Artificial intelligence (AI)

Cultivating epistemic humility in research teams

By Faye Miller.

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Faye Miller (biography)

What makes it so challenging for research teams to be truly receptive to being wrong? And what can teams do to make doubt expressible and useful?

Being aware that knowledge is always situational, incomplete, and prone to error, as well as the willingness to hold opinions tentatively, be receptive to change, and recognise the boundaries of understanding, are all components of epistemic humility. Humility about what one knows and can know is an intellectual quality in individual researchers. Yet epistemic humility also has a structural dimension: which doubts get expressed, whose knowledge is heard, and how teams handle what they don’t yet know, are challenges that go beyond the individual researcher to shape how research teams function.

This i2Insights contribution is an attempt to highlight two challenges that need to be addressed:

  • the confidence trap, built on the pressure to exude certainty, and
  • the silence trap, arising from the social dynamics that can suppress productive doubt.

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A framework for navigating the impact of using artificial intelligence on collaborative research communication

By Faye Miller.

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Faye Miller (biography)

How can research teams recognise when their use of artificial intelligence is affecting their ability to integrate different knowledge and perspectives? How can they navigate the impact of artificial intelligence on their collaborative processes?

When research teams use artificial intelligence in collaborative work, new complexities emerge, especially subtle shifts in communication patterns that can fundamentally alter how teams integrate different perspectives and knowledge forms. Consider an environmental team relying on artificial intelligence summaries across hydrology, ecology, and policy. They might miss crucial disciplinary nuances, or follow its “evidence-based” recommendations that may clash with community priorities.

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Tenth annual review

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Gabriele Bammer (biography)

By Gabriele Bammer.

This is the tenth annual “state of the blog” review.

What are the major achievements of i2Insights for 2025? What have been the main themes of the contributions made? How do these reflect the aims of i2Insights?

This is the last post for 2025. We’ll be back on January 13, 2026 and already have a number of great contributions to start the new year.

Achievements

We celebrated three major achievements in 2025.

1. Our 10th anniversary

In November i2Insights marked its 10th birthday as a global, comprehensive, living toolkit.

We are particularly delighted that INTEREACH (Interdisciplinary Integration Research Careers Hub) is devoting its 2025-2026 webinar series to spotlighting themes from i2Insights.

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Transdisciplinary research with and for artificial intelligence

By Florian Keil, Melina Stein and Flurina Schneider.

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1. Florian Keil’s biography
2. Melina Stein (biography)
3. Flurina Schneider (biography)

Is artificial intelligence, a technology aggressively advertised as the ultimate cure-all, fundamentally incompatible with transdisciplinarity and its decades-old insight that the “wicked” problems of the real world do not lend themselves to one-dimensional solutions? Should transdisciplinary research outright reject a technology that is already undermining efforts to achieve social and environmental justice? Or can artificial intelligence actually support transdisciplinary research when used responsibly?

Using artificial intelligence in transdisciplinary research requires a critical mindset

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Five capacities for human–artificial intelligence collaboration in transdisciplinary research

By Faye Miller

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Faye Miller (biography)

How can transdisciplinary researchers work with artificial intelligence as a genuine collaborator while maintaining integrative thinking? What new capabilities should be developed to ensure that artificial intelligence enhances, rather than fragments or compromises, cross-disciplinary human insights? 

What can trandisciplinary researchers learn from human–artificial intelligence collaboration across disciplines?

Before moving on to capacities, let’s examine the growth in human–artificial intelligence research partnerships to see what lessons can be adapted by transdisciplinary researchers in their work. In particular, I suggest that integrative methodologies can be developed by understanding what is happening within the following domain-specific approaches.

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Three key strategies enabling artificial intelligence to bridge inequities

By Kerstin Nothnagel.

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Kerstin Nothnagel (biography)

With artificial intelligence transforming many aspects of society, from healthcare to education to economic development, how can it be used to reduce rather than perpetuate inequalities? In particular, given that artificial intelligence can widen gaps by exacerbating existing inequalities through biased datasets, lack of infrastructure, and limited access to resources, how can the benefits of artificial intelligence be brought into the reach of low-income nations and marginalised communities? What practical steps can be taken to ensure artificial intelligence is developed and applied in a way that is inclusive and benefits everyone?

My work has been in the health field, but the findings are likely to be more broadly applicable. I suggest three strategies that would enable artificial intelligence to reduce inequities. The first two are key contributions that researchers can make. The third is a call to policy makers and funders. An example is provided for each strategy.

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