John the Baptist Through the Lens of Generative AI : A Narrative and Reception-Historical Analysis of Mark 1

Abstract: This thesis addresses the intersection of reception history in biblical studies, Generative Artificial Intelligence (GAI) and phenomenology. Three images, from text prompts using different English translations of Mark 1:1–8 (KJV, NRSV and NIV) have been generated by GAI. In addition to the three translations, a more encompassing body of information, based on exegetical analysis, reception history and recent scholarly literature on John the Baptist and Mark 1, was also provided. Mark 1 is analyzed using narrative criticism with special focus on John the Baptist. Current research on the historical John is discussed, alongside interpretations of John from Late Ancient Christian Sources seen from a phenomenological perspective.  Traditionally, interpreting biblical art and text has assumed an artist portraying a narrative reading using methods such as visual exegesis. With GAI, this has changed moving the artist from the canvas to the text prompt. It puts the biblical text in a direct causal connection to the created image. Previously the artist had to decide when the image was finished but with GAI the decision is about which image to keep. The purpose of the image becomes a focal point. Images created with this modern technology can be relevant in at least two regards. First, they do represent a new type of biblical art. Second, the iterative process itself is a novel approach to studying and interacting with the Bible. Challenges exists, such as a bias towards Western/American cultural, sociological, and economical values. Data scientists and mathematicians are determining the probabilistic models without problematizing the content. Ethical questions in this field need to be addressed. GAI learning from AI-produced data – instead of human data – will likely become an issue, thus reinforcing existing biases and prejudices further.