DSpace Collection:https://hdl.handle.net/10356/9932024-03-29T15:08:07Z2024-03-29T15:08:07ZBelieving the bot: examining what makes us trust large language models (LLMs) for political informationDeng, Nicholas Yi DarOng, Faith Jia XuanLau, Dora Zi Chenghttps://hdl.handle.net/10356/1743842024-03-28T08:45:39Z2024-01-01T00:00:00ZTitle: Believing the bot: examining what makes us trust large language models (LLMs) for political information
Authors: Deng, Nicholas Yi Dar; Ong, Faith Jia Xuan; Lau, Dora Zi Cheng
Abstract: Affective polarisation, the measure of hostility towards members of opposing political parties, has been widening divisions among Americans. Our research investigates the potential of Large Language Models (LLMs), with their unique ability to tailor responses to users' prompts in natural language, to foster consensus between Republicans and Democrats. Despite their growing usage, academic focus on user engagement with LLMs for political purposes is scarce. Employing an online survey experiment, we exposed participants to stimuli explaining opposing political views and how the chatbot generated responses. Our study measured participants' trust in the chatbot and their levels of affective polarisation. The results suggest that explanations increase trust among weak Democrats but decrease it among weak Republicans and strong Democrats. Transparency only diminished trust among strong Republicans. Notably, perceived bias in ChatGPT was a mediating factor in the relationship between partisanship strength and trust for both parties and between partisanship strength and affective polarisation for Republicans. Additionally, the strength of issue involvement was a significant moderator in the bias-trust relationship. These findings indicate that LLMs are most effective when addressing issues of strong personal relevance and emphasise the chatbot's political neutrality to users.2024-01-01T00:00:00ZAlive = 生Lim, Melanie Hui EnOng, Taylor Yin MengOng, Philip Chong LiLiu, Ashley Yan Tonghttps://hdl.handle.net/10356/1744032024-03-28T08:36:03Z2024-01-01T00:00:00ZTitle: Alive = 生
Authors: Lim, Melanie Hui En; Ong, Taylor Yin Meng; Ong, Philip Chong Li; Liu, Ashley Yan Tong
Abstract: Set in a post-apocalyptic world where fossil fuels have run out, humans must run on treadmills to generate electricity and hit output targets. An 18-year-old girl who often struggles to hit her electricity output targets meets a disillusioned 30-year-old man, who wants to escape this life. Through their journey, she begins to wonder about the true purpose of being alive.2024-01-01T00:00:00ZNavigating modern dating: the impact of gender stereotypes and perceived social roles on individuals' perceptions of others' online dating profilesChan, Jen-Le JemimaAlam, Nabilah HibahPaglinawan, Lace Nicole RiveraQuek, Victoria Xiaoxuanhttps://hdl.handle.net/10356/1744012024-03-28T08:15:10Z2024-01-01T00:00:00ZTitle: Navigating modern dating: the impact of gender stereotypes and perceived social roles on individuals' perceptions of others' online dating profiles
Authors: Chan, Jen-Le Jemima; Alam, Nabilah Hibah; Paglinawan, Lace Nicole Rivera; Quek, Victoria Xiaoxuan
Abstract: Dating applications have been seeing a rapid rise in popularity over the last few years, presenting a new way to meet potential partners online. While there is extensive research on gender stereotypes and social roles in general, studies on how these intersect with the digital space is still a relatively emerging topic of research, and there are even fewer studies that focus specifically on a Singaporean or Asian context. This research paper aims to close this gap and provide further insight into how gender stereotypes and social roles intersect and affect the way Singapore young adults evaluate others on dating apps. In our study, a 2x2 between-subject factorial experiment (N = 220) was conducted to investigate the interaction effects of gender stereotypes and perceived social roles on different measures of romantic attraction in the context of a dating app profile. Our results showed some patterns in these interaction effects, and this paper further discusses the reasons for these and broader findings. They can then be used to evaluate whether the above social constructs are changing with the 21st century, and further deepen our understanding of how individuals find others romantically attractive in the digital space.2024-01-01T00:00:00ZJust ChatGPT it: a mixed methods evaluation of generative AI use among college studentsChng, Elliot Wei ShengChin, Jona Tze MeiKee, JensenNg, Shang Yuhttps://hdl.handle.net/10356/1743982024-03-28T08:02:38Z2024-01-01T00:00:00ZTitle: Just ChatGPT it: a mixed methods evaluation of generative AI use among college students
Authors: Chng, Elliot Wei Sheng; Chin, Jona Tze Mei; Kee, Jensen; Ng, Shang Yu
Abstract: The widespread adoption of ChatGPT in higher education has sparked debate, particularly concerning its potential to facilitate academic dishonesty. Despite attempts to tackle ChatGPT use in universities through policy development, obstacles remain because of unclear boundaries concerning its use in more contentious contexts. Our study offers insights into the use of ChatGPT for graded writing assignments from the student perspective, which may help inform future policies regulating ChatGPT use in universities. A simultaneous explanatory mixed-methods approach was used. Study 1 consisted of an online survey (n = 482) of Singaporean university students, while Study 2 consisted of online interviews (n = 20) with Singaporean undergraduates. Guided by the Theory of Planned Behaviour and the Technology Acceptance Model, our survey found that all determinants, except for attitudes, were significant predictors of intention to use ChatGPT for academic purposes. Study 2 also showed students’ mixed attitudes towards the use of ChatGPT. The interviews also found that students defined their ethical boundaries for the academic use of ChatGPT based on several pragmatic considerations and meta-ethical concerns, and their perceptions of ChatGPT's overall impact on their academic work ranged from negative to positive.2024-01-01T00:00:00Z