MAPPING THE EVOLUTION OF ARTIFICIAL INTELLIGENCE IN COUNSELING RESEARCH: A BIBLIOMETRIC ANALYSIS OF TRENDS, THEMATIC STRUCTURES, AND RESEARCH GAPS FROM 2016 TO 2026

Abstract

Artificial intelligence (AI) has become increasingly visible in counseling-related
scholarship through chatbots, machine learning, natural language processing, large language
models, digital mental health platforms, and decision-support systems. This study maps the
evolution of AI in counseling research from 2016 to 2026 and interprets the field from a
counseling-centered, human-in-the-loop perspective. Records were retrieved from Scopus and
ScienceDirect and processed using sequential year, document type, subject area, and deduplication
filters. From 340 initial records, 119 articles constituted the final bibliometric corpus. Descriptive
bibliometric analysis, VOSviewer, and Bibliometrix/Biblioshiny were used to examine annual
scientific production, keyword co-occurrence, temporal overlay, keyword frequency, thematic
structure, and conceptual proximity. Publication output developed gradually from 2016 to 2022,
accelerated after 2023, and reached its highest reported level in 2025 with 43 articles; the 2026
output remains provisional. Artificial intelligence and mental health form the principal organizing
concepts, whereas counseling and psychotherapy are connected but less structurally prominent
than machine learning, natural language processing, digital health, and symptom-oriented topics.
Temporal and conceptual maps indicate a transition from telemedicine and eHealth toward
ChatGPT, generative AI, and large language models. Counselor competence, counselor education,
supervision, therapeutic alliance, multicultural counseling, school counseling, professional
accountability, and evidence-based counseling outcomes remain weakly integrated. The study
contributes a structured research agenda that emphasizes counselor-supervised AI, ethical
governance, culturally responsive design, rigorous outcome evaluation, and the preservation of
human professional responsibility. Bibliometric prominence is interpreted as evidence of scholarly
attention, not as proof of clinical effectiveness or causal impact.