Self-regulated learning (SRL) is an essential skill that enables students to take charge of their learning process. However, SRL can be a challenging skill to master, with students needing to not only develop their cognitive strategies but also evaluate their own progress and motivate themselves. ith the rapid advancement of generative AI (GenAI), higher educationhas an innovative tool to help students overcome some of these issues and exert agency over their educational journeys.

Why self-regulated learning?

The ability to learn independently is not just an academic ability; it is vital for career achievement and lifelong learning. SRL is a proactive process that includes setting targets, monitoring progress, reflecting on outcomes, and adapting learning strategies as needed (Zimmerman, 2002). SRL activities may include managing study schedules, setting achievable goals, and reflecting on progress. These activities prepare students not only for their assessments but also for the constantly changing requirements of their future careers. However, traditional teaching methods often leave learners without the personalised support needed to develop these essential skills.

What challenges do students face?

The majority of students transition into university from schools or colleges which adopt a ‘teacher-led’ style of learning. Many students find it difficult to self-regulate their learning in a university setting (Bjork et al., 2013). While students may have developed SRL skills at school that were effective for their learning in secondary education, these SRL skills require re-shaping on entry to university (Rutherford 2019). One of the factors contributing to this challenge is the lack of timely, personalised feedback received by students on their learning. In addition, students may struggle to understand the educator’s feedback information, or lack understanding of how to implement it in their future work (Winstone, 2016). It is common to hear questions like, “Am I on the right track?” or “How can I improve?” Without immediate insights into their performance, students can feel overwhelmed and disengaged from the learning process. The lack of structured guidance makes it harder for them to develop the reflective habits needed for true self-regulation.

How generative AI can help

Generative AI offers a novel approach to addressing these challenges by transforming the way the feedback process, reflection, and planning occur within the learning cycle. Recent research from a UK university on a multinational MSc programme revealed that students, although often lacking confidence in GenAI usage, were aware of the tools and using them to support their learning (Smith et al., 2025). This observation highlights the importance of embedding GenAI literacy in programmes; here, we suggest several ways in which GenAI can be leveraged to assist students in becoming self-regulated learners. We also offer suggestions for GenAI prompts to facilitate these activities:

Personalised feedback information and guidance

AI-driven systems can analyse individual responses and offer real-time, personalised feedback information (Slimi et al., 2025). This ensures students receive immediate insights, enabling them to correct errors and improve their study strategies as they advance. An added benefit of GenAI is the 24/7 access to the tools, meaning students can access feedback information at a time and place convenient to them, rather than relying on academic staff responding to queries during office hours. See example prompt 1.

Example prompt 1

“Act as a critical friend, providing supportive, constructive, and personalised feedback on my work. Identify strengths, areas for improvement, and offer specific suggestions to help me improve my [Insert requirement]. Be concise and specific in your advice.”

Enhancing metacognitive skills

Beyond simply answering questions, GenAI can promote reflective thinking. By creating targeted self-assessment exercises and reflective questions, such as “What strategy worked best for you here?” AI encourages learners to critically evaluate their own processes (Xu et al., 2025).

Facilitating goal setting and planning

Interactive AI tools help students set clear, achievable goals and plan structured actions, for example, creating a personalised revision timetable for upcoming exams. With students able to track their progress, they can oversee their development and modify their plans as needed (Ingkavara et al., 2022). See example prompt 2.

Example prompt 2

“I am preparing for my upcoming biochemistry exam. The date of the exam is [insert date], by then I need to have revised all these topics [insert syllabus], help me create a revision timetable. Once I have revised a topic, ask me a set of concept-checking questions [in exam format e.g., MCQs, short answer, etc]. As my competency improves, ask me increasingly challenging questions to stretch my understanding.”

Creating adaptive learning environments

One of the most exciting features of GenAI is its adaptability. It customises content in real time, matching the difficulty and style of learning materials to the individual’s needs. As competence improves, the AI gradually reduces its support, encouraging greater independence (Walter, 2024).

Simulating a collaborative learning environment

Although self-regulation is a personal endeavour, interacting with others (‘co-regulation’ and ‘shared-regulation’) can enhance the process. AI-powered chatbots and virtual study communities simulate peer discussions, providing a safe space for students to express ideas, debate approaches, and gain diverse perspectives without fear of judgment (Ifelebuegu & Kulume, 2023). See example prompt 3.

Example prompt 3

“Act as a Socratic biosciences student to help me explore [insert topic]. Ask open-ended questions and be contrary where appropriate. Encourage me to justify my reasoning and consider alternative viewpoints or gaps in my understanding.”

Moving forward with technology

The integration of GenAI into higher education raises several important considerations. Ethical use, data privacy, and fair access remain central to any technological innovation. For AI to genuinely improve self-regulated learning, educators must be trained to incorporate these tools seamlessly into their teaching practices, ensuring that AI supports, rather than replaces, traditional methods (Chang et al., 2023).

Students becoming overly dependent is a potential problem. It is essential for students to be encouraged to use AI to support and enhance their cognitive strategies and approaches to learning, rather than replacing them. A UK survey of GenAI usage among university students shows an increasing reliance on these tools for a wide variety of academic tasks, for example, from 2024 to 2025, the percentage of students using GenAI in some capacity jumped from 66% to 92%, while those using GenAI to assist with assessments rose from 53% to 88% (Freeman, 2025). We, therefore, need to support students in utilising the AI tools around them to enhance their learning, understanding, and skill development, rather than completely abdicating the intellectual process to AI. Being proactive in showing students ways they can achieve this in an appropriate and ethical manner is essential. This is especially important when considering the change in educational expectations between the tightly structured learning experience that students receive in primary and secondary education, and the more independent learning expectations of a university student.

A collaborative future in learning

The intersection of GenAI and self-regulated learning is heralding a new era in higher education. By offering personalised, adaptive, and reflective learning experiences, GenAI not only tackles longstanding challenges but also paves the way for a more engaged, independent student body. It transforms education from a one-size-fits-all model into a dynamic, student-centred journey, where every learner is enabled to succeed.

As we look towards the future, the potential for GenAI to support SRL is vast. By adopting these technologies, educators can foster an environment where students are better prepared to handle academic challenges and the complexities of modern life. The future of education will be more innovative, more personalised, and ultimately more empowering for every student.

Inspired by our ongoing commitment to innovative teaching and reflective learning, this exploration of GenAI and self-regulated learning is a call to reimagine how we prepare students for the challenges ahead.

References

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