Digital Discipleship in Three Layers: A Theological Framework for AI Integration in the Church
Bibliographic record
Abstract
Abstract This article proposes a theological framework for integrating artificial intelligence (AI) into the issue of Christian discipleship by presenting a three-layer model of formation: foundational, aggregated, and individual. While rooted in the evangelical tradition, the model is adaptable across faith communities, each defining its own sources of authority, communal practices, and individual expressions. Drawing on insights from contextual theology, the framework emphasizes that meaning arises in the dynamic interplay between Scripture, church tradition, and cultural context. It also highlights the central role of the Holy Spirit in communal discernment, ensuring that AI remains a tool for information rather than transformation. The model is designed to facilitate both top-down and bottom-up communication within ecclesial life, fostering resonance between foundational truth, communal practice, and lived experience. By situating the issue of digital discipleship within wider theological conversations on authority, anthropology, and resonance, this article contributes to the emerging field of digital theology and invites dialogue across diverse Christian and interfaith traditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".