Educational Psychology

Learning success mechanisms and adaptability in the AI era: from reactive to predictive and proactive learning

Focus, Scope, and Rationale

This special issue focuses on advancing the understanding of learning in the age of generative artificial intelligence by examining how learning success emerges through dynamic, adaptive, and data-informed processes. Traditionally, learning has been conceptualized as a reactive process, where feedback and support are provided only after performance gaps become evident (Holmes et al., 2019; Siemens, 2013; Tu et al., 2026). Recent advances in generative AI and learning analytics enable a shift toward predictive and proactive learning environments, in which potential difficulties can be anticipated and addressed via learning diagnosis using AI systems as an assessment tool before initiating individual learning stages (Kirschner & Hendrick, 2024; Luckin, 2018; UNESCO, 2023).

Within this context, this special issue centers on learning success mechanisms and adaptability as key constructs for understanding how learners interact with evolving learning environments over time. Learning success is conceptualized as a dynamic and emergent outcome of cognitive, motivational, and behavioral processes, while adaptability reflects learners’ capacity to adjust these processes in response to changing conditions (Bandura, 1977; Zimmerman, 2002). C. J. Yin defined Learning success as a new educational paradigm that utilizes GenAI and Educational Big Data to proactively identify learners’ problems and propose precise interventions, personalized guidance, and tailored solutions. Its goal is to maximize students’ sustainable learning and ensure they achieve the best possible learning outcomes (Zhao et al., 2027). The issue aims to bring together empirical research that examines how these processes unfold across time, particularly in AI or learning analytics-supported environments that enable data collection, prediction (or diagnosis), and intervention.

 

Objectives

This special issue aims to publish articles that address one of the following needs:

• A theoretical need to conceptualise learning success as a dynamic and adaptive process rather than a static outcome.
• A methodological need to move beyond single-shot and cross-sectional studies toward longitudinal, process-oriented, and data-driven approaches.
• A contextual need to examine learning within AI-supported environments, where data collection, prediction, and intervention reshape learning processes.

In addition, this special issue recognises the importance of well-designed learning activities and instructional contexts in generating meaningful empirical data. Design-informed empirical studies are therefore encouraged, providing that they contribute to the analysis of learning processes rather than focusing solely on design itself.

Overall, this special issue aims to advance educational psychology by promoting a shift toward using AI or analytics tools:

• Process-based understanding of learning
• Predictive and proactive models of learning support
• Integration of diverse empirical methods
• Data-informed and longitudinal perspectives on learning

 

Main Themes

This special issue welcomes empirical, data-driven research that contributes to understanding learning success and adaptability as dynamic and/or process-oriented phenomena. Submissions may include, but are not limited to, the following categories:

1. Learning success mechanisms and adaptability in AI-supported learning

• Conceptualizations of learning success as a dynamic, adaptive, and process-oriented construct in the age of generative AI.
• Theoretical perspectives on how adaptability supports learning success across changing learning contexts and trajectories.
• Relationships among learning success, adaptability, self-regulation, motivation, engagement, and behavioural change over time.

2. From reactive to predictive and proactive learning

• New and adapted learning theories explaining the shift from reactive support toward predictive and proactive learning.
• Empirical studies examining how predictive and proactive interventions influence learning processes, learning outcomes, and learner adaptation.
• Conceptual and evidence-based analyses of how generative AI reshapes learning support, feedback, and intervention timing.

3. Learning trajectories and longitudinal evidence in AI-supported environments

• Longitudinal studies examining how learning success and adaptability develop over time in AI-supported learning environments.
• Process-oriented analyses of learning trajectories using behavioural, performance, and engagement data.
• Empirical research tracking changes in learners’ strategies, participation, persistence, and progress across sustained learning activities.

4. Data-driven, multimodal, and mixed-methods approaches to learning

• Studies using learning analytics, behavioural logs, and predictive modelling to investigate learning success mechanisms.
• Multimodal research integrating text, interaction, physiological, emotional, or sensor-based data to understand learning processes.
• Mixed-methods studies combining quantitative evidence with qualitative data to interpret behavioural patterns and adaptive learning processes.

5. Design-informed empirical studies of learning success and adaptability

• Empirical studies based on instructional design, activity design, or task-based interventions that generate meaningful data on learning processes.
• Research examining how designed learning activities support adaptability, engagement, and predictive or proactive learning.
• Studies in which teaching and learning designs serve as contexts for investigating learning success across time.

6. Assessment and evaluation of predictive and proactive learning

• Empirical studies evaluating the effects of predictive and proactive learning approaches on student learning, engagement, and adaptability.
• Assessment and evaluation methods for examining learning success in AI-supported and data-rich learning environments.
• Comparative studies investigating reactive, predictive, and proactive approaches to learning support and intervention.

References

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. https://psycnet.apa.org/doi/10.1037/0033-295X.84.2.191

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.

Kirschner, P. A., & Hendrick, C. (2024). How learning happens: Seminal works in educational psychology and what they mean in practice. Routledge.

Luckin, R. (2018). Machine learning and human intelligence. The future of education for the 21st century. UCL Institute of Education Press.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851

Tu, Y. F., Liu, G. P., Hwang, G. J., Chen, X. W., & Guo, X. G. (2026). AI self-efficacy and knowledge graph-integrated generative AI feedback in higher education. The Internet and Higher Education, 69, 101079. https://doi.org/10.1016/j.iheduc.2026.101079

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Zhao, F., Hwang, G. J., & Yin, C. (2027). Learning success in the age of generative AI: A new paradigm and theoretical model for education technology. International Journal of Mobile Learning and Organisation, 21(2). https://doi.org/10.1504/IJMLO.2027.10077554

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2