Thinking for ourselves in the age of AI

Why metacognition matters?

Students’ extensive use of AI has raised concerns for the deteriorating thinking capabilities and the loss of human discernment in educational contexts and in all realms of life. Not only are our students, and everyone else, us included, using AI both for doing complex and/or repetitive work tasks, but it is being used so extensively that it seems to be used as a replacement for their unique and individual thought. These concerns have evoked us to search for a better understanding on how to preserve and nurture our ability to think for ourselves, and more importantly: to think about how we are thinking?

When task completion creates an illusion of competence

In our previous blog we’ve discussed the importance of cognitive effort in learning and how good performance is not always an indication of learning. A recent study points out that a successful completion of a task by using generative AI may leave students to struggle with understanding the overall process and overestimating their competence (Prather et al., 2024). Successful completion may produce an illusion of competence and conceal the students’ ongoing need to learn. Another study found that while the use of AI may reduce cognitive load, it may also result in weaker unaided justifications. Easier access to an explanation does not necessarily translate into equally strong independent reasoning (Stadler, Bannert & Sailer, 2024). It seems that students are experiencing metacognitive difficulties that in some cases can be compounded using AI for completing learning tasks.

Helping students understand their own thinking

Why is metacognition such an important dimension of learning and how to foster its development? Metacognition is the capability to be aware of and to regulate our own thinking.  Understanding what it is and how to nourish and improve it is now more important than ever. Metacognition helps us to understand what we know, and most importantly,:

what we do not know;

• it helps us organise and evaluate information,

• understand how we’ve arrived at certain conclusions, and

• to assess situations and the potential actions to take, to evaluate strategies and their feasibility.

  
  

These are vital skills, not least for students who are building their world view and still learning how to learn. Learning research has a long history with these ideas, including Flavell’s (1979) foundational account of cognitive monitoring.

For students, metacognition becomes concrete through questions such as:

• What are my assumptions? Why do I think things are like this?

• Can I explain why we ended here and why these are the conclusions?

• Which parts do I understand, and which am I accepting without examination?

• What evidence would make me reconsider?

• What should I do next to improve my understanding?

  
  

These questions gain renewed importance now as the use of AI is so widespread. When an explanation is fluent and readily available, students need ways to check whether they can reconstruct its reasoning, recognise its limitations and apply it in a different situation.

Teachers can create opportunities for students’ use and development of metacognition. Asking students to make their assumptions explicit and to predict outcomes, or have them explain an answer to a peer or revisit an earlier assumption which can reveal gaps in their thinking.

Connecting decisions, consequences and systems thinking

In business education, these gaps can have particular significance. Students need to learn how decisions along the value chain interact with each other, and how a seemingly straight-forward decision may have a substantial impact elsewhere, sometimes at a later time point. An apparently successful decision in one area may create difficulties elsewhere.

Balancing conflicting goals and understanding trade-offs requires a comprehensive understanding of how business organisations and their processes work. Systems thinking helps students examine these interdependencies, including feedback, accumulations and delayed consequences.

Metacognition adds an additional dimension: students must examine how well their own explanations account for what happens as well as investigate their starting points. Students learn by revising both their understanding of the business system and their approach to investigating it.

Making thinking visible through shared challenges

Authentic challenges provide useful conditions for this work. When students encounter incomplete information, competing objectives and consequences that unfold over time, they need to make decisions and remain involved long enough to investigate the results. An important element in this process is purposeful collaboration during which students make their thinking visible, to themselves and others, consider various viewpoints and come up with explanations, suggestions and conclusions.

Team-based learning with business simulations, in particular can provide a shared experience for business students that helps develop systems thinking and metacognition. Managing dynamic and interrelated processes in the business simulation, negotiating competing goals and reflecting, analysing and making revised plans fosters collaborative learning and the development of higher order thinking.

Designing learning for independent and collaborative thought

For teachers and academic leaders, this suggests reviewing the relationship between learning activities and assessment. Supporting the development of students’ metacognitive skills and systems thinking requires that focus is shifted from testing how well students can remember isolated concepts to fostering students’ collaborative knowledge-creation. Making the most of simulation-based learning environments obviously requires strong pedagogic support also for the teacher, as instructional support is a vital element in simulation-based learning (Chernikova et al., 2020).

The educational ambition needs to be set high – aim must be to educate graduates, who:

• identify what and how they think,

• know the limits of their knowledge and

• are capable of understanding complex relationships and express, analyse and reflect on their thought processes and actions,

  
  

This should happen in collaboration and interaction with others. Developing these capacities requires repeated opportunities to make students’ thinking explicit, put it to work and learn from consequences. Dynamic business simulation games provide a fruitful opportunity for this.

References and further reading:

Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of educational research, 90(4), 499-541.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911.

Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B., Kimmel, B., Wright, J., & Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In Proceedings of the 2024 ACM Conference on International Computing Education Research—Volume 1. Association for Computing Machinery.

Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, Article 108386.