The public release of ChatGPT in November 2022 marked a watershed moment for teaching in the humanities, as Large Language Models (LLMs) suddenly demonstrated text generation capabilities indistinguishable from human writing. Using biblical studies as a case study, this paper examines how these tools challenge core pedagogical practices across text-based disciplines. The article offers a look into the ‘black box’ of LLMs through hands-on demonstrations with OpenAI’s playground, revealing their nature as probabilistic systems where the next word is selected from weighted possibilities rather than retrieved deterministically. Understanding this proves crucial for students and educators, explaining phenomena like ‘hallucinations’ and exposing why treating LLMs as citable sources fundamentally misunderstands what they are. Having established how LLMs function, the article examines three areas where these tools challenge traditional teaching in biblical scholarship – and by extension, the humanities: increasingly sophisticated AI translations question the value of intensive language training; LLMs that process entire books in seconds make existing reading practices appear inefficient; and AI-generated content increasingly rivals scholarly writing. Rather than dismissing LLMs as “stochastic parrots” or embracing them uncritically, the article proposes a balanced response preserving essential practices – particularly slow reading and writing as critical thinking tools – while leveraging LLMs as dialogue partners and research assistants. The humanities’ future vitality depends on articulating why human engagement with texts remains irreplaceable, even as these tools are integrated into educational frameworks. Biblical studies thus offers a compelling test case for how humanities disciplines must reimagine their teaching – not through isolated policy adjustments but through fundamental reconsideration of core competencies in an AItransformed landscape.
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Between Ancient Texts and Large Language Models
Semantic Scholar · 2026
Abstract
The public release of ChatGPT in November 2022 marked a watershed moment for teaching in the humanities, as Large Language Models (LLMs) suddenly demonstrated text generation capabilities indistinguishable from human writing. Using biblical studies as a case study, this paper examines how these tools challenge core pedagogical practices across text-based disciplines. The article offers a look into the ‘black box’ of LLMs through hands-on demonstrations with OpenAI’s playground, revealing their nature as probabilistic systems where the next word is selected from weighted possibilities rather than retrieved deterministically. Understanding this proves crucial for students and educators, explaining phenomena like ‘hallucinations’ and exposing why treating LLMs as citable sources fundamentally misunderstands what they are. Having established how LLMs function, the article examines three areas where these tools challenge traditional teaching in biblical scholarship – and by extension, the humanities: increasingly sophisticated AI translations question the value of intensive language training; LLMs that process entire books in seconds make existing reading practices appear inefficient; and AI-generated content increasingly rivals scholarly writing. Rather than dismissing LLMs as “stochastic parrots” or embracing them uncritically, the article proposes a balanced response preserving essential practices – particularly slow reading and writing as critical thinking tools – while leveraging LLMs as dialogue partners and research assistants. The humanities’ future vitality depends on articulating why human engagement with texts remains irreplaceable, even as these tools are integrated into educational frameworks. Biblical studies thus offers a compelling test case for how humanities disciplines must reimagine their teaching – not through isolated policy adjustments but through fundamental reconsideration of core competencies in an AItransformed landscape.