Framework

The perspective by which I want to research the development of generative AI (Gen-AI) is through the Diffusion of Innovation model by Everett Rogers in combination with Situated Learning theory.

Diffusion of Innovation-model

The Diffusion of Innovation model explains how, why, and at what rate new ideas, products, or technologies spread through a population. Rather than seeing adoption as a single event, this model outlines a process in which different segments of a community adopt innovations over time.

The model categorizes adopters into five groups: innovators, early adopters, early majority, late majority, and laggards—each with different risk tolerances and decision-making speeds. Individuals typically move through stages of awareness, persuasion, decision, implementation, and confirmation as they decide whether to embrace a new idea.

Adoption rates are influenced by how people perceive the innovation’s relative advantage, compatibility, complexity, trialability, and observability. Early adopters are often drawn by clear benefits and the ability to test the technology, while later adopters wait to see widespread acceptance and tangible results before committing.

In the context of Gen-AI, early adopters within the academy may already be incorporating AI tools for creative exploration, while others remain hesitant, concerned about ethical implications or their relevance to artistic processes. By listening to varied perspectives and identifying patterns, I aim to understand whether—and if so, how—Gen-AI tools can become broadly adopted over time.

 Situated Learning Theory

The Situated Learning Theory, proposed by Jean Lave and Etienne Wenger, emphasizes that learning occurs through participation in a community of practice rather than through passive instruction. In these communities, newcomers gain knowledge and skills by engaging in authentic, real-world activities. Over time, learners move from peripheral roles, where they handle simpler tasks, to more central roles as their competence and confidence grow.

A key principle of this theory is that knowledge cannot be separated from the cultural, social, and physical settings in which it is used. Activities, tools, and interactions create a context that makes learning meaningful and enduring. Through collaboration with experienced practitioners, learners not only acquire technical skills but also internalize the norms, values, and practices of their community.

In the context of my research, I see the academy as a community of practice where students, tutors, and professionals collaboratively explore the integration of Gen-AI in creative workflows. By engaging different stakeholders in authentic experimentation, I hope to create meaningful learning experiences that help students and staff build confidence in navigating these emerging technologies.

 

Key tenets of situated learning theory | Download Scientific Diagram

Why These Frameworks?
This research draws on both the Diffusion of Innovation (DOI) model and Situated Learning (SL) theory because they address different but interconnected aspects of how generative AI is adopted and integrated into creative education.

  • Diffusion of Innovation explains the stages by which new technologies spread through a community, highlighting patterns in adoption based on individual and group characteristics. This helps to map who adopts generative AI tools early, who hesitates, and why.
  • Situated Learning emphasizes that learning happens through active participation in a community of practice. It focuses on the cultural, social, and contextual elements that shape how individuals learn to use new tools, such as generative AI, in authentic ways.

Together, these frameworks provide a holistic view of the research topic. The Diffusion of Innovation model captures the “when” and “how” of adoption, while Situated Learning sheds light on the “where,” “why,” and “with whom.” By combining these perspectives, this research explores both the timeline of adoption and the collaborative environments that make meaningful learning possible.