As human-AI interactions increasingly reshape social and research landscapes, the risks of overreliance, cognitive offloading, and the nuanced degradation of human agency have become more apparent. This paper addresses these challenges by introducing a novel conceptual framework based on the psychological triad: cognition, behavior, and emotion. Through a comprehensive survey of recent research, we observe that while AI can substantially enhance outcomes such as memory, creativity, and engagement, it simultaneously introduces systemic risks, including diminished critical thinking, skill erosion, and increased anxiety. Emotional outcomes are shown to be similarly mixed, with AI systems offering support and stress reduction while raising critical concerns regarding dependency, inappropriate attachments, and ethical oversight. By synthesizing these findings into the TRIAD model, a recursive framework of appraisal, execution, and regulation, this paper moves beyond isolated metrics toward a systems-level understanding of how AI shapes cognitive depth, behavioral patterns, and emotional development over time. The proposed model underscores the need for responsible, context-aware AI integration and establishes a grounded evaluation framework capable of capturing the complex, human-centered trade-offs inherent in modern technological advancement.
Figures & tables
Figure 1: Illustration of the Human-AI Interaction concepts outlined in the survey.
Figure 2: Cognitive effects of AI use across Bloom’s Taxonomy
Impact Focus
Study Topic / Focus
Study Type
Sample Size (N)
Key Metrics / Questions Used
Knowledge Acquisition
ChatTutor vs. ChatGPT for Learning ( Makransky et al., 2025 )
Between-subjects experiments
N=175 (Uni); N=234 (HS)
9-item conceptual knowledge test; Likert scales (1-5) for self-efficacy, trust, and enjoyment.
Critical Thinking Skills
AI and Critical Thinking Skills ( Gerlich, 2025 )
Mixed-method (Survey + Int.)
N=666 (Sur.); N=50 (Int.)
Halpern Critical Thinking Assessment (HCTA); Terenzini’s measures of development.
Memory
Alzheimer’s VR-AI Intervention ( Al-Rajab et al., 2025 )
Comparative pilot study
N=23 (Int.); N=10 (Ctrl)
Montreal Cognitive Assessment (MoCA); task completion rate; caregiver feedback.
Knowledge Retention
ChatGPT-Assisted Retrieval Practice ( Yusof, 2025 )
Retrospective cohort study
N=128
Final exam scores (60 MCQs); Feedback questionnaire on AI feedback quality.
Knowledge Retention
LLM-Generated Retrieval Practice ( An et al., 2025 )
Cognitive Skill Degradation (Aviation) ( K. et al., 2016 )
Between-subject experiment
N=59 (Init); N=46 (Final)
NASA-TLX (subjective workload); objective error rates in flight planning.
Table 1: Summary of AI Impact Studies: Cognitive Focus
Figure 3: The I-PACE model, illustrating the mechanisms for the development and maintenance of Internet-use disorders proposed by Brand et al. (2016) .
Impact Focus
Study Topic / Focus
Study Type
Sample Size (N)
Key Metrics / Questions Used
Dependence
AI Dependence and Mental Health ( Huang et al., 2024 )
Longitudinal cohort study
N=3,843
5-item AI dependence scale; PHQ-9; GAD-7.
Engagement
Alzheimer’s VR-AI Intervention ( Al-Rajab et al., 2025 )
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China · Institute of Scientific and Technical Research on Archives, Beijing 100050, China · China Electronics Engineering Design Institute Co., Ltd., Beijing 100142, China