Personality and Personal AI Agents: A Co-Evolutionary Framework

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Bibliographic Details
Title: Personality and Personal AI Agents: A Co-Evolutionary Framework
Language: English
Authors: Oluwatoyosi Ogunsola (ORCID 0009-0005-2224-7463)
Source: International Journal on Social and Education Sciences. 2026 8(2):149-164.
Availability: International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijonsesoffice@gmail.com; Web site: https://www.ijonses.net/index.php/ijonses
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Artificial Intelligence, Personality Traits, Feedback (Response), Adjustment (to Environment), Man Machine Systems, Personal Autonomy, Well Being, Design, Behavior, Models
ISSN: 2688-7061
Abstract: Research on personality and technology has traditionally focused on a one-directional model where stable user traits predict digital behavior. This paradigm is insufficient for understanding the influence of modern adaptive AI agents, which actively and continuously personalize user experiences. This paper challenges the static view by introducing the Personality-Agent Co-Evolution (PACE) framework, a conceptual model that theorizes the dynamic, bidirectional, and reciprocal relationship between human personality and personal AI agents. We argue that users and agents are engaged in a process of mutual influence, where personality shapes agent interaction, and the agent, in turn, reinforces and nudges user behaviors and self-presentation over time. The framework details the reinforcement and corrective feedback loops that drive this co-evolution. From this model, we derive a set of crucial design principles for creating autonomy-supportive, transparent, and ethically-aligned systems. Finally, we present a research agenda to guide future empirical investigation into these dynamics. The PACE framework offers a new theoretical lens for communication and HCI scholars, providing a blueprint for the responsible design of the next generation of humancentric AI.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506046
Database: ERIC
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