables enhanced precision in diagnoses, tailored treatment strategies, and optimised clinical processes by leveraging advanced algorithms and machine learning methodologies. Furthermore, the utilisation of AI-driven clinical decision support systems provides medical personnel with immediate access to valuable information and evidence-based suggestions, thereby improving patient outcomes and ensuring safety. Moreover, in fields like medical imaging, AI systems demonstrate exceptional proficiency in identifying abnormalities and aiding radiologists in the process of interpreting data.

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Augmented Intelligence: Human-Ai Collaboration in the Era of Digital Transformation

Cited by 4 publications

References 19 publications

Advancing the generative AI in education research agenda: Insights from the Asia-Pacific region

Advancing the generative AI in education research agenda: Insights from the Asia-Pacific region

Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT)

AI-Augmented Medicine

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