This panel investigates how artificial intelligence (AI) transforms religious scholarship and practice through collaborative human-machine engagement. Centering ethical and methodological challenges, the papers collectively explore how AI tools—from retrieval-augmented generation to reasoning models—mediate issues of representation, interpretation, and agency in religious contexts. Key themes include the necessity of human oversight in mitigating AI biases, particularly in amplifying marginalized voices (e.g., women in religious history, womanist visual culture) and preserving theological nuance in cross-cultural translation. While AI offers novel possibilities—generating content, simulating historical figures, or enhancing interpretive frameworks—the research underscores questions of transparency, cultural sensitivity, and ethical responsibility permeate discussions. Together, the panel highlights AI’s potential to expand religious inquiry while advocating for frameworks that prioritize equity, accountability, and interdisciplinary collaboration, balancing technological innovation with critical humanistic reflection.
This paper describes an experiment to generate stub articles about women religious leaders using a purpose-built artificial intelligence system as a means to address gender imbalances on Wikipedia. The Women in Religion User Group is an officially recognized Wikimedia Movement Affiliate that “seeks to create, update, and improve Wikipedia articles pertaining to the lives of cis and transgender women scholars, activists, and practitioners in the world's religious, spiritual, and wisdom traditions.” (Women in Religion 2025) In the early stages of the project, we explored the use of retrieval augmented generation (RAG) to improve the veracity of the stubs that the LLM generated. In the current phase of the project, we are fine-tuning an open-source large language model to improve its ability to create Wikipedia stubs. After reviewing these techniques, we discuss their effectiveness while also raising ethical questions about releasing our project in open source.
In the summer of 2024, AI development appeared to stagnate with delays in major model releases and concerns about training data limitations. However, the emergence of Deepseek's reasoning model, deepseek-r1, revolutionized AI research by introducing a new "Reasoning Space" component. Unlike traditional transformer LLMs, which operate as black boxes producing token-by-token responses, reasoning models provide transparency into their decision-making process through accessible "reasoning traces."
This advancement enables researchers to examine how AI systems arrive at their conclusions. Building on this technology, this project aims to create AI instances replicating different Gospel versions of Jesus (Mark, Matthew, Luke, John, and Thomas) using reasoning models. The goal is to analyze how these AI representations think differently and whether their reasoning can evolve through interaction with each other. This research could potentially extend to dialogues with other AI-simulated historical or philosophical figures, offering new insights into AI reasoning and simulation capabilities.
This study examines the role of human agency in AI-assisted translation, focusing on how the selection of reference databases influences the translation of Classical Chinese Buddhist texts. Using Retrieval-Augmented Generation (RAG) with the Deepseek R1 model, the research evaluates how consciously selected knowledge sources impact translation accuracy and doctrinal interpretation. The study applies this methodology to the Pumenpin chapter of the Fahuajing, analyzing multiple translation iterations with varying reference materials. Evaluation is conducted against authoritative translations to assess accuracy, doctrinal nuance, and interpretive biases. The study demonstrates that AI does not replace human expertise but instead requires active engagement in selecting reference sources, which fundamentally shape translation outcomes. This research establishes a methodological framework for AI-assisted religious text translation, emphasizing the necessity of human oversight to maintain theological coherence while leveraging computational advancements. It highlights the evolving role of translators in curating AI inputs rather than merely post-editing outputs.
This session explores the intersection of artificial intelligence (AI), womanist visual culture, and the theo-moral imagination, as conceptualized by AnneMarie Mingo. By examining how AI can analyze and generate visual narratives that reflect womanist visual culture, this research aims to illuminate new dimensions of agency and moral responsibility in religious contexts. Through a critical lens, we will discuss how AI-driven visual narratives can both enhance and complicate notions of freedom, particularly in terms of representing marginalized voices. By integrating AI experiments with womanist theology, this session will highlight the potential of AI to amplify the theo-moral imagination that guides social activism and justice movements, while emphasizing the need for culturally sensitive AI practices that respect diverse religious narratives.
This title and abstract incorporate Mingo's concept of theo-moral imagination, emphasizing its role in guiding the ethical use of AI in womanist visual culture to promote freedom and agency.