Systematic Review of 68 Papers on Human–AI Chatbot Relationships

68 papers (78 studies) were identified from a systematic review of 16,358 records from six academic databases. The review, guided by the HAIR Framework, clarifies antecedents and social influence outcomes of human–AI chatbot relationships. It was authored by Yoo Jung Oh and last updated in April 2026.

Use Cases

• Analyzing trust and perceived social support as relational mediators based on the review's findings.
• Investigating the impact of chatbot design features on relationship outcomes as described in the literature.
• Examining the associations between message-level factors like empathy and relational outcomes as highlighted in the review.
• Identifying research gaps in long-term and cross-cultural relationship development based on the study's limitations.

Strengths

• Systematic review following PRISMA guidelines, providing a structured synthesis of the field.
• Analysis of 68 papers (78 studies) identified from a search of 16,358 records across six major databases.
• Clear framework (HAIR) applied to clarify relational processes and social influence outcomes.

Limitations

• The underlying dataset is a 208.2 KB DOCX file; its scale for direct computational analysis is limited.
• Row count and column-level documentation for the reviewed studies are unknown.
• The literature review notes a dominance of short-term experimental designs and Western samples, which may limit generalizability.

Provenance

Source

figshare, authored by Yoo Jung Oh.

Collection Method

Systematic literature review following PRISMA guidelines, searching ACM Digital Library, Web of Science, Scopus, IEEE, EBSCO, and PsycINFO.

Freshness

Last updated 2026-04-16 07:36:04