Conference Agenda

The study on how individuals search for and use online information about indoor air quality (IAQ) is motivated by the growing concern over health misinformation on social media and the importance of eHealth literacy in public health. This research aims to bridge gaps in understanding how people seek and utilize IAQ information, which is crucial given the significant time spent indoors and the impact of IAQ on health. Researchers such as Nazarnia et al. (2023) came to the conclusion that health misinformation on social media is a persistent public health concern that requires the proper skill set for interpreting and evaluating accurate information. Tesfa et al. (2022) also expressed concern that eHealth literacy is acknowledged as a critical component of public health and Gao et al. (2022) have conducted research which aimed to examine the relationship between residents’ health literacy (HL) and their use of and trust in information channels. This review aimed to understand how individuals search for and use online information about indoor air quality, identify key factors influencing this behavior, and highlight gaps in the literature to propose directions for future research. Thematic analysis approach was used for critical literature review, by identifying relevant studies through academic databases (Web of Science, PubMed and Google Scholar) using keywords such as “indoor air quality,” “online information-seeking,” and “health concerns.” Studies were selected based on their relevance to IAQ information-seeking behavior, trust in sources, and the impact on media or events. They were categorized into key themes and analyzed to identify trends, gaps, and limitations in those studies. The findings were then synthesized to provide a cohesive overview of the current state of research. According to Yang (2021), health concerns drive most IAQ-related searches, often reactively, and while users prefer authoritative sources like government agencies (Unni et al., 2022), they frequently rely on social media and forums, which can spread misinformation. A significant portion of online activity focuses on researching IAQ-related products, but users often lack a clear understanding of the technology (Li et al., 2021). Events like wildfires or pandemics significantly increase IAQ-related searches acccording to Google trends, but public health campaigns often fail to translate awareness into action. Previous studies highlight the importance of eHealth literacy in navigating health information effectively. This study extends this by focusing on IAQ, a specific health concern where misinformation can have significant health impacts (Kbaier et al., 2024).

References

Gao, Y. et al. (2022). Improving health literacy: Analysis of the relationship between residents’ usage of information channels and health literacy in Shanghai, China. International Journal of Environmental Research and Public Health, 19(10): 6324.

Google Trends. (2020). Search Interest in “Ventilation” and “Air Filtration” during the COVID-19 Pandemic. Retrieved 21 August, 2025 from https://trends.google.com

Unni, B., Tang, N., Cheng, Y. M., Gan, D., & Aik, J. (2022) Community knowledge, attitude and behaviour towards indoor air quality: A national cross-sectional study in Singapore. Environmental Science & Policy, 136: 348–356 https://doi.org/10.1016/j.envsci.2022.06.021

Kbaier, D., Kane, A., McJury, M., & Kenny, I. (2024). Prevalence of health misinformation on social media: Challenges and mitigation before, during, and beyond the covid-19 pandemic: Scoping literature review. Journal of Medical Internet Research, 26, e38786.

Li, C., Bai, L., He, Z., Liu, X., & Xu, X. (2021). The effect of air purifiers on the reduction in indoor PM2.5 concentrations and population health improvement. Sustainable Cities Soc, 75:103298. https://doi.org/10.1016/j.scs.2021.103298

Nazarnia, M., Zarei, F., & Roozbahani, N. (2023). A mobile-based educational intervention on media health literacy: A quasi-experimental study. Health Promotion Perspectives, 13(3): 227.

Tesfa, G. A., Yehualashet, D. E., Ewune, H. A., Zemeskel, A. G., Kalayou, M. H., & Seboka, B. T. (2022). eHealth literacy and its associated factors among health professionals during the COVID-19 pandemic in resource-limited settings: Cross-sectional study. JMIR Formative Research, 6(7), e36206.

Yang, J. (2021). Combating pandemic: An exploration of social media users’ risk information seeking during the COVID-19 outbreak. Journal of Risk Research, 25(10): 1190–1212. https://doi.org/10.1080/13669877.2021.1990112

This presentation reports on preliminary results from a PhD study into the information experiences (Gorichanaz, 2020) of female engineering undergraduates in the UK. The study uses a phenomenological approach to examine how the lived experience of being a woman in an engineering classroom interacts with and shapes women’s information experiences.

Women make up 20% of undergraduate engineering and technology students in the UK (Higher Education Statistics Authority, 2023). Previous research has suggested that minoritised groups may have unique information experiences compared with the majority (see for example Louvier & Innocenti, 2022; Smeaton et al., 2017). The PhD research from which this presentation is drawn focuses on the information experiences of women engineering undergraduates within their highly masculine learning environment.

The study uses qualitative methods to examine women’s information experiences from a reflective lifeworld perspective (Dahlberg et al., 2008). Pilot data was collected from October-December 2024, and the study entered its main data collection phase in January 2025. Data collection methods include multimedia diaries and semi-structured interviews. Data from the diaries and interviews is being analysed using thematic analysis (Braun & Clarke, 2022).

Very early analysis of the data suggests themes including the use of shared documents as a collaborative information strategy, and the influence of affective needs such as social belonging and self-confidence. Information sharing is often neglected in engineering student work (Fosmire, 2017), however the women in the pilot study appeared to take a leading role on this form of information behaviour. The pilot study participants also all reported a preference for seeking information alone and from non-human sources, due to various affective needs including wanting to appear knowledgeable. The influence of affect and emotion is an under-studied aspect of academic information literacy (Hewitt, 2023), so this may present a valuable perspective.

Preliminary results of the study will be presented, as well as reflections on the methodology and analysis process. Conference attendees will gain an insight into the information experiences of women in engineering education, and the use of qualitative methods to explore information experiences from phenomenological perspective.

References

Braun, V., & Clarke, V. (2022). Thematic Aanalysis: A practical guide. SAGE.

Dahlberg, K., Dahlberg, H., & Nystrom, M. (2008). Reflective Lifeworld Research. Studentlitteratur.

Fosmire, M. (2017). Making informed decisions: The role of information literacy in ethical and effective engineering design. Theory into Practice, 56(4): 308–317. https://doi.org/10.1080/00405841.2017.1350495

Gorichanaz, T. (2020). Information Experience in Theory and Design. Emerald Publishing Limited.

Hewitt, A. (2023). What role can affect and emotion play in academic and research information literacy practices? Journal of Information Literacy, 17(1): 120–137. https://doi.org/10.11645/17.1.3311

Higher Education Statistics Authority. (2023). What do HE students study? Retrieved 22 August, 2025 from https://www.hesa.ac.uk/data-and-analysis/students/what-study

Louvier, K. L., & Innocenti, P. (2022). A grounded theory of information exclusion and information inclusion: Framing the information experience of people seeking asylum. Journal of Documentation, 79(2): 468–486. https://doi.org/10.1108/JD-04-2022-0077

Smeaton, K., Bruce, C. S., Hughes, H., & Davis, K. (2017). The online life of individuals experiencing socioeconomic disadvantage: How do they experience information? Information Research: An International Electronic Journal, 22(3). Retrieved 22 August, 2025 from https://eric.ed.gov/?id=EJ1156399

Introduction and Methodology

This paper presents selected results of the ongoing research on information behavior concerning climate change in the Czech Republic based on the Risk Information Seeking and Processing Model (RISPM) created by Griffin et al. (1999). The authors developed the model to complement existing research, which focused mainly on risk communication, exposure, and response, by emphasizing the role of information seeking and processing in shaping individuals’ risk perception and behavior. The model is particularly relevant for impersonal risks like climate change (Kahlor et al., 2006). As misinformation often surrounds climate change, it is essential to understand how people seek and process related information to support informed decisions and climate action (Yang et al., 2014).

Prior research suggests that information sufficiency, perceived information gathering capacity, and relevant channel beliefs influence how individuals seek and analyze risk information (Griffin et al., 1999; Kahlor et al., 2006). By examining how gender, age, education, and income relate to key components of RISPM, this study aims to describe information seeking and processing patterns within different demographic groups. A quantitative online survey (n=1,000) was conducted. The data were analyzed using SPSS Statistics, applying t-tests, correlations, and regression models to examine relationships between demographic factors and RISPM components. The sample consisted of 48.8% men and 51.2% women, with the largest age group being 65 years old and older. Most respondents had secondary education. The median monthly net income was 25,001-30,000 CZK (approx. 1,100-1,300 USD).

Results and Conclusion

The results indicate that sociodemographic factors have a limited impact on information sufficiency and the ability to locate and understand climate change information. Gender remains the only variable significantly predicting perceived information sufficiency with women demonstrating higher mean values (M=20.02) than men (M=10.08), a difference confirmed by a t-test (p<0.001). Age, education, and income are not key factors and these groups’ perceptions of information gathering capacity are consistent. Differences in the evaluation of information channels were observed: where younger and less educated respondents and women assigned greater importance to social networks (M=3.74) and close surroundings (M=5.12) compared to other groups.

The significant gender differences in perceived information sufficiency align with previous research (e.g., Yang et al., 2014), suggesting that women perceive climate change as a more significant threat and seek more information. However, the limited effect of other sociodemographic variables challenges assumptions (e.g., in Kahlor et al., 2006) that age or education strongly determine individuals’ ability to locate and process climate-related information, suggesting that other factors may play a more significant role. As part of an ongoing research project, further analyses will explore additional components of RISPM and describe differences in behavior between demographic groups, thus contributing to the ongoing international debate and a new approach to researching environmental issues. These findings also provide valuable insights for climate communication strategies and information literacy programs, emphasizing the need for targeted approaches based on demographic variations in media preferences and perceived information sufficiency.

References

Griffin, R. J., Dunwoody, S., & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80(2): 230–245.

Kahlor, L. A., Dunwoody, S., Griffin, R. J., & Neuwirth, K. (2006). Seeking and processing information about impersonal risk. Science Communication, 28(2): 163–194.

Yang, Z. J., Rickard, L. N., Harrison, T. M., & Seo, M. (2014). Applying the risk information seeking and processing model to examine support for climate change mitigation policy. Science Communication, 36(3): 296–324. https://doi.org/10.1177/1075547014525350

As a result of rapid development of generative artificial intelligence tools (AI tools) and their widespread use, ability to effectively communicate, critically evaluate and effectively collaborate with AI has become an essential skill (Long and Magerko, 2020; Ng et al., 2021). Conversely, although AI-based technologies have become pervasive, their users have been left ill-prepared to comprehend, utilize, and critically engage with AI (Wilton et al., 2022). Following the conclusions made by Pinski and Benlian (2024), and Cox (2024), who claim that different AI user groups have different AI literacy requirements, we argue that there is need to address the subject of AI literacy in the higher education context. This paper presents the results of systematical critical literature review and findings uncovered through subsequent comprehensive thematic analysis of selected scientific articles.

Using the PRISMA instrument (Page et al., 2021) for construction of samples for systematic review, we retrieved a sample of 1,590 records by submitting following query (TITLE-ABS-KEY (artificial AND intelligence AND literacy) OR TITLE-ABS-KEY (ai AND literacy) AND TITLE-ABS-KEY (university*)) to three databases: Google Scholar, Scopus, and Web of Science. Out perceived outcomes are: to learn what particular aspects of AI literacy have been researched in higher education context so far and to what extent; to determine possible directions for future AI literacy research in the context of higher education; and, to contribute in establishing a conceptual framework for future AI literacy research.

References

Cox, A. (2024). Algorithmic literacy, AI literacy and responsible generative AI literacy. Journal of Web Librarianship, 18(3), 93–110. https://doi.org/10.1080/19322909.2024.2395341

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, (pp. 1–16). https://doi.org/10.1145/3313831.3376727

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71

Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062

Wilton, L., Ip, S., Sharma, M., & Fan, F. (2022). Where is the AI? AI literacy for educators. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova, (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, vol. 13356 (pp. 180–188). Springer International Publishing. https://doi.org/10.1007/978-3-031-11647-6_31

Media bias is an enduring feature of news dissemination, reflecting the subjective perspectives of its creators across history. From archaic records like “The Victory Stele of Naram-Sin” to contemporary news channels, bias permeates media, influencing political, social, and public health narratives. This study investigated the persistent phenomenon of media bias and the potential of large language models (LLMs) (Kojima et al., 2022) in its detection and classification, in order to deploy publicly available software tools aiming to enhance media literacy among news consumers.Traditionally, media bias served the interests of ruling powers; even with the rise of modern journalism, objectivity is often compromised by commercial pressures and inherent human biases. (Rodrigo-Ginés et al., 2024). As media landscapes evolve, bias continues to shape public opinion, impacting democratic processes and public health perceptions. This wasevident during the COVID-19 pandemic where polarized media narratives swayed public health decisions and fueled misinformation (Recio-Román et al., 2023).Current research on the effects of labeling media bias or propaganda, whether automatically or with human involvement, highlights the complexity of the issue. Depending on different circumstances, labeling can lead to negative outcomes (such as reinforcing filter bubbles by providing means to avoid news with a different perspective), no change in news consumption behavior at all, or, in some cases, an actual improvement in media literacy as intended (Zavolokina et al., 2024).

This research aims to develop a technical solution for the automatic labeling of biased media content, emphasizing several proposals that we hope will lead to a positive effect on media literacy among those presented with the system’s assessments. These proposals include using a fine-grained taxonomy of bias types rather than a simple binary left/right labeling. Thus, we focused on detailed explanations for each model decision in natural language, marking bias at the sentence level rather than at the article or publication level. That provided more insights, fine-tuning autoregressive models like GPT-3.5 or Mistral with high-quality examples instead of using “simple” bidirectional models like BERT(Brown et al., 2020) or non-finetuned models, and focusing on the German language, which has not yet been properly explored for such systems.Understanding readers’ perceptions when exposed to bias-labeled content is another facet of this research. It explored how bias labeling influences readers’ views on credibility and neutrality and whether real-time bias indicators affect news consumption behaviors. As mentioned, practical applications served as a cornerstone of this research. One aim was to implement bias detection systems in real-world settings, such as search engines and news aggregators, to promote balanced information consumption. The development of user tools, like browser extensions highlighting media bias, intends to address public need for transparent information evaluation. In essence, this research contributed to media literacy enhancement by demystifying media bias through advanced computational methods. By refining detection mechanisms, classifying bias more effectively, and implementing practical tools, it aimed to fortify democratic discourse and public understanding, thereby addressing the pervasive influence of media bias in today’s interconnected world.

References

Brown, T. B., et al. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems, vol. 33 (pp. 1877–1901). Curran.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems, vol. 35 (pp. 22199–22213). Curran.

Recio-Román, A., Recio-Menéndez, M., & Román-González, M. V. (2023). Influence of media information sources on vaccine uptake: The Full and inconsistent mediating role of vaccine hesitancy. Computation. Basel. https://doi.org/10.3390/computation11100208

Rodrigo-Ginés, F.-J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.