GenAI and SRL

This week I would like to share a very recent SoTL article that combines the rapidly progressing tools of generative AI (GenAI) and the foundational learning theory of self-regulated learning (SRL). The article is entitled, “Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: A configurational analysis” by Wu and Chiu (March 2025).

Several Research Questions were explored:

Higher levels of technological proficiency positively influence SRL outcomes when combined with advanced tool functionality and high-quality feedback from GenAI tools.
Strong research skills significantly enhance SRL capacities, particularly when learners receive high-quality feedback and engage with GenAI tools that offer extensive functionalities.
Positive user attitudes towards GenAI tools are associated with improved SRL outcomes, especially when these attitudes are coupled with high user engagement and supportive user interfaces.
Increased user engagement with GenAI tools, characterized by frequent and deep interaction, leads to better SRL outcomes, particularly when the tools provide robust functionality and high-quality feedback.
The functionality of GenAI tools, including their adaptability and interactivity, has a significant positive impact on SRL, especially when learners have high levels of tech proficiency and research skills.
The quality of feedback provided by GenAI tools enhances SRL outcomes, particularly when learners exhibit strong research skills and engage actively with the tools.
A positive user interface and overall user experience with GenAI tools enhance SRL outcomes, moderating the relationship between tool functionality and SRL capacities.

The study collected data across various educational levels using a 28-week fuzzy-set qualitative comparative analysis (fsQCA) approach (an advanced analytical technique well-suited for examining intricate causal relationships within educational contexts (Toots & Lauri, 2015)). The research explores how factors such as

technological proficiency,
user engagement and attitude,
research skills,
tool functionality,
user interface experience, and
feedback quality

interact with the functionalities of GenAI tools to enhance SRL capacities. Data were collected through semi-structured surveys and qualitative assessments from a diverse sample of undergraduate and postgraduate students. Here is a link to the list of AI Tools used.

The findings reveal that the synergistic relationship between learner characteristics and GenAI tool affordances significantly boosts SRL skills. Key configurations identified include

the critical role of high-quality feedback and tool functionalities,
the importance of positive user attitudes and engagement, and
the moderating effect of user interface experience.

GenAI tools support the development of SRL skills, such as goal setting, strategic planning, and self-assessment (Mishra et al., 2024). Moreover, the interactive nature of GenAI tools fosters an immersive learning environment where learners can engage with content dynamically (Daher & Hussein, 2024). One of the most significant affordances of GenAI tools is their ability to provide immediate and actionable feedback (Mishra et al., 2023). Real-time feedback helps learners identify and correct errors promptly, reinforcing learning and encouraging self-reflection (Hattie & Timperley, 2007). This continuous feedback loop is crucial for developing SRL skills, as it enables learners to monitor their progress, adjust strategies, and achieve their learning goals more effectively (Chou & Zou, 2020).

The results of this study advocate for the strategic integration of GenAI tools in educational practices to support diverse learning pathways, contributing to the global discourse on digital pedagogy and the enhancement of SRL.

References

Wu, X., & Chiu, T. (2025). Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: A configurational analysis. Journal of New Approaches Educational Research, 14(10). https://doi.org/10.1007/s44322-025-00028-x