Aivars Spilbergs, Biruta Dzērve, Sandra Ozoliņa, Gunta Innuse-Breidaka, Tatjana Mavrenko, Laima Čable, Agnese Vincēviča, Biruta Sloka, Ginta Tora and Kristīne Liepiņa

https://doi.org/10.54175/hsustain4040017

This study examines the primary risks associated with using generative artificial intelligence (GAI) in social science research and proposes a framework for higher education institutions to effectively manage these risks. As universities increasingly integrate GAI into ... Read More ... Read More This study examines the primary risks associated with using generative artificial intelligence (GAI) in social science research and proposes a framework for higher education institutions to effectively manage these risks. As universities increasingly integrate GAI into teaching, research, and administration, concerns around intellectual property, academic integrity, data privacy, and ethical use have intensified. This paper explores the adequacy of current legal frameworks in addressing these challenges, drawing on recent legal analyses and institutional practices. Survey data reveal statistically significant differences in perceptions of the need for GAI guidelines based on respondents’ age, education level, field of study, research experience, and geographic region. The findings underscore the urgency of developing adaptive, risk-based policies that support responsible integration of GAI while safeguarding academic standards. The study concludes by proposing guiding principles for a dynamic legal framework that balances innovation with accountability. These recommendations aim to support sustainable and ethical GAI adoption in higher education institutions and contribute to the broader discourse on responsible AI governance in academia. ... « or Access Full Article

Md Tasbirul Islam, Usha Iyer-Raniga and Amjad Ali

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Hannes Antonschmidt

https://doi.org/10.54175/hsustain3020009

Afonso Delgado, Paulo Caldas and Miguel Varela

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Sevasti Malisiova and Stella Kostopoulou

https://doi.org/10.54175/hsustain2040017