Metacognition GenAI

This week I would like to share recent research on the benefits of empowering learners with metacognition strategies combined with generative artificial intelligence (GenAI). The first article is entitled, “Effectiveness of the metacognitive-based pedagogical intervention on mathematics achievement: A meta-analysis” by Sercenia and Prudente (2023). The authors remind us of elements crucial to learning and acquisition, such as attention to form, noticing, salience of input, and learner involvement. Findings of their research showed that metacognitive-based pedagogical intervention has a significantly large and positive effect on achievement. There are many types of metacognitive strategies. Meher et al. (2021) found that brainstorming, concept mapping strategy, and the think-aloud strategy had the largest effect.

According to Chatzipanteli et al. (2014), students who apply their metacognitive skills (e.g., planning, monitoring, evaluation, think-aloud, journal writing, concept mapping, KWL) can better identify problems, and determine how to reinforce what they have learned.

Mizumoto (2023) connects metacognition to GenAI in the article “Data-driven learning (DDL) meets Generative AI (GenAI): Introducing the framework of metacognitive resource use.” The author advocates for a blended methodology. Anchored in the foundational principles of metacognition, the framework centers on two pivotal dimensions: metacognitive knowledge and metacognitive regulation. The paper proposes pedagogical recommendations designed to enable learners to strategically utilize a wide range of resources, including GenAI guided by self-awareness, the specifics of the task, and relevant strategies.

Finally, the University of Illinois Center for Innovation in Teaching and Learning suggests integrating Generative AI with metacognitive strategies. GenAI can:

• Provide timely feedback on assignments, highlighting areas for improvement and offering explanations to encourage iterative learning;

• Identify areas of struggle and provide targeted resources (articles, videos, and audios) based on learning outcomes;

• Offer accessibility features, such as text-to-speech and translation services as well as pronunciation feedback, and vocabulary suggestions, making language acquisition more interactive.

In case you have not identified the GenAI tool that works best for you, here are several that I have used that have been helpful to pedagogy in a variety of ways:

• OpenAI ChatGPT

• Anthropic Claude

• Google Bard

• Pi

References

Sercenia, J. C., & Prudente, M. S. (2023). Effectiveness of the metacognitive-based pedagogical intervention on mathematics achievement: A meta-analysis. International Journal of Instruction,16(4). www.e-iji.net ISSN: 1694-609X, 561-578.

Mizumoto, A. (2023). Data-driven learning meets generative AI: Introducing the framework of metacognitive resource use, Applied Corpus Linguistics, 3(3), 100074, ISSN 2666-7991, https://doi.org/10.1016/j.acorp.2023.100074.