• Generative AI tools are highly adopted in academia, with significant differences across fields, genders, and countries. • PhD students and early-career academics are the highest adopters for research purposes. • AI applications are widely used for translation, proofreading, and literature review, but less for data analysis in research activities. • Content creation, learning support, and assignment design are key reasons for using AI in teaching, with different patterns based on academic positions and countries. • Inaccurate information, plagiarism, and reduced critical thinking skills are the top concerns of AI use in academia. Although generative AI is transforming academic research and education, little is known about the role, gender, international, and disciplinary variations in uptake and use. This 20-country survey of publishing academics shows the widespread awareness and adoption of generative AI tools in academia, but with substantial international and disciplinary differences, and some role and gender differences. In particular, females were 10 % less likely to use Gen AI frequently (daily or weekly) for research, which may exacerbate gender inequalities. Perhaps surprisingly, the highest adoption rates occurred in some non-Western nations, possibly because of a greater need for translation services. The highest awareness is in the social sciences, perhaps because of the greater need for text analysis. Across all groups, these tools were mainly used for academic writing rather than data analysis and support for critical thinking. Despite this, personalized instruction and problem-solving are among generative AI's most generally claimed benefits. However, participants in all groups were skeptical about the creativity, accuracy, and consistency of AI-generated content in academic contexts. The most significant concerns about using generative AI in academia were inaccuracy, plagiarism, discouraging critical thinking, a lack of transparency and explainability, intellectual property rights violations, and data privacy risks. For policymakers, the findings point to fields and countries that may need action to prevent falling behind, as well as the ongoing need to investigate and monitor the impacts of generative AI on research practices.

Abstract Rarely any study investigates how information gatekeeping through the solutions and services enabled by algorithms, hereafter referred to as algorithmic technologies (AT), creates negative consequences for the users. To fill this gap, this state‐of‐the‐art review analyzes 229 relevant articles from diverse academic disciplines. We employed thematic analysis to identify, analyze, classify, and reveal the chain reactions among the negative consequences. We found that the gatekeeping of information (text, audio, video, and graphics) through AT like artificial intelligence (e.g., chatbots, large language models, machine learning, robots), decision support systems (used by banks, grocery stores, police, etc.), hashtags, online gaming platforms, search technologies (e.g., voice assistants, ChatGPT), and Web 3.0 (e.g., Internet of Things, non‐fungible tokens) creates or reinforces cognitive vulnerability, economic divide and financial vulnerability, information divide, physical vulnerability, psychological vulnerability, and social divide virtually and in the offline world. Theoretical implications include the hierarchical depiction of the chain reactions among the primary, secondary, and tertiary divides and vulnerabilities. To mitigate these negative consequences, we call for concerted efforts using top‐down strategies for governments, organizations, and technology experts to attain more transparency, accountability, ethical behavior, and moral practices, and bottom‐up strategies for users to be more alert, discerning, critical, and proactive.

Abstract Since the inception of gatekeeping research in the 1940s, most studies on gatekeeping have been human‐centric, treating and studying individuals as gatekeepers, who perform their gatekeeping role using a combination of the following mechanisms: forming communities, and/or broadcasting, discovering‐searching, collecting, organizing, or protecting information. However, the nature of communication channels and how information is produced by and shared with users has fundamentally changed in the last 80 years. One significant change is the growing use of technology‐enabled metadata like hashtags when sharing information on social media. Rarely any study investigates whether hashtags can perform gatekeeping of information and what it means for information gatekeeping. This paper fills in the gap by conducting a content analysis of 77 interdisciplinary studies on hashtags and gatekeeping to confirm how they can implement six gatekeeping mechanisms. This study shows that hashtags expand our understanding of the role of technology solutions in gatekeeping and advance research on hierarchical gatekeeping. The benefits of hashtags for gatekeeping suggest that they act as “information anchors” for online communities, thereby highlighting the utility of information gatekeepers for society.

I aim to investigate how autonomous motivation versus controlled motivation affects Web search behaviors of health consumers, as well as their search effort and enjoyment. I will conduct a within-subject study to investigate the research questions. The design of the search tasks will be the key to whether participants are autonomously motivated to search or not. The design of the search tasks will be based on the Self-Determination Theory (SDT), a theory of motivation. The results of the experiment will enrich our understanding of how users within different motivational situations search online information.