The integration of artificial intelligence (AI) into education has accelerated dramatically in recent years, with recent advancements in AI being hailed as transformative for the sector (Kasneci et al., 2023; Rudolph et al., 2023; and Zhang et al., 2023). Specifically, generative AI (GenAI) – which involves models or algorithms that can generate new forms of creative content based on their training inputs – is gaining traction, giving rise to new, innovative pedagogies. One such pedagogy is generativism, which is defined as ‘a symbiotic approach to teaching and learning with GenAI’ (Pratschke, 2023: 2).
Generativism is characterised by dynamic, personalised and adaptive learning experiences that involve co-creation, co-facilitation and co-assessment using GenAI tools. This emerging, innovative pedagogy emphasises a constructivist approach to learning design, whereby students and educators collaborate with AI-powered tools to co-construct knowledge. According to Pratschke (2023: 2), generativism is ‘grounded in the principle of learning as a process’, leveraging AI to promote active learning. GenAI tools, such as Google’s Bard, Microsoft’s Copilot, OpenAI’s ChatGPT and Anthropic’s Claude, are built on large language models (LLMs), which harness the power of deep learning algorithms and natural language processing (NLP) to simulate human language processing capabilities. These chatbots can be used to create interactive, personalised and adaptive learning experiences for students based on their individual learning needs.
Through the technique of combinatorial innovation, Pratschke (2023) maps GenAI onto evidence-based digital learning design frameworks to demonstrate the potential of these emerging technologies in enhancing learning and teaching. Among these frameworks is Young and Perović’s (2016) ABC Learning Design method, which draws on Laurillard’s (2002, 2012) Conversational Framework. By integrating GenAI with the ABC method, generative activity design becomes possible. Moreover, integrating GenAI with the Community of Inquiry (CoI) framework proposed by Garrison et al. (1999) enables the creation of generative collaborative experiences, whereby students think and learn collaboratively with AI actors (e.g. collaborator AI, analytical AI and facilitator AI). Pratschke (2023) argues that these frameworks, with their focus on community, collaboration, conversation and connection, align well with the affordances of GenAI.
By embedding GenAI into evidence-based digital learning frameworks, generativism embraces the agility enabled by AI, and it encourages educators to rethink how they design learning experiences that are ‘social, collaborative, community-oriented and human-centred’ (Pratschke, 2023: 8). Furthermore, this AI-enabled model of education presents educators with exciting opportunities for the future. As is evident, there is immense potential for enhancing teaching practices with AI-powered tools to increase productivity, efficiency and student engagement.
References
Garrison, D. R., Anderson, T. and Archer, W. (1999), ‘Critical inquiry in a text-based environment: Computer conferencing in higher education’. The Internet and Higher Education 2(2–3): 87–105.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Wellwe, J., Kuhn, J. and Kasneci, G. (2023), ‘ChatGPT for good? On opportunities and challenges of large language models for education’. Learning and Individual Differences 10(102274).
Laurillard, D. (2002), Rethinking University Teaching: A conversational framework for the effective use of learning technologies. 2nd edn. London: Routledge.
Laurillard, D. (2012), Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology. New York, NY: Routledge.
Pratschke, B. M. (2023), ‘Generativism: the new hybrid’. Unpublished.
Rudolph, J. Tan, S. and Tan, S. (2023), ‘ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?’ Journal of Applied Learning & Teaching 6(1): 342–363.
Young, C. and Perović, N. (2016), ‘Rapid and Creative Course Design: As Easy as ABC?’ Procedia – Social and Behavioral Sciences 228: 390–395.
Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., Dam, S. K., Thwal, C. M., Tun, Y. L., Huy, L. L., Kim, D., Bae, S. H., Lee, L. H., Yang, Y., Shen, H. T., Kweon, I. S. and Hong, C. S. (2023), ‘A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?’ Unpublished.
