Synthetic relationships and artificial intimacy: an ethical framework for evaluating the impact of generative-AI on community
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,116 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,047 |
| Communication savante | 0,013 | 0,013 |
| Science ouverte | 0,002 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».