IPILM is a learning environment that promotes collaborative knowledge construction among students from diverse cultural backgrounds. Educators and learners from various countries take part in an intercultural learning endeavor.
The importance of AI is steadily growing globally and across all areas of social life, and LLMs such as ChatGPT are increasingly being used to obtain information on a wide variety of topics. In this year’s summer workshop on “Intercultural Perspectives on Information Literacy and Metaliteracy” (IPILM), student groups compared ChatGPT outputs across languages and countries on culturally sensitive topics. The question was whether ChatGPT produces culturally divergent narratives depending on prompt language and country. The results will be presented in a public session on Friday, 31th July 2026, 2 – 4.30 PM CET.
Students from Austria, Germany, India and the United States of America conducted the research and analysis for the following topics:
Students who are eager to learn about topics related to AI Literacy, Information literacy and Metaliteracy in an intercultural learning scenario.
Course dates: July 20, 2026 – July 31, 2026
The scenario: In this workshop, you will conduct a cross-cultural comparison study of AI-generated information in different languages and national contexts. Drawing on concepts of information literacy and metaliteracy, you will collect, evaluate, and compare AI responses to identify similarities, differences, and their implications for information practices.
The learning tasks
AI and Political Misinformation / Elections
AI and Migration / Refugee Policy
AI and Healthcare / Medical Misinformation
AI and Climate Change Policy
AI and Digital Surveillance / Data Privacy
AI and Digital Media Arts
AI and Warfare
Learning requirements
✓ Most importantly, be curious and open-minded
✓ English language proficiency: The course is fully held in English
✓ A substantial time commitment is expected
✓ Authentic and respectful way of communicating
Participation is not guaranteed, as the number of places is limited.
The slides for the keynote “Mindful Metaliteracy in the Age of Generative AI: Attention, Reflection, and Human Agency” (Nicola Marae Allain) from our last online conference are online now. You can find them here.
Embedded Youtube-Screencast: “IPILM 2025: AI and value propositions for stakeholder groups”.
Don’t have time to watch? No problem!
Session report and key aspects:
The presentation started with a brief introduction to AI and generative AI, followed by the section on roles of AI. There, three potential areas were introduced: customer experience, talent management and productivity, as well as risk management and governance.
AI-generated with ChatGPT
Particular attention was given to introducing the stakeholder groups: individual, organizational, and national and international stakeholders. The interest and power structure of each group was introduced, and showed their connectivity and interdependence.
AI-generated with ChatGPT
The perceived value of AI for individual stakeholders depends on life stage and occupational status, aligning with the distinct priorities of each group (UN Human Development Report 2025).
Used in the right way, AI may offer an opportunity to expand human capabilities. Institutional and social choices can enable AI to expand people’s capabilities and agency, as illustrated through AI’s applications for people with disabilities, care systems and gender equality, as well as in conceptualizing and mitigating AI bias. The following section on value propositions presented potential positive values.
AI-generated with ChatGPT
A part of the presentation also addressed several risks connected to AI. With a critical eye, we highlighted the risk of algorithmic bias, AI’s environmental impact, data privacy breaches, lack of transparency and cognitive debt.
AI-generated with ChatGPT
Discussion
This section addresses the key questions raised during the conference and summarizes the main points of discussion among participants.
The group was asked about their individual views on the personal value of AI tools. A variety of answers was given: while there were participants who did not view AI as particularly valuable, other participants saw great personal value in using the tools. Depending on the use cases and tasks, the majority saw a positive value.
Referencing the keynote speech on mindfulness, an open-ended question wanted the participants to share if they thought of AI as an inevitable feature and if humans would lose innate skills through the use of AI. This question sparked a discussion with different directions and focal points. The art of photography was named as an example for all three issues and sparked a dynamic discussion.
Another question wanted the participants to share their views on whether AI literacy should be introduced at an earlier age. The participants discussed that other types of literacy skills were introduced during childhood to prepare children at an early age and continually increase their skill set. The group members came to the conclusion that it should be similar with AI literacy education.
Conference Session Report: 7th IPILM-Conference on 11.12.2025
Original illustration, AI-generated with ChatGPT (OpenAI).
What happens when artificial intelligence becomes part of mental health care, and how should we deal with its risks and responsibilities?
Key Focus of the Session
AI as a support tool in mental health
Benefits: Early detection, accessibility, continuous support
Risks: Data protection, bias, transparency
Cultural and social contexts shaping perceptions, use, and risks of AI
Importance of information literacy and meta literacy
Building on these focal points, the Session examined the potential and limitations of artificial intelligence in the field of mental health from an information literacy and metaliteracy perspective. Drawing on a systematic literature review and concept mapping, it showed that AI-based applications can offer advantages, particularly with regard to early detection, continuous support, and low-threshold accessibility. These findings were largely consistent across the reviewed literature and were primarily informed by two key studies that shaped the session. Scientific evidence on the effectiveness and acceptance of AI-based mental health applications was mainly drawn from Dehbozorgi et al. (2025 – Read More). In contrast, ethical, cultural, and epistemic risks, such as data protection concerns, algorithmic bias, and limited transparency, were largely informed by the ethical review of Saeidnia et al. (2024 – Read more). The international survey largely reflected and reinforced the risks discussed in these studies, while also illustrating how these issues are perceived in practice. Overall, the findings emphasized that AI in mental health contexts should primarily be understood as a complementary tool to human expertise and that well-developed information literacy and metaliteracy are essential for responsible use.
❗️Below are a few selected examples of mental health services that incorporate AI-based support tools.
The examples illustrate current applications of AI in mental health and are not intended as recommendations.
Cultural and Ethical Considerations
Mental health is deeply shaped by cultural norms, social stigma, and structural inequalities, an aspect that was central to the intercultural perspective of the session and the conference as a whole. These factors also influence how AI-based systems are developed and used. AI applications risk reinforcing existing disparities through biased data, data poverty, and predominantly Western-centered models of mental health. Ethical challenges such as privacy, autonomy, and emotional adequacy are therefore particularly intensified for vulnerable and marginalized groups, highlighting the need for culturally sensitive and ethically grounded AI design.
Discussion
The discussion focused in particular on questions of responsibility. A majority of participants attributed responsibility for potentially harmful or misleading AI-based advice primarily to the providing companies, indicating a strong demand for institutional safeguards while simultaneously raising questions about the role of user responsibility. From an information literacy and metaliteracy perspective, this highlights the importance of enabling users to critically assess AI-based systems, understand their limitations, and recognize potential risks. At the same time, individual awareness alone cannot replace structural responsibility, especially in light of asymmetrical power and knowledge relations between providers and users, as well as the vulnerability of mental health contexts.
Another key point concerned the ambivalent level of trust in AI within mental health applications. Although many participants expressed general openness toward the use of AI, trust remained limited due to concerns about data protection, reliability, and the quality of AI-generated advice. Increasing trust was found to depend on transparent system design, strong data protection measures, explainable decision-making processes, and the clear integration of AI into human-supported care structures. Overall, the discussion suggests that trust in AI is shaped less by technological performance alone than by ethical design, cultural sensitivity, and informed and reflective practices of use.
Key Takeaways
AI can meaningfully support mental health care, but its value depends on ethical design, cultural sensitivity, and human oversight.
Users tend to view AI as a supportive tool rather than a replacement for professional care, while concerns about privacy and trust remain strong.
Cultural context plays a significant role in shaping how AI-based mental health services are perceived and used.
Strong information literacy and metaliteracy are essential for enabling critical, informed, and responsible engagement with AI in mental health contexts.
The following publications provide further insights into the scientific, ethical, and informational dimensions of AI in mental health contexts.
Dehbozorgi, R., Zangeneh, S., Khooshab, E. et al. The application of artificial intelligence in the field of mental health: a systematic review. BMC Psychiatry25, 132 (2025). https://doi.org/10.1186/s12888-025-06483-2
Li, H., Zhang, R., Lee, Y. C., Kraut, R. E., & Mohr, D. C. (2023). Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. NPJ digital medicine, 6(1), 236. https://doi.org/10.1038/s41746-023-00979-5
Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024). AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories. Perspectives on psychological science : a journal of the Association for Psychological Science, 19(5), 808–826. https://doi.org/10.1177/17456916231214460
Saeidnia, H. R., Hashemi Fotami, S. G., Lund, B., & Ghiasi, N. (2024). Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact. Social Sciences, 13(7), 381. https://doi.org/10.3390/socsci13070381
