Educators and trainee teachers want to use AI but lack the necessary training

Study shows that teacher training courses do not make good use of generative artificial intelligence due to lack of training and institutional guidelines

Gilberto Stam

A team meeting with professionals discussing ideas and analyzing information on laptops on a desk

Generative artificial intelligence (AI) is generating growing interest as a pedagogical tool, but its adoption in teacher training faces significant obstacles. According to an article published in the journal Teaching and Teacher Education by Priya Panday-Shukla of Washington State University, USA, trainee teachers and their university professors say they lack the preparation and formal training needed to integrate the technology into their courses.

The study, involving 52 trainee teachers (with an average age of 20) and 21 teacher training professors (with an average age of 54 and average of two decades of experience), was conducted at a university in the northwestern USA in January and February 2024. The data reveal minimal use of generative AI in teacher training:

Among trainee teachers: 48 do not use the technology in their courses; 49 have never received training on how to use it; and only 3 reported having received any type of guidance.
Among teaching course professors: 18 do not use AI in their classes; 18 have not received training on how to do so; and only 9 allow students to use the tool, albeit with limitations, such as for brainstorming or drafting lesson plans.

The low level of adoption is reflected in self-assessments: on a scale of 0 to 10, trainee teachers rated their AI literacy at an average of 4.2, while teachers scored themselves at 3.9.

“The main takeaway is that our students and teachers are asking to learn more about AI, but we do not have the support to do it,” says Priya Panday-Shukla, an education specialist at Washington State University.

AI: a mixture of enthusiasm and distrust

Despite having limited hands-on experience, many trainee teachers see advantages in using AI, such as saving time, generating ideas, and learning new content. Educators also cited benefits, including greater technological awareness and assistance in creating teaching materials.

Both groups also expressed concerns, however.

Students worry that overuse could lead to superficial learning and dependence, while teachers highlight issues of academic integrity, plagiarism, and the learning curve required to master the technology.

Focus groups signaled a certain tension between optimism and resistance.

One future English teacher said she was interested in using AI to create starting points for learning but warned about the risks of irresponsible use. A trainee mathematics teacher said he was opposed to adopting it in the classroom, although he acknowledged that his students “could be left behind” if he ignored AI entirely.

Barriers and pathways to AI adoption

The study was structured around Everett Rogers’s Diffusion of Innovation theory, which evaluates attributes that facilitate or hinder the adoption of new technologies.

With regard to AI in education, the respondents recognized clear advantages but reported low compatibility with current practices and a lack of formal training, limiting the perceived benefits.

To overcome these barriers, Panday-Shukla developed a pilot workshop at the WSU Global Campus, inspired by a matrix created by the state education agency (OSPI).

The workshop proposes four levels of use, from total prohibition to mandatory use in certain activities, remaining transparent at all times about what is or is not permitted.

“When you need to verify information, you still do it the old way: one source at a time, one piece of information at a time. It is no different from that.” — Priya Panday-Shukla

Implications for the workplace

The AI debate is not confined to the classroom. The study cites McKinsey Global Institute projections that up to 30% of US working hours could be automated by 2030, forcing millions of workers to seek new career paths. In this context, resisting AI and failing to properly prepare could harm not only future teachers, but also the students they will go on to educate.

The key recommendation is to invest in digital literacy and AI for trainee teachers and their educators through training programs, clear institutional guidelines, and spaces for ethics debates.

For Panday-Shukla, preparing future educators to teach in a world already being shaped by artificial intelligence is a matter of educational justice.

Diffusion of innovations applied to AI in education

What is it?

The diffusion of innovations theory, formulated by Everett Rogers in 2003, uses five attributes to explain how new technologies and practices spread within a social group. When applied to AI in education, it helps explain why future teachers and their educators are still reluctant to incorporate the technology into the classroom, despite recognizing its potential.

The five attributes in the context of AI

Relative advantage

The adoption of an innovation relies on it being seen as better than existing tools or methods. Many trainee teachers believe AI can offer concrete benefits, such as saving time when creating content and helping brainstorm ideas. Some educators also recognize this potential, highlighting that AI can help lessons feel more personalized and assist in the production of teaching materials.

Compatibility

Compatibility refers to how well a technology aligns with current values and practices. In this context, it is a significant barrier: several teachers felt that AI conflicts with institutional norms. In many cases, using AI is treated as plagiarism or cheating, going against the principles of academic integrity. This perception makes it difficult to accept the technology as a legitimate element of teacher training.

Complexity

When an innovation is perceived as difficult to learn or use, adoption tends to be slower. Some trainee teachers described AI as intuitive and easy to use, but others said they found it difficult and mentioned a steep learning curve. Teacher educators, meanwhile, said they do not have enough time to master the tools and adapt them to the needs of their courses, which increases resistance.

Trialability

Another essential factor is the opportunity to experiment with a technology in a controlled environment before fully adopting it. Nearly all trainee teachers had already tried AI tools like ChatGPT or Gemini, but most used them for personal purposes, such as entertainment or informal study support. Few had risked using them for teaching tasks, largely because their courses did not offer a space or incentive to do so.

Observability

Finally, for an innovation to be seen as credible, the results need to be visible. The observability of AI is greatly reduced by a lack of formal training and practical classroom examples. Students and professors know the technology exists and are aware of its potential, but they rarely see real demonstrations of how it can improve teaching practice.

Why does it matter?

These five attributes help explain the dilemma highlighted by the study. Artificial intelligence is perceived as useful and promising, but is hindered by obstacles such as restrictive institutional guidelines, a steep learning curve, a lack of opportunities to try it out, and an absence of visible examples of its application. Without training and clear guidance, both future teachers and their educators remain caught between curiosity and caution, delaying the technology’s integration into teacher training.

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