Executive Summary

As AI systems become increasingly capable of generating theological commentary, exegetical insights, and instructional materials, Christian educators face a growing need to integrate these tools without compromising biblically faithful interpretation. AI can accelerate learning, broaden access to historical and linguistic resources, and empower under-resourced students. Yet without well-designed guardrails, AI may amplify shallow interpretations, smuggle in foreign presuppositions, and distort biblical theology.

This white paper outlines a training framework for equipping students and educators to use AI as a servant of biblical fidelity rather than as a substitute for disciplined exegesis. The framework combines biblicist hermeneutical principles, AI-specific interpretive safeguards, and pedagogical procedures for integrating AI into theological training and student assignments.

I. The Need for Biblicist AI Training

1.1 Growth of AI in Instruction

AI is now used to produce Bible commentaries, word studies, teacher’s guides, sermon outlines, and theological essays. Students may rely on AI-generated materials without foundational knowledge, creating the risk of:

Doctrinal drift Inconsistent interpretive methods Loss of primary text engagement Artificial authority mistaken for divine authority Substitution of “AI creativity” for “biblical clarity”

1.2 Challenges in Maintaining Sound Doctrine

AI models are probabilistic, not confessional. Even if instructed to use biblicist principles, they:

Draw from broad interpretive traditions Lack commitment to internal biblical coherence May invent historical or linguistic details Default to general religious ecumenism Cannot distinguish authoritative from speculative sources unless guided

Hence the burden falls on students and teachers to understand how to request, evaluate, refine, and correct AI output.

II. Philosophical Foundations of Biblicist AI Use

2.1 AI as Instrumental, Not Authoritative

A biblicist framework affirms:

Scripture interprets Scripture Authority lies in the canon, not in extra-biblical synthesis Teachers must test every claim (Acts 17:11) Tools must serve the interpretive process, not replace it

AI is treated as:

A research accelerator that assists the teacher but never becomes the teacher.

2.2 The Interpreter’s Responsibility

AI is not morally or spiritually accountable. Therefore:

Human interpreters remain responsible before God Teachers must understand the reasoning behind interpretations Students must be trained to interrogate AI outputs rather than accept them

2.3 The Goal: Depth of Learning

AI can promote depth if used properly:

Prompting multilayered exegesis Demanding explicit Scriptural citation Surfacing historical sources for student evaluation Supporting slow, careful reasoning through structured prompts

III. Core Interpretive Guardrails

These guardrails ensure that AI remains anchored in biblicist principles.

3.1 Sola Scriptura Orientation

Require AI outputs to:

Prioritize Scriptural evidence Show intertextual connections Use lexical analysis grounded in biblical languages Avoid theological speculation unless requested Clearly flag “extra-biblical” content

3.2 Historical-Linguistic Accuracy

Train users to ask AI for:

Hebrew/Greek lexical notes with Strong’s or Lexham references Citations to historical-cultural backgrounds Multiple scholarly interpretations Clear differentiation between attested fact and scholarly hypothesis

3.3 The Canonical Context Rule

AI-generated interpretations must show:

How a passage aligns with the whole counsel of God How themes develop across the canon How parallel passages illuminate meaning

3.4 Authorial Intent Orientation

Users should prompt AI to:

Prioritize the biblical author’s meaning Ground interpretations in genre, grammar, and context Avoid reading later doctrinal constructs back into ancient contexts

3.5 The “Three Checks” Rule

Students must verify:

Textual Check: Does the output match Scripture directly? Contextual Check: Does it align with surrounding paragraphs, book structure, and canonical themes? Cross-Reference Check: Are parallels applied properly?

IV. Training Framework: Building AI-Literate Biblicist Interpreters

This section outlines a practical process for training both students and educators to use AI in biblically faithful ways.

4.1 Stage 1: Foundations of Biblicist Hermeneutics

Before using AI, students must master:

Genre analysis Contextual reading Canonical theology Lexical and syntactical methods Principles of comparing Scripture with Scripture Distinguishing description vs. prescription

This ensures AI does not become a “shortcut” that replaces exegetical muscle.

4.2 Stage 2: AI Literacy and Prompting Skills

Educators teach students how to:

Write prompts that specify biblicist methods Demand explicit citations Require structured responses Request multiple interpretive alternatives Ask for evidence chains Flag speculative or low-confidence claims

Example AI-Promoting Exercises

“Generate three biblicist interpretations of Romans 14 and evaluate which fits canonical theology best.” “Provide a lexical analysis of hesed using only attested sources.” “Compare the AI’s interpretation of Matthew 24 with Scripture-only readings.”

4.3 Stage 3: Critical Evaluation Training

Students must be trained to critique AI outputs as rigorously as they would critique a commentary.

Key evaluation skills:

Identify hidden assumptions Check whether verses were cited accurately Verify lexical claims Detect anachronisms Distinguish between biblicist and non-biblicist frameworks Evaluate the interpretive weight given to cultural background material

4.4 Stage 4: Integrating AI into Assignments

Appropriate Assignment Types

Preliminary research summaries Step-by-step exegesis guides Comparative theological outlines Alternative interpretive hypotheses Structured drafts for student editing

Inappropriate Assignment Types

AI-written papers submitted as-is AI-driven doctrinal conclusions unverified by Scripture AI-generated sermons without teacher review

4.5 Stage 5: Instructor Oversight and Guardrails

Educators should:

Require students to submit both their prompts and raw AI outputs Annotate AI output with student corrections Show awareness of theological assumptions introduced by AI Use “adversarial prompts” to test student discernment Maintain confessional boundaries or biblicist standards appropriate to the institution

V. Institutional Guardrails for Biblicist AI Use

5.1 Confessional Constraints or Statement of Interpretive Practice

Schools should issue a clear AI policy covering:

Hermeneutical basis Prohibited interpretive frameworks (e.g., speculative typology without evidence) Acceptable uses in assignments Verification requirements Expectations for student competence

5.2 AI-Generated Material Review Process

Institutions can create:

A rubric evaluating AI-generated exegesis Standards for transparency in student submissions Protocols requiring Scripture citation A “biblicist alignment checklist” for instructors

5.3 Data and Privacy Considerations

Schools must ensure:

Sensitive student queries are not exposed Pastoral counseling content is never fed to external models Proprietary educational plans remain protected

5.4 Accountability Measures

Students must sign an academic integrity statement for AI use Teachers retain veto authority on doctrinal accuracy AI cannot be used to replace teacher oversight

VI. Sample Biblicist AI Training Exercises

Exercise 1: Multi-layered Exegesis

Prompt the AI:

“Provide a biblicist interpretation of Ephesians 2:1–10 using only canonical cross-references, lexical analysis, and contextual reasoning.”

Student task:

Identify strengths Identify theological drift Correct errors Provide improved prompts

Exercise 2: Interpretive Comparison

Ask AI:

“Compare the covenant themes in Genesis 12, 15, and 22 using purely biblical-theological analysis.”

Student task:

Evaluate canonical coherence Cross-check citations Identify speculative claims

Exercise 3: Distinguishing Description vs Prescription

Prompt the AI:

“Explain whether Acts 2 establishes normative church structure using strict biblicist methodology.”

Student task:

Evaluate genre and narrative context Test for theological overreach

VII. Long-Term Strategy for Developing Biblicist AI Proficiency

7.1 Building a Distinct Biblicist AI Corpus

Over time, institutions may want to:

Train models on biblicist commentaries Create custom lexicons and canonical maps Develop prompt templates Build institutional style guides Produce model outputs as exemplars

7.2 Training Specialist Instructors

Instructors must be trained to:

Diagnose AI drift Teach prompt engineering Evaluate theological accuracy Prevent syncretistic interpretations Maintain doctrinal fidelity

7.3 Student Competency Benchmarks

By the end of training, students should be able to:

Use AI to accelerate, not replace, exegesis Identify interpretive assumptions Apply biblicist correction to AI output Produce biblically grounded materials for teaching and discipleship

VIII. Conclusion: AI as a Tool for Deeper, More Faithful Biblical Learning

AI can either:

Flatten biblical learning into surface summaries, or Catalyze profound engagement with Scripture

—depending entirely on how students are trained.

A biblicist AI training program must teach:

Hermeneutical discipline Prompting skill Critical evaluation Doctrinal accountability Pastoral discernment

When these elements are combined, AI becomes an ally of faithful interpretation—enabling students to develop deeper insight, broader contextual understanding, and stronger confidence in Scripture.