Artificial intelligence (AI) chatbots are increasingly integrated into higher education as tools that support learning processes and academic performance. However, their psychological and ethical implications for university students remain insufficiently examined. This paper analyzes the relationship between AI chatbot use, student anxiety, and academic engagement from a cognitive psychology perspective. Drawing on cognitive load theory, attention research, and self-regulated learning, the study argues that chatbots may reduce perceived overload, support information organization, and scaffold self-regulatory processes when implemented within a human-centered framework. At the same time, excessive reliance on AI-based assistance may weaken autonomy, disrupt sustained attention, and limit deeper cognitive processing, potentially increasing anxiety and fostering psychological dependence. To operationalize these ideas, the paper proposes a human-centered, adaptive AI chatbot architecture and an algorithmic interaction model that modulates response strategies based on behavioral indicators. A rule-based prototype of the adaptive interaction mechanism was implemented and evaluated through a pilot simulation using a synthetic interaction dataset. The results illustrate how adaptive response strategies can redistribute chatbot interactions from direct solution delivery toward scaffolded and supportive modes, thereby reducing potential overreliance on automated assistance. The proposed framework highlights the importance of integrating cognitive psychology principles with applied IT system design in order to develop AI-supported educational technologies that align technological functionality with students’ psychological needs.
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