Integrating Generative AI within Emerging Learning Theories

Background

Many moons ago, while I was in the process of getting my Masters degree, I wrote, “Emerging Theories of Learning and the Role of Technology” as a chapter in a class eBook. In this chapter, I delved into how modern technological advancements are redefining the educational landscape. The chapter addressed the evolving needs of today’s students and the crucial role technology plays in developing essential 21st-century skills. Particularly, I focused on three progressive learning theories: Situated Cognition, Distributed Cognition, and Socially-Shared Cognition, which emphasize context, collaboration, and communal learning as pillars of cognitive development.

Reflecting on these theories, I have recently considered the integration of Generative AI, a technology that can significantly magnify the impact of these learning frameworks. Here’s how Generative AI not only aligns with but also enhances these theories of learning:

1. Situated Cognition

Situated Cognition posits that learning is most effective when it occurs in context. Generative AI can create dynamic and immersive virtual environments that simulate real-life scenarios for students to navigate. For example, AI could generate a virtual lab where students can conduct chemical experiments safely and repeatedly, which reinforces learning through practical engagement. Another example of this was when Alexis Snider created a custom-built chatbot that emulated a 6-7-year-old child from Acnient Mesopotamia for her student to interact with and interview. The students used AI to situate themselves in the context of the era they were learning about.

2. Distributed Cognition

This theory extends cognitive processes beyond the individual to include tools and group interactions. Generative AI can serve as one of these cognitive tools, offering predictive analytics and decision support systems that help in problem-solving tasks. For instance, in a project-based learning setup, AI can suggest design modifications for student projects based on historical data and trends, effectively becoming a “member” of the project team. Another example of how AI can support this form of cognition is as a feedback tool on student writing. Having AI give student feedback to improve their writing is a clear way to scaffold new capabilities.

3. Socially-Shared Cognition

According to this theory, cognition is a shared activity that can enhance group problem-solving abilities. Generative AI can be instrumental in managing and facilitating group dynamics through platforms that optimize project collaboration. It can also synthesize information from various group interactions to provide insights that might not be apparent to individual group members, thus supporting the collective cognitive process. An example of this could include using an AI chatbot that has been constructed for a specific purpose where the students and others in the learning community can communicate with the chatbot to get ideas and ask for suggestions on next steps.

The Synergy Between Generative AI and Modern Learning Theories

The application of Generative AI within these frameworks supports a more nuanced and effective learning environment:

• Adaptive Learning Systems: Generative AI can design customized learning pathways for students, which adapt in real-time based on their performance and engagement levels. This personalization ensures that all students, regardless of their starting level, can achieve mastery at their own pace.
• Intelligent Tutoring Systems: By simulating one-on-one tutoring experiences, AI tutors can provide instant feedback, clarify student doubts, and offer explanations tailored to individual learning styles, thereby enhancing understanding and retention.
• Creative and Analytical Skill Development: Generative AI can prompt students to engage in creative exercises like writing, art, and music, providing tools that foster not only creativity but also critical thinking and problem-solving skills.

Conclusion

As we progress deeper into the integration of AI in education, it is clear that Generative AI is not just a supplementary tool but a foundational technology that can transform traditional educational models. By aligning Generative AI with Situated, Distributed, and Socially-Shared Cognition, we can cultivate an educational ecosystem that is not only technologically advanced but also deeply humanistic, nurturing the full spectrum of cognitive abilities in our students.