Collaborative learning, self-regulation, feedback literacy and AI: Towards future-oriented education

In recent years, there has been growing attention for teaching strategies that go beyond the classic transfer of knowledge. Collaborative learning, self-regulation and feedback literacy have become essential elements in the pursuit of authentic, future-oriented education. In this blog I explore these concepts, backed by recent research, and link them to the possibilities of AI in education, in particular through the use of the ‘Feedback Companion‘.

Collaborative learning: Learning through dialogue

Collaborative learning is seen as a powerful form of education in which interaction between students leads to deeper learning experiences. By working together, students not only come to understand the material better, but also develop crucial social skills such as communication, problem-solving and critical thinking. Research by Kirschner and van Merriënboer (2023) shows that collaborative learning has a significant positive effect on learning performance. Students who actively engage in dialogue with their peers perform better than those who work in isolation. It is crucial that there is a supportive structure in which teachers set clear goals and provide guidance.

Self-regulation: The engine of autonomy

Self-regulation is students' ability to steer their own learning process. It involves setting goals, planning and monitoring progress, and adjusting strategies when necessary. Zimmerman and Schunk (2022) stress that self-regulated learning is strongly linked to success. Students who learn how to regulate their learning process effectively turn out to be more motivated and more successful. This calls for a learning environment in which students are encouraged to take ownership of their learning, and in which formative feedback helps them understand where they stand and how they can move forward.

Feedback literacy: Feedback as a learning tool

Feedback literacy refers to students' ability to understand, process and use feedback to improve their learning process. Carless and Boud (2023) explain that feedback is not just about receiving information, but precisely about actively using that information to make progress. Students learn from the dialogue that feedback stimulates, making feedback a two-way process in which both student and teacher are involved. Creating a feedback-rich environment is essential to support students in their growth and development.

AI and the Feedback Companion: Technology as a catalyst for dialogue

The rise of AI in education offers an opportunity to take these concepts to a higher level. My ‘Feedback Companion’ uses AI to give students personalised and structured feedback based on learning outcomes and work processes. This goes beyond simple data points: it stimulates in-depth reflection and self-regulation. Research shows that AI-based feedback systems increase students' motivation and self-regulation because they provide tailored insights (Yin et al., 2023). The use of AI in feedback processes leads to greater engagement and better performance, because students receive targeted pointers at the right moment.

A recent study also confirms that AI has a positive effect on dialogue in education. According to Wang et al. (2024), AI can be used to make feedback more personal and interactive, so that students feel more heard and supported. The Feedback Companion responds to this by not only informing, but also inspiring students to take action and improve their learning process.

Conclusion

Collaborative learning, self-regulation and feedback literacy form the basis for future-oriented education in which students are prepared for the complex challenges of the real world. With the support of technology, such as AI in the Feedback Companion, we can apply these principles even better. In doing so, we are building a learning environment in which learning revolves around interaction, personal growth and the ability to actually make use of feedback. Let's work together towards an education system that does justice to the unique talents and potential of every student.

Practical applications: Didactic coaching and AI workshops

For anyone interested in how these principles can be applied in practice, from February 2025 I am offering the training Didactic Coaching 2.0: Self-Regulation and Innovation with AI in Education. This training focuses on strengthening self-regulated learning and using AI effectively to enrich educational interactions. I also offer AI workshops in which teachers learn how they can use technology to optimise the feedback processes in their own teaching. You can find more information on my website, onderwijsorakel.nl.

References:

• Carless, D., & Boud, D. (2023). The development of feedback literacy in higher education. Assessment & Evaluation in Higher Education.
• Kirschner, P. A., & van Merriënboer, J. J. G. (2023). Collaborative learning: The future of education. Educational Research Review.
• Wang, Y., Li, X., & Chen, Z. (2024). AI-enhanced feedback and student engagement: A meta-analysis. Research Square. https://doi.org/10.21203/rs.4243877.v1
• Yin, H., Han, J., & Lo, M. (2023). Exploring the impact of AI-generated feedback on student engagement and self-regulation. Computers & Education.
• Zimmerman, B. J., & Schunk, D. H. (2022). Self-regulated learning and academic achievement: Theoretical perspectives. Educational Psychology Review.