Synthetic relationships and artificial intimacy: an ethical framework for evaluating the impact of generative-AI on community
Bibliographic record
Abstract
Purpose This study aims to examine the ethical impact of generative artificial intelligence (AI) tools on human relationships and community life. It explores how AI-mediated interactions can reshape essential social practices, particularly in emotionally meaningful or developmentally formative spaces. Drawing on interdisciplinary research and moral philosophy, the article introduces the REAL Framework: Retained, Eroded, Atrophied and Leveraged. This model helps evaluate the relational consequences of emerging technologies. The purpose is to provide educators, institutional leaders and technology designers with a critical and practical tool for assessing whether generative AI tools support authentic human connection or subtly undermine it. Design/methodology/approach This article uses a conceptual and ethical analysis methodology, drawing from recent interdisciplinary literature in AI ethics, psychology and theology. Rather than presenting empirical findings, it offers a critical examination of how generative AI tools shape human relationships and community dynamics. The article synthesizes insights from scholarly research and cultural observation to develop the REAL Framework, a practical model for ethical evaluation. This approach allows for a reflective, theory-informed perspective that emphasizes relational integrity and communal well-being in the adoption and use of AI technologies. Findings The article finds that generative AI tools, while offering potential benefits, can also subtly distort or displace essential elements of human relationships. Through critical analysis, it identifies specific risks such as relational erosion, skill atrophy and the simulation of emotional intimacy without moral reciprocity. The REAL Framework, which stands for Retained, Eroded, Atrophied and Leveraged, serves as a practical tool to assess these relational impacts. Findings suggest that ethical evaluation of AI must move beyond technical concerns to consider the formation of individuals and communities. The framework helps users evaluate whether AI tools support or undermine authentic connections. Research limitations/implications This article presents a conceptual framework rather than empirical research, which limits the generalizability of its conclusions. While grounded in interdisciplinary scholarship, its findings are interpretive and intended to guide ethical reflection rather than predict outcomes. Future studies could test the REAL Framework across various cultural and technological contexts to assess its practical utility. Despite these limitations, the article offers valuable implications for educators, developers and institutional leaders. It encourages proactive, community-centered evaluation of generative AI tools and highlights the need for ethical discernment that prioritizes relational integrity and long-term human development over short-term technological efficiency. Practical implications The article provides a usable framework for evaluating the relational impact of generative AI tools within educational, organizational and community settings. The REAL Framework equips practitioners to ask targeted questions about whether a tool preserves essential human connection, erodes relational depth, weakens emotional skills or can be used to support authentic community. This model is especially relevant for educators, institutional leaders and developers who are navigating the integration of AI into emotionally significant environments. By applying the framework, stakeholders can make more informed, ethically responsible decisions that prioritize the dignity of persons and the health of human relationships. Social implications This article highlights the broader social implications of generative AI tools that increasingly shape human interaction, identity and community life. As AI systems mediate emotionally significant exchanges, there is a risk that relational authenticity may be replaced by simulation and convenience. The REAL Framework encourages reflection on how technology influences not only individual behavior but also collective values and social norms. Its application can help communities safeguard relational integrity, resist depersonalization and foster practices that strengthen human connection. The framework invites ongoing communal discernment about the kind of society being formed through the tools we choose to adopt. Originality/value This article offers an original contribution by introducing the REAL Framework as a practical tool for evaluating the relational and ethical impact of generative AI technologies. Unlike purely technical or utilitarian approaches, this model emphasizes the social and moral dimensions of AI use, particularly in emotionally formative and community-based contexts. The framework draws from interdisciplinary research and applies it to a timely cultural concern, offering a structured means of reflection for educators, leaders and designers. Its value lies in equipping stakeholders to move beyond efficiency-based assessments and instead prioritize the preservation of authentic human connection and communal well-being.
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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 | 0.069 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".