New Research - Refusing Educational Technology: Artificial Intelligence, Inequity, and the Problem of Critical Optimism
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Research article by: Niral Shah, David Stroupe, Aman Yadav , Tess Bernhard , Charles Logan, Elizabeth B. Dyer , Peng He , Christina (Stina) Krist , and Tingting Li
Blog by: David Stroupe, Niral Shah, Aman Yadav
Educators are wrestling with the potential pitfalls and positive uses of generativeAI (genAI). Each day, a new story emerges that highlights the range of conversations: cheating on tests; students’ mental health; worries about reading skills; and so on. These challenges are real, complex, and pressing.
As scholars of STEM education, we note that genAI enters a historical arc of educational technology that includes many false starts, empty promises, and inequities. Rather than openly accepting genAI into educational spaces (including our research agendas), we interrogated how educational scholars frame ideological positions with regard to educational technology. The purpose of our conceptual paper is to highlight the visible and hidden assumptions educational researchers bring when using and studying educational technology in sites of learning, and to better understand why, despite ample concerns, some people choose to be “critical optimists” about genAI in education.
GenAI is not unique
Despite proclamations that genAI is completely different from every form of educational technology, our paper outlines a long history of false promises and soaring rhetoric. Over the last century, from film and radio to computers and massively open online courses (MOOCs), historical analyses show that educational technologies tend to follow a common pattern: irrational hype followed by limited impact (Cuban, 1986; Reich, 2020). While the capabilities of genAI are certainly more powerful than past technologies, it seems to this point that the potential for genAI to transform learning outcomes may be limited as every other form of educational technology.
Rigid ideological positions and “critical optimism”
Given the history of educational technology’s failures in learning settings, we initially note two ideological positions: the hopeful and the wary. While the hopeful position generally assumes that technologies will transform education for the better, the wary position assumes that any technology will harm learners. We argue that these positions often operate in the extremes without sufficient consideration of context, nuance, and conversation.
The ideological position that worries us, and seems to be the default position of many education researchers, is the stance of “critical optimism.” A critical optimist position is clear-eyed about the harm technology can cause while maintaining the belief that technology can improve education. In relation to genAI, a pithy version might be: “Harnessing AI for good”.
Our main issue with critical optimism is that such a position downplays the critiques that seemingly attract people to the position. If a technology has caused so much harm in the past and present (as many critical optimists argue), why should we believe that “this time will be different”? How has society become a space that would allow for educational technologies to broadly generate net benefit, especially for minoritized learners? We accuse critical optimists of “both sides-ism” by noting their seemingly strong critiques of genAI while advocating for genAI’s potential as a beneficial educational technology. While critical optimists claim that they share concerns about the harms of genAI, their stance permits educational technologies into educational settings.
Three forms of refusal
We understand that genAI is rapidly creeping into education settings, and we recognize that complete refusal of genAI might be implausible. However, to combat critical optimism about genAI, we propose three forms of refusal. First, curricular refusal involves teaching against technologies rather than only about or with them, as is typical with technological literacy approaches (Vogel et al., 2024). Here, teachers and students eschew curricula that assume technology’s beneficence and instead decide that not using a technology is a legitimate option. Teachers’ lessons on refusal can take inspiration from the Luddites and their rejection of dehumanizing automation. Second, bureaucratic refusal involves side-stepping top-down mandates to use educational technologies. Here, people reclaim agency to decide if and how genAI should ever be used in a learning setting, and if so, how to mitigate known and potential harms. Third, administrative refusal refers to actions that educational leaders (e.g., superintendents, principals, school board members, district technology directors) can take to resist the unfettered incursion of educational technology into learning settings. Here, people and organizations can limit and study genAI in learning settings, and can ask for evidence of benefit from technology companies rather than assume good intentions and negotiate behind non-disclosure agreements.
Critical questions
We recognize that refusal is scary, challenging, and difficult to enact for everyone in every educational setting. We applaud educators and researchers who are refusing in whatever ways are possible. In the paper, we also encourage everyone who is curious about refusal and tired of genAI to ask:
Why should I use this technology? Does this technology help me solve a real problem in my work? Are there external factors (e.g., financial incentives) pressuring me to use this technology?
What harms might this technology cause, both within and beyond my immediate context?
How can families and communities co-lead in making decisions about educational technology?
What types of evidence (and how much evidence) do I need to be convinced that refusal no longer remains my best option?
We hope that our article resonates with local social movements and legitimizes refusal as a necessary and viable stance for educational researchers and practitioners. We are actively seeking stories of refusal, and we want to hear from you about successes and challenges of refusing genAI in education settings.
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References and Further Reading
Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: Explaining an apparent paradox. American Educational Research Journal, 38(4), 813-834.
Reich, J. (2020). Failure to disrupt: Why technology alone can’t transform education. Harvard University Press.
Vogel, S., Yadav, A., Phelps, D., & Patel, A. (2024). Entrypoints for integrating computing and tech into teacher education: Addressing problems and opportunities with the EnCITE framework. Journal of Technology and Teacher Education 32(2), 217-248.