A Critical Narrative Synthesis of Psychological Correlates, Measurement, and Reported Findings on Conversational AI Engagement and Dependence-Related Constructs

Synthesis: Deyu Yan (2026) presents a critical narrative synthesis of 51 peer-reviewed records (2021–2026) on the psychological correlates, measurement, and reported findings of conversational AI engagement and dependence-related constructs. The review separates conceptual and measurement work from 22 first-order empirical records and argues that labels such as trust, reliance, attachment, problematic use, and dependence refer to different processes that are frequently conflated in a rapidly growing body of scales. Cross-sectional studies mainly associated stronger AI-engagement or dependence-oriented scores with loneliness, social anxiety, depressive symptoms, low self-esteem, attachment insecurity, academic stress, escapism, anthropomorphism, fatigue, weaker critical thinking, procrastination, and lower well-being — associations that do not establish prediction or consequence. The synthesis develops a provisional framework separating instrumental-cognitive from relational-emotional use and treating AI Regulation in Education as a distinct dimension, and it calls for careful construct separation and design-matched claims.

Key Findings

• Constructs are routinely conflated: Trust (expectations about system competence), reliance (behavioral delegation), over-reliance (delegation without scrutiny), attachment/companionship (emotional closeness), and problematic use/dysregulated dependence (impaired control) refer to different processes yet are often treated as one continuum; several distinct instruments share overlapping labels without a stable object of measurement.
• Cross-sectional correlates, not causation: Mainly cross-sectional studies linked stronger engagement or dependence-oriented scores to poorer well-being, fatigue, weaker critical thinking, procrastination, loneliness, and academic stress — but none of these findings establish prediction or consequence.
• Both supportive and harmful experiences are reported: Qualitative and quasi-experimental work documents both relational support (perceived disclosure, lower loneliness in some settings) and distress linked to emotionally significant use, with limited, context-dependent evidence of short-term reductions in loneliness and social anxiety.
• Evidence is largely low leverage: Of 22 first-order empirical records, 16 (72.7%) provided lower-leverage evidence for directional claims and six moderate-leverage evidence; none supported a firm causal model, and direct cross-cultural and platform-comparative research remains rare.
• A provisional framework: Separating use orientation (instrumental-cognitive vs. relational-emotional) from regulation offers an interpretive — not validated — way to organize the field; frequent use, reliance, or attachment should not be labeled dependence without impaired control or functional harm.

Implications for AI in Education

For educators and researchers the review clarifies a critical distinction relevant to AI in education: productive cognitive delegation to AI differs from dysregulated dependence, and claims about educational over-reliance (e.g. Zhai et al., 2024) must not be conflated with relational attachment or clinical addiction constructs. It argues for design-matched claims and careful measurement — choosing instruments whose item content matches the intended construct — which matters for studies of engagement with AI tutors and chatbots in higher education. The provisional instrumental/relational framing also invites attention to how learners use AI for thinking support versus emotional companionship, and to the ethical and Well-Being dimensions of emotionally significant AI use.

Connected Concepts

• Conversational AI
• Cognitive Offloading
• Well-Being
• Metacognition
• Ethics
• Higher Education
• Generative AI
• Student Engagement

Connected Articles

• Conversational AI agents in education: an umbrella review of current utilization, challenges, and future directions — Conversational AI Agents in Education: An Umbrella Review
• Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators — Trust and Reliance on AI in Education: AI Literacy and Need for Cognition
• Engagement in LLM Chatbot-Supported Learning: The Pivotal Roles of GenAI Competency and Emotion — Engagement in LLM Chatbot-Supported Learning
• New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach — AI-Driven Tools for Enhancing Campus Well-being

Citation

Yan, D. (2026). A critical narrative synthesis of psychological correlates, measurement, and reported findings on conversational AI engagement and dependence-related constructs. Frontiers in Psychology, 17, 1827795.