GenAI and Self-Regulated Learning

As we continue to monitor the potential benefits and challenges of generative artificial intelligence (GenAI) in higher education, I would like to share very recent research on how we could incorporate GenAI into building self-regulated learning (SRL) through writing. This week’s article is entitled, “A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking” by Kong et al. (2024). 

The study follows the Zimmerman (2002) model, which describes proactive learning where learners know their strengths and limitations, and are guided by personally defined goals and task-oriented strategies in three stages:

• Forethought

• Performance

• Self-reflection

Students set feasible goals and plan strategies, which require prior knowledge and experience (Pintrich, 2004; Hargis, 2000). Students apply the strategies and monitor their progress, which Zimmerman (2002) termed self-control and self-observation. As digital tools become prevalent, students have adopted real-time and personalized self-observation methods such as learning analytics (Araka et al., 2020).

The authors propose a pedagogical design that models on SRL and the authoring cycle to develop SRL when composing academic writing using text-based GenAI. The model contains six iterative and interactive phases. Students: 

1. Plan the content and structure of the writing;

2. Prompt GenAI generation;

3. Preview and verify the output;

4. Produce the writing using the corrected output;

5. Peer-review; and 

6. through Portfolio-tracking, reflect and formulate strategies for future use.

7. When students compose portfolios, they deliberate over the learning experience and suggest improvements, thereby engaging in self-reflection (Bavlı, 2023).

During these times of evolving perspective and application of GenAI in higher ed teaching and learning, the authors further suggest that their framework could serve as a foundation to establish guidelines around text-based GenAI.

References

Araka, E., Maina, E., Gitonga, R., & Oboko, R. (2020). Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). Research and Practice in Technology Enhanced Learning,15(6), 1–21. https://doi.org/10.1186/s41039-020-00129-5 

Bavlı, B. (2023). Learning from online learning journals: Experiences of postgraduate students. Interactive Learning Environments, 31(10), 7040–7052. https://doi.org/10.1080/10494820.2022.2061005 

Hargis, J. (2000). The Self-regulated learner advantage:  Learning science on the Internet. Electronic Journal of Science Education, 4(4).

Kong, S.-C., Lee, J. C.-K., & Tsang, O. (2024). A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking. Research and Practice in Technology Enhanced Learning, 19, 030. https://doi.org/10.58459/rptel.2024.19030

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x 

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