Lodge, J. M., Yang, S., Furze, L., & Dawson, P. (2023). It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence? Learning: Research and Practice, 9(2), 117-124. https://doi.org/10.1080/23735082.2023.2261106
Lodge et al. (2023) set out to establish a frame for discussions about generative AI technologies in education by offering typologies for AI. They begin by dispelling the common analogy of generative AI technology to the calculator. Lodge et al. hold that comparing generative AI to a calculator assumes that generative AI technologies will be able to do tasks to arrive at a correct answer, but really generative AI functions are more complex than that oversimplified analogy of the calculator. Lodge et al. view generative AI as an infrastructure, rather than a singular tool. Next, Lodge et al. provide an overview of human-generative AI interaction. They contextualize this interaction in the more firmly established human computer interactions that take in to account social and cognitive processes involved in learning. Computers were used to offload tasks – like complex addition problems to a calculator, for example. However generative AI is not about offloading a task to take the data and move forward. Lodge et al. introduced a four-quadrant typology for human and machine interactions for education. The vertical axis represents how AI can free up humans from boring tasks so they can engage in higher-level thinking or extend human thought capabilities. The horizontal axis then illustrates the way the human-machine relationship functions, individually or in collaboration. For example, using a calculator is an individual use and is an example of cognitive offloading. Cognitive offloading occurs people shift part of their cognitive tasks elsewhere – for example, a Google calendar keeps track of appointments, cell phones hold phone numbers, journals hold notes, etc. – but technology does not necessarily have to be involved. Cognitive offloading can damage learning if too much information or cognitive work is left to other devices. But cognitive offloading can also free up thinking space to allow people to engage in higher-order thinking processes. The extended mind theory holds that technology is used to expand human capabilities in complex tasks and thought processes. Generative AI could be an extension of the mind. Next. AI can also be used as a collaborative tool that assists in the co-regulation of learning. While generative AI cannot regulate human learning, the outputs of generative AI as it monitors human learning can help humans reflect on and monitor their learning in relationship to their goals. AI can “coach” humans here. Finally, ther eis hybrid learning. AI tools can help humans learn because it provides real-time feedback that is adaptive and personalized. AI can guide learners to grow and develop through opportunities for reflection.
Lodge et al. provide a very clear description of their typologies for generative AI use and human interaction in education. Their writing is clear and concise and cites relevant resources. This article does provide a framework for discussing generative-AI human interaction without reducing it down to an overly simplified statement of: “It’s just like a calculator.” Not only does that phrase oversimplify, but it also discredits those who have legitimate concerns about the integration and implementation of generative AI in the classroom. The four typologies are discussed in a way that connects each one to the next. Lodge et al. start out with generative-AI human interaction, then discuss cognitive offloading, then expanded mind theory, then co-regulated learning and hybrid learning. This process allows the framework to develop from more simple to more complex, and as the paper moves forward the seeds for discussion about generative AI grow more complex, leaving the reader to seek out more complex connections.
I am interested in this as a doctoral student because I am extremely interested in the ways generative AI, cognitive offloading, knowledge acquisition, and transactive memory partnerships work. When I was earning my Ed.S. degree, I focused my work on the ways Google was shifting knowledge acquisition and learning as a possible transactive memory partner in the context of classroom discussions. But as my work was ending on that degree ChatGPT emerged, and my interested shifted to generative AI. As Lodge et al. showed, there are many ways that generative AI will reshape learning and knowledge work – and don’t know all of those ways yet. So this is something that is very in line with my research interests.
Also, reading this made me cringe because I have been using the calculator analogy in almost every discussion I have had about generative AI. And when I read how the analogy was broken down to illustrate the ways generative AI is more complex than a calculator, I realized I had inadvertently been dismissing some very legitimate concerns about the inclusion of generative AI in the classroom. This was a good reminder to slow down and think through something before just embracing it.
APA Citations for Additional Sources
References
Chauncey, S. A., & McKenna, H. P. (2023). A framework and exemplars for ethical and responsible use of AI chatbot technology to support teaching and learning. Computers and Education: Artificial Intelligence, 5, 100182. https://doi.org/10.1016/j.caeai.2023.100182
Chen, B., Zhu, X., & Díaz del Castillo H, F. (2023). Integrating generative AI in knowledge building. Computers and Education: Artificial Intelligence, 5, 100184. https://doi.org/10.1016/j.caeai.2023.100184
Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with ai? Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056
Vinchon, F., Lubart, T., Bartolotta, S., Gironnay, V., Botella, M., Bourgeois, S., Burkhardt, J.-M., Bonnardel, N., Corazza, G. E., Glaveanu, V., Hanson, M. H., Ivcevic, Z., Karwowski, M., Kaufman, J. C., Okada, T., Reiter-Palmon, R., & Gaggioli, A. (2023). Artificial intelligence & Creativity: A manifesto for collaboration. Pre-Print. https://doi.org/10.31234/osf.io/ukqc9
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