Adaptive Generative AI for Personalized Learning and Human Behavior Change
The intersection of artificial intelligence, human learning, and behavior change has gained increasing importance in recent years. Traditional adaptive systems typically rely on data-driven personalization. However, emerging generative models, including large language models and diffusion-based systems which can dynamically create learning materials, challenges, or interactions tailored to an individual’s cognitive and emotional profile.
This paradigm shift presents new opportunities to explore how humans and AI systems can engage in mutual adaptation, forming feedback loops that stimulate curiosity, persistence, and positive habits. At the same time, it raises important questions related to trust, data ethics, and the boundaries of algorithmic influence on human behavior.
The goal of this Research Topic is to advance our understanding of how generative and adaptive AI can foster personalized learning and behavioral change in ways that are effective, explainable, and ethically sustainable. Current AI systems often optimize for performance metrics but overlook essential aspects of human cognition, such as motivation, self-reflection, and long-term adaptation.
By integrating advances from machine learning, neuroscience, and behavioral science, this topic encourages the development of new frameworks and applications that enable AI to act as a co-regulator of learning and behavior, rather than merely a recommender or evaluator. We seek studies that provide theoretical models, computational architectures, or empirical validation of adaptive AI systems that promote deeper, more enduring learning and positive behavior transformation.
We welcome original research, conceptual analyses, reviews, and methodological papers addressing:
Generative AI in personalized learning environments
Adaptive feedback and cognitive modeling for self-regulated learning
AI-driven behavior change interventions in health, sustainability, or education
Human-AI co-adaptation mechanisms and explainability
Ethical and societal implications of behavior-influencing AI
Manuscripts may focus on algorithmic innovations, experimental evaluations, or interdisciplinary frameworks that combine AI, behavioral science, and ethics. We encourage empirical, theoretical, or design-based contributions.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Brief Research Report
- Conceptual Analysis
- Data Report
- Editorial
- FAIR² Data
- FAIR² DATA Direct Submission
- General Commentary
- Hypothesis and Theory
- Methods
- … View all formats
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: generative AI, personalized learning, cognitive adaptation, behavioral modeling, human-computer interaction, self-regulated learning
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
