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.
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
AI can provide emotional comfort and a sense of social presence.
Users often perceive AI as non-judgmental and constantly available.
Emotional attachment to AI is possible, but true reciprocity is missing.
AI tends to replace functional roles rather than deep emotional relationships.
AI complements human relationships but does not replace them.
AI-generated image created using ChatGPT (DALL·E), 2026.
Introduction
This session aimed to provide an initial overview of the question of whether artificial intelligence can function as a substitute for human relationships. With AI tools increasingly used not only for information retrieval but also for emotional and social interaction, this topic has gained growing relevance in both academic research and everyday life.
The presentation combined a theoretical perspective, based on key findings from current scientific literature, with a practical approach. While the theoretical part outlined how human–AI relationships are conceptualized and evaluated in research, the practical component presented an exploratory survey to illustrate a possible research approach and to identify early tendencies in user experiences.
Summary
The presentation focused on two key academic studies and an exploratory survey to examine how AI may take on social and emotional roles traditionally associated with human relationships. The presented studies are: Brandtzaeg et al. (2022) showed that users candevelop friend-like attachments to social chatbots, perceiving them as safe and non-judgmental, while emphasizing the lack of reciprocity and emotional depth. Smith et al. (2025) further highlighted that although generative AI can convincingly simulate emotional responsiveness, it lacks key psychological components of genuine human connection, such as mutuality, shared experience, and emotional depth, which limits its ability to fully replicate human relationships. In addition, an exploratory online survey was conducted to demonstrate a possible research approach and to identify initial tendencies, such as emotional comfort, functional role substitution, and perceived non-judgment. Further details on the survey design, sample characteristics, and key findings are presented in the screencast linked below.
Discussion: Questions, Answers, and Reflections
During the discussion, a few questions focused on the methodology of the survey and the validity of its results. Participants critically addressed the small and non-representative sample. In response, it was emphasized that the survey was intended as an exploratory approach rather than a source of generalizable conclusions. Its purpose was to illustrate how human–AI relationships can be empirically examined and to reveal early tendencies that may guide future research. These included the frequent use of AI for emotional comfort, the perception of AI as less judgmental than humans, and the limited replacement of human roles.
Another discussion point concerned whether and how emotionally responsive AI systems should be regulated. It was debated whether emotional support provided by AI should be restricted and, if so, how “too emotional” AI could be defined. While arguments for regulation often focus on preventing emotional dependence, potential benefits are also emphasized, particularly AI’s role as a low-threshold form of support for individuals experiencing loneliness or social anxiety.
Finally, the discussion addressed broader opportunities and risks. Opportunities included availability, emotional relief, and reduced social pressure, whereas risks centered on privacy concerns, emotional dependence, and the potential weakening of real-life social relationships. Overall, the discussion underscored the need for continued critical reflection and interdisciplinary research on emotional AI.
Screencast
Below you can find the screencast of our presentation “AI as a Substitute for Human Relationships”, which summarizes the theoretical background and the practical insights discussed during the session.
Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI friend: How users of a social chatbot understand their human–AI friendship. Human Communication Research, 48(3), 404–429. https://doi.org/10.1093/hcr/hqac008
Smith, M. G., Bradbury, T. N., & Karney, B. R. (2025). Can generative AI chatbots emulate human connection? A relationship science perspective. Perspectives on Psychological Science, 20(6), 1081–1099. https://doi.org/10.1177/17456916251351306
Hohenstein, J., Kizilcec, R. F., DiFranzo, D., Aghajari, Z., Mieczkowski, H., Levy, K., & Jung, M. F. (2023). Artificial intelligence in communication impacts language and social relationships. Scientific Reports, 13, 5487. https://doi.org/10.1038/s41598-023-32354-5
Malfacini, K. (2025). The impacts of companion AI on human relationships: Risks, benefits, and design considerations. AI & Society. https://doi.org/10.1007/s00146-025-02318-6
Zimmerman, A., Janhonen, J., & Beer, E. (2024). Human/AI relationships: Challenges, downsides, and impacts on human/human relationships. AI and Ethics, 4, 1555–1567. https://doi.org/10.1007/s43681-023-00348-8
