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  1. Balancing AI and academic integrity: what are the positions of academic publishers and universities?Bashar Haruna Gulumbe, Shuaibu Muhammad Audu & Abubakar Muhammad Hashim - forthcoming - AI and Society:1-10.
    This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in (...)
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  • Fiction writing workshops to explore staff perceptions of artificial intelligence (AI) in higher education.Neil Dixon & Andrew Cox - forthcoming - AI and Society:1-16.
    This study explores perceptions of artificial intelligence (AI) in the higher education workplace through innovative use of fiction writing workshops. Twenty-three participants took part in three workshops, imagining the application of AI assistants and chatbots to their roles. Key themes were identified, including perceived benefits and challenges of AI implementation, interface design implications, and factors influencing task delegation to AI. Participants envisioned AI primarily as a tool to enhance task efficiency rather than fundamentally transform job roles. This research contributes insights (...)
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  • Interplay of factors determining users’ intentions to adopt chatbots for airline tickets assistance. The moderating role of perceived waiting time.Imdadullah Hidayat-ur-Rehman - forthcoming - AI and Society:1-15.
    This research seeks to perform a detailed empirical examination of the variables affecting user intent to utilize chatbots for airline ticket inquiries, using the Technology Acceptance Model (TAM) as a foundation. The study introduces a thorough framework that merges crucial elements like Perceived Social Presence, Perceived Completeness, Perceived Accuracy, Perceived Convenience, and User Satisfaction with TAM’s conventional constructs of Perceived Usefulness and Behavioural Intention to Use Chatbots. The partial least squares-structural equation modelling (PLS-SEM) method was employed to empirically verify our (...)
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