AI and Bible Translation

Few mission applications of artificial intelligence carry greater promise—or higher stakes—than Bible translation.

The need is clear. Translation is difficult, specialized, and often slow. Some communities still lack Scripture in the languages they know best. AI can assist with drafting, alignment, terminology, corpus search, consistency checking, transcription, back translation, speech processing, and linguistic analysis.

The temptation is equally clear: if a system can produce a plausible translation of Scripture in seconds, why not accelerate the entire pipeline by treating generation as the main work and human review as the final safeguard? Contemporary Bible-translation practice does not offer one answer.

The First-Draft Dispute

Taeho Jang has described Scripture Forge workflows in which AI can generate what is explicitly called “Draft 0.” The designation matters. The machine output is preliminary material inside an established translation process. Translators evaluate, correct, refine, and continue through normal checking procedures.¹

Jang also emphasizes something easy to miss in optimistic narratives: manual translation experience remains important because translators need enough competence to recognize subtle errors in AI output.

This is a strong case for augmentation. AI reduces the cost of producing candidate language while qualified humans retain responsibility.

At the 2025 Bible Translation Conference, Judy Heath and colleagues presented a sharply different assessment from experiments with AI-generated first drafts of 1 Samuel and Psalms in Chadian Arabic. They reported significant problems in exegetical accuracy and naturalness and argued that the privilege and responsibility of the first draft should remain with in-culture, first-language speakers.²

Their concern is not only current model quality. It is procedural and formative. First drafting can be where translators wrestle with meaning, make interpretive decisions, develop competence, and take ownership of the text. If AI supplies the first coherent wording, reviewers may become anchored to its choices even when alternative formulations would have emerged from local-language thinking.

The disagreement is real. One side treats Draft 0 as a preliminary artifact that can accelerate a human-governed process.

The other argues that first drafting is itself an important human and community practice whose delegation can distort the process.

A responsible book should not manufacture consensus where practitioners disagree. Critics sometimes argue that AI should not draft Scripture because AI makes errors.

Humans make errors too. If error alone prohibited a drafting method, human translation would be impossible. The stronger question is how errors are generated, detected, corrected, and governed.

AI errors can be unusually fluent. They can repeat across many verses. A generated draft can anchor later reviewers. A system may reflect the domain, language, or theological biases of training data in ways translators cannot easily inspect.

Human first drafts have different weaknesses: inconsistency, fatigue, limited reference access, individual bias, and variable skill.

The comparison should therefore be procedural, not romantic. Which workflow produces the most faithful translation while strengthening local competence, community ownership, accountability, and sustainable quality?

The answer may differ by language and team. The dispute over AI Draft 0 is sometimes presented as a technical argument about quality. It is also a debate over what translators become through the act of drafting.

First drafting forces decisions before a polished alternative exists. Translators must interpret the source, search their language, negotiate ambiguity, and create formulations that emerge from local linguistic intuition.

A machine draft changes the cognitive starting point. Reviewers respond to existing wording. Even when they are free to rewrite everything, anchoring is possible.

The strength of this concern will vary by team. Highly experienced translators may resist anchoring better. Beginning translators may learn from strong candidate drafts or become dependent on them.

This is why one universal workflow is premature. Even teams skeptical of machine first drafts may find substantial value in later stages.

Terminology consistency. Named-entity checking. Verse alignment. Back translation. Comparison with reference translations. Detection of omissions or unusual patterns. Transcription and audio alignment. These uses can increase quality without displacing first drafting. The debate should therefore avoid binary categories such as “AI translation” versus “human translation.” Many mixed architectures are possible.

Quality Is a Process

The Ciyawo oral Bible-translation project in Mozambique illustrates why translation quality cannot be reduced to first-draft quality. The documented process includes repeated peer review, community checking, revision, coordinator and exegetical input, transcription work, and consultant checking.³

The important unit is the workflow. A first draft can be imperfect if the process is designed to find and correct imperfection. A fluent machine draft is not publication-ready merely because it reads well.

This suggests a mature place for AI: optional assistance beneath a clearly human-governed translation architecture.

Source study. Drafting. Peer review. Community testing. Revision. Back translation and checking. Consultant or formal review. Approval. AI may contribute to research, terminology, candidate drafting, consistency analysis, transcription, comparison, or back translation. The accountable human and community process remains above it.

Bible-translation organizations experimenting with AI should evaluate more than speed. Measure exegetical accuracy, naturalness, consistency, revision distance, reviewer confidence, time, translator learning, community acceptance, and the distribution of decision-making.

A workflow that reduces hours while increasing anchoring or weakening local ownership may not be an improvement.

Where AI Can Assist

AI performance varies enormously by language. A model that translates English and Spanish well may perform poorly in a low-resource language with little parallel data, unstable orthography, dialect variation, or primarily oral use.

The impressive general capability of a global model should therefore not be generalized to every translation project.

Evaluation must happen in the actual language. Scripture translation includes concepts whose faithful rendering requires more than lexical substitution. Terms connected to covenant, justification, holiness, sacrifice, kinship, spirit, lordship, or worship can carry networks of meaning.

AI can search candidate usages, compare existing translations, and identify consistency problems. It should not decide theological terminology independently of translators and communities who understand how the language functions.

Bible-translation projects often possess valuable corpora and linguistic resources. These may be useful for training or adapting models. Organizations should clarify permission, licensing, consent, and community interests before repurposing data for machine learning.

A corpus created for Scripture translation is not automatically ownerless technological raw material.

Figure 15.1. AI-Assisted Bible Translation Workflow

SOURCE STUDY	→	DRAFTING	→	PEER REVIEW	→	COMMUNITY TESTING
REVISION	→	CONSULTANT REVIEW	→	APPROVAL

AI may assist several stages. The authorized human and community process governs the whole.

Who Has Authority to Approve Scripture

The people who speak the language should possess meaningful authority over how the technology enters the workflow.

An external organization should not arrive with a policy that AI first drafting is mandatory because headquarters wants faster metrics. Nor should outsiders impose a blanket AI prohibition on a local team that understands the technology and sees clear benefit.

Local authority does not eliminate the need for translation standards or consultant review. It ensures that communities are not reduced to reviewers of machine-generated language produced according to someone else’s production strategy.

The simplest irresponsible workflow is: AI generates Scripture; Ministry publishes it. No serious translation methodology should collapse the process that far. The final authority for a translation must remain within a qualified, accountable human and community process appropriate to the translation tradition and organization.

This is not anti-AI. It is what allows AI to be used aggressively in the places where it truly helps without pretending that fluent generation is equivalent to translation approval.

The future of Bible translation may contain far more machine assistance than the present. If so, the mature question will not be whether AI participated.

It will be whether the resulting process remained faithful, competent, locally accountable, and capable of explaining why this text should be trusted as Scripture in this language.

Churches receiving a new translation may ask whether AI was used. Organizations should be able to answer clearly.

Transparency does not require disclosing every technical operation, but a community should not be misled about how the text was produced and approved.

Final trust should rest in the accountable translation process, not in claims that either humans or machines are incapable of error.

The strongest measure of success may be whether the language community ends the project with greater translation competence and infrastructure than it began with.

If AI allows an external organization to produce Scripture faster while local translators remain peripheral, the output may improve and the ownership model worsen. A mission-centered process seeks both faithful text and strengthened local Christian agency.

Footnotes

1. Jang, “The Impact of AI on Bible Translation: Opportunities and Challenges.” ↩

2. Heath et al., “The Privilege of First Draft: Technical and Ethical Aspects of Using AI to Draft Scripture.” ↩

3. Houston, “Experimenting with Excellence: Quality Assessment in a Mozambican Oral Bible Translation and Transcription Project.” ↩