The Right Tool for the Job: Metacognitive Processes and AI
How this will help
A current challenge facing university instructors is the use of generative AI tools by students to achieve the goals and objectives of a course. Students commonly interpret course goals and objectives as things they need to produce – the outputs of learning. But what is missing in this view is how they need to think, learn, and build their knowledge – the process of learning.
Students are commonly asked to produce written output as evidence of their knowledge and thinking, such as essays and assignments. Given that, it’s not surprising they would turn to generative AI tools, such as ChatGPT, that are programmed to generate and produce contextually appropriate written output. And because of the relative ease with which quality-looking output can be generated with these tools, it’s also not surprising that instructors are placing even greater attention on verifying that assignments are created by students and not produced by a generative AI tool.
With so much concern surrounding students’ use of generative AI tools in classroom learning settings, specifically with the outputs of learning, could AI promote other important learning processes that are not focused on outputs? Could the focus be shifted to strengthening students’ thinking processes instead?
Expert Thinking
At the center of an effective learning experience are problems to be solved. The goal is for students to come away from the learning experience having acquired a certain amount of content knowledge so they can engage with and solve problems that are presented. However, teachers and educational researchers know that content knowledge alone isn’t sufficient to solve problems.
When presented with a problem, experts not only use content knowledge, they also actively weigh the possible strategies and options they may want to use to tackle the problem – using metacognition in the problem-solving process. Metacognition is thinking about your own thought processes. Metacognitive strategies are ways to encourage and actively engage in the “whys” around thought processes.
For example, the problem-solving process may involve asking or reminding themselves:
- Have I considered all the other options?
- Am I jumping to conclusions too quickly?
- Have I seen this before?
- Remember what happened the last time.
And while the use and benefits of metacognitive knowledge may have been developed over the course of a professional career, educational researchers know that metacognitive strategies can not only be of great benefit for novices learning in the classroom, but that it can be explicitly taught alongside content.
Using AI to Support Metacognition for Learning
Let’s say you’ve assigned an analysis of a local art installation. Some students might find themselves struggling to get started even after attending lectures, passing quizzes, and attending small group discussions. By introducing metacognitive strategies, you could help get their thinking started.
Providing them questions to ask themselves and explicitly walking them through this thinking process is an example. By demonstrating this question strategy with students, they not only begin the process of generating output for their analysis, but they also strengthen their metacognitive processes for similar analysis in the future.
Your students are now feeling confident that they can tackle this analysis and will be able to implement these metacognitive strategies at 11 pm when they do their work, right? Of course, we know learning doesn’t work this way.
This is where generative AI tools can come in to support students’ use of metacognitive strategies and their writing process – at the time when they need it. This shifts the use of generative AI tools from “prompting for output” to “prompting for learning.”
For example, if the student is stuck and doesn’t know how to get started, they could be provided a set of prompts that ask the generative AI tool to provide questions designed to elicit written responses from the student.
Prompt: Act as a writing coach to help me write a critical analysis of a local art installation. Your goal is to model metacognitive strategies to help me unpack, organize, and compose my analysis.
The requirements for the analysis are as follows:
- The rubric that will be used to determine the quality of the analysis is as follows: [provide rubric information].
- As a writing coach, you will ask me questions that will help me gather and compose my observations and insights about the art installation I visited.
- You will then evaluate my response against the requirements and rubric, and ask me to consider what other information I could provide to clarify my analysis. Continue to ask me questions until I have written at least 200 words.
- Do not provide any examples for how to improve my analysis. Your job is to help guide my approach toward composing my analysis.
- Do not ask all the questions at once. Instead, ask one question at a time, expect a response, evaluate the response, then ask another question.
In creating a writing coach with a generative AI tool, students are not only prompted to start writing, but the prompts themselves model a process of what a metacognitive strategy looks like for students, and what they can begin to use for future writing assignments.
If we want to help students fully realize the goals and objectives of our courses – to be able to apply what they’ve learned and create solutions in the real world – it’s important that we provide them with instruction and tools that emphasize strengthening their thinking skills and building their knowledge.
Resources
References
Dennis, J.L., Somerville, M.P. Supporting thinking about thinking: examining the metacognition theory-practice gap in higher education. High Educ 86, 99–117 (2023).
Krathwohl, D. R. (2002). A Revision of Bloom’s Taxonomy: An Overview. Theory Into Practice, 41(4), 212–218.
National Research Council. 2000. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press.
McCormick, C. B., Dimmitt, C., & Sullivan, F. R. (2012). Metacognition, Learning, and Instruction. Handbook of Psychology, Second Edition, 7.
Merrill, M. D. (2013). First Principles of Instruction: Identifying and Designing Effective, Efficient, and Engaging Instruction. San Francisco, CA: Pfeiffer.
Pintrich, P. R. (2002). The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing. Theory Into Practice, 41(4), 219–225.
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. CHI ‘24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
