Artificial intelligence brings increasing attention to critical thinking and discourse. From an educational perspective, my rationale is that the community of inquiry framework, whose essence is critical thinking from an integrated personal and shared experience, is distinctly positioned to critically and creatively utilize the potential and risks of AI. My goal is to explore the dynamics and guiding principles in monitoring and managing AI output in educational contexts.

I begin with an update on shared metacognition in a community of inquiry that provides a jumping off point for the discussion of why shared metacognition and collaborative inquiry is of great relevance as we enter the age of artificial intelligence. For those who may need an overview or refresh their memory and understanding of the Shared Metacognition construct, you can find a brief introduction in a previous editorial (https://www.thecommunityofinquiry.org/editorial24).For others a reminder is that the key components of this construct are self and co-regulation operationalized through monitoring and management functions embedded in collaborative inquiry.

Shared Metacognition

We have defined the Shared Metacognition construct and an empirical instrument in terms of the purposeful regulation of learning in communities of inquiry (Garrison & Akyol, 2015). I have argued that shared metacognition is critical to understand and effectively implement inquiry in a community of learners as it provides an essential awareness of the process of constructing personal meaning and collaboratively confirming understanding (https://www.thecommunityofinquiry.org/editorial39).Moreover, it was noted that before we can fully appreciate the power and application of this construct, we need to demonstrate the impact of the awareness of shared metacognition in taking personal and shared responsibility to progress effectively and efficiently through the phases of practical inquiry(cognitive presence).

It is this issue of the impact of metacognitive awareness that a recent study used the Shared Metacognition construct and questionnaire to explore the influence of support in adopting self and co-regulation associated with collaborative inquiry. Martha et al., (20223) explored the effect of integrating metacognitive support provided through teaching presence on self and co-regulation learning skills and concluded that this had “a significant impact on increasing students' self-regulation and co-regulation learning skills” (Abstract). This result was supported by quantitative and qualitative data. Moreover, the authors concluded that the “evidence that integrating metacognitive and motivational scaffolds fosters cognitive engagement and manages learner motivation” (Discussion and Conclusion). This is clear evidence as to the effectiveness of shared metacognition in regulating collaborative inquiry.

Other recent publications have also pointed to the value of metacognitive awareness regarding collaborative inquiry (Stewart, 2023; Tang, et al., 2023). For example, Stewart(2023) found that knowledge of the four phases of cognitive presence “positively impacted their plans for designing learning environments to help their future online students move through the four phases of practical inquiry” (p. 242). The author also concluded that “… the CoI framework as an instructional design heuristic creates ample opportunities for metacognition and learning transfer”(p. 248). Similarly, Tang et al. (2023) stated that “Socially shared metacognition is important for effective collaborative problem solving in virtual laboratory settings” (Abstract).

As a segway to the challenges of artificial intelligence (AI) to learn in educationally worthwhile ways, let me briefly explore our understanding and significance of shared metacognition. Of relevance here is the work I did on thinking collaboratively (Garrison, 2016). The premise is that deep approaches to thinking and learning necessitate that we “communicate, explain, and justify…one’s thinking to self and others” (Flavell, 1987 in Garrison, 2016, p. 23). In short, “Thinking collaboratively reveals our thought processes and encourages us to think about our thinking” (Garrison, 2016, p. 82). This dynamic is central to shared metacognition in that learners must take responsibility for constructing meaning and knowledge through collaborative inquiry. Is this not the needed approach and ability we need to face the challenges of AI? A major goal of a Community of Inquiry is to develop metacognitive awareness and strategies that fully engage learners in challenging ideas and propositions for meaning and validity. Shared metacognition is required for deep thinking and learning. It would seem to me that such awareness and strategies speak to the approach needed to constructively incorporate the enormous power of AI.

Artificial Intelligence

Shifting the focus to other means of advancing our understanding and application of shared metacognition, in a previous editorial I made a connection between shared metacognition and addressing the challenges of generative artificial intelligence (AI) (https://www.thecommunityofinquiry.org/editorial41).I argued that AI cannot be ignored. Generative AI will inevitably change how we learn. As I noted in the previous editorial, the reason for this disruption is that “AI has the potential to support online learning communities by curating resources and presenting results in natural language. However, there is a risk of over reliance on non-transparent AI technology and its ability to assess disinformation.” Ultimately my concern is ceding academic direction to AI. Although I recognized that AI could provide valuable information for regulating the inquiry dynamic, I am very concerned that this would get too close to the edge in terms of excessive reliance on AI and undermine worthwhile educational processes and outcomes. The core issue is whether we maintain the willingness to question and challenge AI results. Will learners sustain a critical attitude and thinking presence regarding implausibility and ever-present misinformation? In other words, will we become too reliant on generative AI as it becomes embedded in the educational experience?

To be clear, my position is that shared metacognition is the means for generative AI to augment the educational experience while encouraging deep approaches to learning and limiting the risks of uncritical assimilation of misinformation. Shared metacognition goes to the essence of understanding and effectively monitoring and managing a collaborative inquiry educational experience. Most importantly, I believe the true value of shared metacognition will be constructively integrating generative AI into communities of inquiry in a manner where learners maintain control and responsibility in the construction and validation of knowledge. It will play an essential role in managing the benefits and risks of AI. Through metacognitive awareness learners can maintain skepticism and control over decisions regarding implications of AI content. Educational leaders must provide learners guidance and examples how to constructively and critically use AI resources for educationally worthwhile outcomes.

The Shared Metacognition construct in the context of the Community of Inquiry framework has shown theoretical and practical credibility. Collaborative inquiry shaped by shared metacognition can provide the means to facilitate and direct deep and meaningful thinking and learning. One example of an opportunity to open AI results to critical reflection and discourse is the question concerning the depth of AI results. This speaks directly to the essence of the CoI framework and specifically shared metacognition to encourage insights and deep learning that extend beyond surface meaning. In the final analysis, educators and learners must manage the risks of AI by remaining skeptical and maintaining the control of educational decisions. AI speeds things up while educators need to slow things down. That is, we need to take the time to be critically reflective and openly share our understanding of generative AI output. Thoughtful and constructive use of generative AI necessitates critical thinking and discourse best made possible through shared metacognitive inquiry (cognitive presence) that reflects collaborative monitoring and management of the learning process.

Conclusion

My position is that AI can be extremely useful in revealing ideas, insights and relationships, but it can also mislead and discourage deep thinking and learning. If we are going to be critical and creative thinkers, then it is clear to me that metacognitive awareness and regulation is essential. Effective collaborative inquiry that can cope with mis and disinformation in the age of AI will largely be dependent upon metacognitive awareness of the inquiry dynamic operationalized through cognitive presence in a community of inquiry where participants can challenge ideas and collaborative construct meaning open to verification. The core principles are skepticism, critical thinking, and the validation of meaning and knowledge. If we are going to be critical thinkers, then it is clear to me that metacognitive awareness and regulation is essential. I strongly believe that shared metacognition is a crucial guardrail regarding the oversight and management of the benefits and risks of AI.