Artificial intelligence (AI) is disrupting how knowledge is produced and has to potential to influence how leadership is enacted. Yet dominant AI infrastructures remain embedded within Eurocentric epistemologies (Ricaurte, 2022), linguistic hierarchies, and colonial data regimes (Zimeta, 2023). As a result, it reproduces global inequalities, contributing to the marginalisation of Indigenous, Global South, and subaltern ways of knowing in digital spaces (Baradaran, 2024). Within peace and leadership studies, this epistemic imbalance risks community-based and culturally grounded leadership practices.

The Critical AI, Peace, and Leadership (CAPL) research group seeks to critically reimagine AI as a tool for inclusive leadership, and peacebuilding. Hence, the group will explore how AI can support the development of peace ontologies in underrepresented communities, bridging local knowledge systems with computational models. The group will explore how AI can documentation and digital representation of Indigenous understandings of peace and leadership. A central focus will be on identifying an Indigenous community in Nigeria exhibiting gender diglossolalia (Adegoke & Alvarez, 2025), whose language has not yet been mapped or modelled for AI applications. Working closely with local scholars, community leaders, and the Ubang community, the group will generate semantic ontologies and knowledge representations that capture indigenous conceptualisations of peace, conflict resolution, and social cohesion. This work will prepare the language and knowledge system for further AI capture, facilitating ethical and culturally grounded AI interventions in governance, conflict prevention, and social development.

By integrating interdisciplinary expertise from education, AI ethics, and leadership studies, CALP advances a participatory and decolonial framework for engaging with AI. Rather than extracting knowledge for technocratic purposes, the group positions communities as epistemic agents and co-creators. Through this approach, the research group seeks to reshape debates on peace, AI and leadership, while generating policy-relevant and pedagogically transformative insights for Global South contexts.

Core objectives

The research group pursues three interrelated objectives:

a. To advance decolonial critiques of AI by centring Indigenous pedagogies, linguistic systems, and community-based epistemologies

b. To develop participatory methodologies for constructing peace and leadership ontologies that can interface with AI systems without reproducing extractive research practices.

c. To generate empirically grounded insights into how Indigenous peace education practices shape inclusive leadership practices.

Key research strands

1. Indigenous Peace Pedagogies and Knowledge Transmission
Mapping how peace, reconciliation, authority, justice, and belonging are taught and learned within Indigenous peoples and underrepresented communities.

2. Gender, Language, and Educational Authority
Investigating gendered diglossolalia and its implications for pedagogy, leadership, epistemology, and peace conceptualisations.

3. AI and Decolonial Knowledge
Advancing debates on data sovereignty, algorithmic bias, and community ownership.

 

References

Adegoke, D., & Alvarez, G. R. (2025). Peace ontologies, narratives, and epistemes among indigenous communities of Nigeria and Bolivia. Frontiers in Political Science, 7, 1502731. https://doi.org/10.3389/fpos.2025.1502731

Baradaran, A. (2024). Towards a decolonial I in AI: Mapping the pervasive effects of artificial intelligence on the art ecosystem. AI & SOCIETY, 39(1), 7–19. https://doi.org/10.1007/s00146-023-01771-5

Ricaurte, P. (2022). Ethics for the majority world: AI and the question of violence at scale. Media, Culture & Society, 44(4), 726–745. https://doi.org/10.1177/01634437221099612

Zimeta, M. (2023, October 3). Why AI must be decolonized to fulfill its true potential. Chatham House. https://www.chathamhouse.org/publications/the-world-today/2023-10/why-ai-must-be-decolonized-fulfill-its-true-potential