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20.07.2026
Teaching faculty how to use ChatGPT is not enough, study warns
Research suggests teacher training needs to go beyond tutorials and address ethics, methodologies, and new forms of AI-aided assessment
The adoption of AI in education is still a work in progress: current and future teachers are looking for guidance and training on how to use the technology in the classroom | Image: Unsplash
Generative artificial intelligence (AI) is increasingly recognized as a tool with pedagogical potential, but its integration into classrooms is being hindered by a structural obstacle: teachers are not adequately prepared to use it.
The current situation reflects an institutional void: current and future teachers are eager to use AI and often attempt to do so independently, in a limited way and without methodological support from universities.
This is the conclusion of a paper published in the scientific journal Teaching and Teacher Education. The study, conducted in January and February 2024 at a university in the northwestern United States, involved 52 pre-service teachers with an average age of 20 and 21 teacher educators with an average age of 54 and 20 years’ experience.
The findings were revealing: of the 52 future teachers interviewed, 49 stated that they had never received any formal training on how to use AI in education.
This lack of preparation was not exclusive to the undergraduates. Among the 21 teacher educators, 18 reported that they do not use generative AI in their own classes, 18 said they had never received any training on the subject, and only nine allow students to use the tool during their studies, and only then with restrictions.
The lack of institutional guidance creates a disconnect: the study indicates that both pre-service teachers and teacher educators recognize the advantages of AI—saving time in lesson planning, for example—but due to the absence of training, its use remains limited to experimental, informal, individual efforts, a fact reflected in participants’ self-assessments of their own AI literacy: on a scale of 0 to 10, pre-service teachers scored themselves 4.2 on average, while teacher educators scored themselves 3.9.
“Our students and teachers are asking to learn more about AI, but we do not have the support to do it,” says study author Priya Panday-Shukla.
Unless universities take a more active role in reshaping their curricula, there is a risk that the use of AI in academia will remain clandestine, associated only with the fear of plagiarism and a loss of scientific integrity.
Why so much resistance, despite so much interest?
To understand why educators and future teachers remain hesitant to adopt AI despite recognizing its potential, the researchers drew on sociologist Everett Rogers’s 2003 theory on the diffusion of innovation.
Rogers uses five attributes to explain how new technologies spread (or do not spread) within a social group, and his theory was used to identify the barriers that continue to hinder the adoption of AI in education.
Five factors behind the adoption or rejection of AI in teacher training
1. Relative advantage: The extent to which AI is seen as better than existing methods—pre-service teachers cite time savings and support in generating ideas as clear benefits.
2. Compatibility: How well the tool aligns with the values and established practices of teacher training, including ethical concerns about plagiarism and integrity.
3. Complexity: The perceived difficulty of learning to use AI appropriately in education without institutional support.
4. Trialability: The possibility of experimenting with the tool before fully incorporating it into teaching—currently limited to informal and individual initiatives.
5. Observability: How easy it is to see the concrete results of using AI in the classroom. This remains limited due to a lack of institutional guidance and examples.
Continuing education as a methodological approach
Experts argue that overcoming these barriers requires more than software tutorials and occasional workshops on “how to use ChatGPT.”
What education needs is a set of public policies that promote continuing education focused on digital literacy and methodologies.
The solution lies in debating the ethics and limitations of AI and designing assessment processes that incorporate AI tools transparently.
If teacher training programs do not take the lead in this process now, teachers will continue to grope in the dark and students will graduate into a world already shaped by the technology.
For detailed data from the study and a theoretical analysis of the diffusion of AI in education, read this article previously published on Science Arena.
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